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Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

Interactive molecular dynamics simulation in virtual reality (iMD-VR) is emerging as a promising technique in molecular science. Here, we demonstrate its use in a range of fifteen applications in materials science and heterogeneous catalysis. In this work, the iMD-VR package Narupa is used with the MD package, DL_POLY [1]. We show how iMD-VR can be used to: (i) investigate the mechanism of lithium fast ion conduction by directing the formation of defects showing that vacancy transport is favoured over interstitialcy mechanisms, and (ii) guide a molecule through a zeolite pore to explore diffusion within zeolites, examining in detail the motion of methyl n-hexanoate in H-ZSM-5 zeolite and identifying bottlenecks restricting diffusion. iMD-VR allows users to manipulate these systems intuitively, to drive changes in them and observe the resulting changes in structure and dynamics. We make these simulations available, as a resource for both teaching and research. All simulation files, with videos, can be found online (https://doi.org/10.5281/zenodo.8252314) and are provided as open-source material., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Joe Crossley-Lewis reports financial support was provided by BP Plc. Josh Dunn reports financial support was provided by BP Plc., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

We describe a two-step approach for combining interactive molecular dynamics in virtual reality (iMD-VR) with free energy (FE) calculation to explore the dynamics of biological processes at the molecular level. We refer to this combined approach as iMD-VR-FE. Stage one involves using a state-of-the-art 'human-in-the-loop' iMD-VR framework to generate a diverse range of protein-ligand unbinding pathways, benefitting from the sophistication of human spatial and chemical intuition. Stage two involves using the iMD-VR-sampled pathways as initial guesses for defining a path-based reaction coordinate from which we can obtain a corresponding free energy profile using FE methods. To investigate the performance of the method, we apply iMD-VR-FE to investigate the unbinding of a benzamidine ligand from a trypsin protein. The binding free energy calculated using iMD-VR-FE is similar for each pathway, indicating internal consistency. Moreover, the resulting free energy profiles can distinguish energetic differences between pathways corresponding to various protein-ligand conformations (e.g., helping to identify pathways that are more favourable) and enable identification of metastable states along the pathways. The two-step iMD-VR-FE approach offers an intuitive way for researchers to test hypotheses for candidate pathways in biomolecular systems, quickly obtaining both qualitative and quantitative insight., (© 2023. Springer Nature Limited.)

Introduction: The potential of virtual reality (VR) to contribute to drug design and development has been recognized for many years. A recent advance is to use VR not only to visualize and interact with molecules, but also to interact with molecular dynamics simulations 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful for flexible docking and examining binding processes and conformational changes., Areas Covered: The authors use the term 'interactive VR' to refer to software where interactivity is an inherent part of the user VR experience e.g . in making structural modifications or interacting with a physically rigorous molecular dynamics (MD) simulation, as opposed to using VR controllers to rotate and translate the molecule for enhanced visualization. Here, they describe these methods and their application to problems relevant to drug discovery, highlighting the possibilities that they offer in this arena., Expert Opinion: The ease of viewing and manipulating molecular structures and dynamics, using accessible VR hardware, and the ability to modify structures on the fly ( e.g . adding or deleting atoms) - and for groups of researchers to work together in the same virtual environment - makes modern interactive VR a valuable tool to add to the armory of drug design and development methods.

With a growing body of research highlighting the therapeutic potential of experiential phenomenology which diminishes egoic identity and increases one's sense of connectedness, there is significant interest in how to elicit such 'self-transcendent experiences' (STEs) in laboratory contexts. Psychedelic drugs (YDs) have proven particularly effective in this respect, producing subjective phenomenology which reliably elicits intense STEs. With virtual reality (VR) emerging as a powerful tool for constructing new perceptual environments, we describe a VR framework called 'Isness-distributed' (Isness-D) which harnesses the unique affordances of distributed multi-person VR to blur conventional self-other boundaries. Within Isness-D, groups of participants co-habit a shared virtual space, collectively experiencing their bodies as luminous energetic essences with diffuse spatial boundaries. It enables moments of 'energetic coalescence', a new class of embodied intersubjective experience where bodies can fluidly merge, enabling participants to include multiple others within their self-representation. To evaluate Isness-D, we adopted a citizen science approach, coordinating an international network of Isness-D 'nodes'. We analyzed the results (N = 58) using 4 different self-report scales previously applied to analyze subjective YD phenomenology (the inclusion of community in self scale, ego-dissolution inventory, communitas scale, and the MEQ30 mystical experience questionnaire). Despite the complexities associated with a distributed experiment like this, the Isness-D scores on all 4 scales were statistically indistinguishable from recently published YD studies, demonstrating that distributed VR can be used to design intersubjective STEs where people dissolve their sense of self in the connection to others., (© 2022. The Author(s).)

Characterizing the photochemical reactivity of transient volatile organic compounds (VOCs) in our atmosphere begins with a proper understanding of their abilities to absorb sunlight. Unfortunately, the photoabsorption cross-sections for a large number of transient VOCs remain unavailable experimentally due to their short lifetime or high reactivity. While structure-activity relationships (SARs) have been successfully employed to estimate the unknown photoabsorption cross-sections of VOCs, computational photochemistry offers another promising strategy to predict not only the vertical electronic transitions of a given molecule but also the width and shape of the bands forming its absorption spectrum. In this work, we focus on the use of the nuclear ensemble approach (NEA) to determine the photoabsorption cross-section of four exemplary VOCs, namely, acrolein, methylhydroperoxide, 2-hydroperoxy-propanal, and (microsolvated) pyruvic acid. More specifically, we analyze the influence that different strategies for sampling the ground-state nuclear density-Wigner sampling and ab initio molecular dynamics with a quantum thermostat-can have on the simulated absorption spectra. We highlight the potential shortcomings of using uncoupled harmonic modes within Wigner sampling of nuclear density to describe flexible or microsolvated VOCs and some limitations of SARs for multichromophoric VOCs. Our results suggest that the NEA could constitute a powerful tool for the atmospheric community to predict the photoabsorption cross-section for transient VOCs., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin., (© 2021 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.)

The emerging fields of citizen science and gamification reformulate scientific problems as games or puzzles to be solved. Through engaging the wider non-scientific community, significant breakthroughs may be made by analyzing citizen-gathered data. In parallel, recent advances in virtual reality (VR) technology are increasingly being used within a scientific context and the burgeoning field of interactive molecular dynamics in VR (iMD-VR) allows users to interact with dynamical chemistry simulations in real time. Here, we demonstrate the utility of iMD-VR as a medium for gamification of chemistry research tasks. An iMD-VR "game" was designed to encourage users to explore the reactivity of a particular chemical system, and a cohort of 18 participants was recruited to playtest this game as part of a user study. The reaction game encouraged users to experiment with making chemical reactions between a propyne molecule and an OH radical, and "molecular snapshots" from each game session were then compiled and used to map out reaction pathways. The reaction network generated by users was compared to existing literature networks demonstrating that users in VR capture almost all the important reaction pathways. Further comparisons between humans and an algorithmic method for guiding molecular dynamics show that through using citizen science to explore these kinds of chemical problems, new approaches and strategies start to emerge.

The main protease (M pro ) of SARS-CoV-2 is central to viral maturation and is a promising drug target, but little is known about structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of biomolecular simulation techniques, including automated docking, molecular dynamics (MD) and interactive MD in virtual reality, QM/MM, and linear-scaling DFT, to investigate the molecular features underlying recognition of the natural M pro substrates. We extensively analysed the subsite interactions of modelled 11-residue cleavage site peptides, crystallographic ligands, and docked COVID Moonshot-designed covalent inhibitors. Our modelling studies reveal remarkable consistency in the hydrogen bonding patterns of the natural M pro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular plasticity at the S2 site. Building on our initial M pro -substrate models, we used predictive saturation variation scanning (PreSaVS) to design peptides with improved affinity. Non-denaturing mass spectrometry and other biophysical analyses confirm these new and effective 'peptibitors' inhibit M pro competitively. Our combined results provide new insights and highlight opportunities for the development of M pro inhibitors as anti-COVID-19 drugs., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

In many scientific fields, there is an interest in understanding the way in which chemical networks evolve. The chemical networks which researchers focus upon have become increasingly complex, and this has motivated the development of automated methods for exploring chemical reactivity or conformational change in a "black-box" manner, harnessing modern computing resources to automate mechanism discovery. In this work, we present a new approach to automated mechanism generation which couples molecular dynamics and statistical rate theory to automatically find kinetically important reactions and then solve the time evolution of the species in the evolving network. The key to this chemical network mapping through combined dynamics and ME simulation approach is the concept of "kinetic convergence", whereby the search for new reactions is constrained to those species which are kinetically favorable at the conditions of interest. We demonstrate the capability of the new approach for two systems, a well-studied combustion system and a multiple oxygen addition system relevant to atmospheric aerosol formation.

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published 'in-house' efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties., Competing Interests: Authors SB, LD, PH, AH, SK, ZK, MK, YL, JPM, TTN, MP, GR, WR, LS, NT, and DW are affiliated with commercial companies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

We propose and test an extension of the energy-grained master equation (EGME) for treating nonadiabatic (NA) hopping between different potential energy surfaces, which enables us to model the competition between stepwise collisional relaxation and kinetic processes which transfer population between different electronic states of the same spin symmetry. By incorporating Zhu-Nakamura theory into the EGME, we are able to treat NA passages beyond the simple Landau-Zener approximation, along with the corresponding treatments of zero-point energy and tunneling probability. To evaluate the performance of this NA-EGME approach, we carried out detailed studies of the UV photodynamics of the volatile organic compound C 6 -hydroperoxy aldehyde (C 6 -HPALD) using on-the-fly ab initio molecular dynamics and trajectory surface hopping. For this multichromophore molecule, we show that the EGME is able to capture important aspects of the dynamics, including kinetic timescales, and diabatic trapping. Such an approach provides a promising and efficient strategy for treating the long-time dynamics of photoexcited molecules in regimes which are difficult to capture using atomistic on-the-fly molecular dynamics.

The main protease (Mpro) of the SARS-CoV-2 virus is one focus of drug development efforts for COVID-19. Here, we show that interactive molecular dynamics in virtual reality (iMD-VR) is a useful and effective tool for creating Mpro complexes. We make these tools and models freely available. iMD-VR provides an immersive environment in which users can interact with MD simulations and so build protein complexes in a physically rigorous and flexible way. Recently, we have demonstrated that iMD-VR is an effective method for interactive, flexible docking of small molecule drugs into their protein targets (Deeks et al. PLoS One 2020, 15, e0228461). Here, we apply this approach to both an Mpro inhibitor and an oligopeptide substrate, using experimentally determined crystal structures. For the oligopeptide, we test against a crystallographic structure of the original SARS Mpro. Docking with iMD-VR gives models in agreement with experimentally observed (crystal) structures. The docked structures are also tested in MD simulations and found to be stable. Different protocols for iMD-VR docking are explored, e.g., with and without restraints on protein backbone, and we provide recommendations for its use. We find that it is important for the user to focus on forming binding interactions, such as hydrogen bonds, and not to rely on using simple metrics (such as RMSD), in order to create realistic, stable complexes. We also test the use of apo (uncomplexed) crystal structures for docking and find that they can give good results. This is because of the flexibility and dynamic response allowed by the physically rigorous, atomically detailed simulation approach of iMD-VR. We make our models (and interactive simulations) freely available. The software framework that we use, Narupa, is open source, and uses commodity VR hardware, so these tools are readily accessible to the wider research community working on Mpro (and other COVID-19 targets). These should be widely useful in drug development, in education applications, e.g., on viral enzyme structure and function, and in scientific communication more generally.

The ability to understand and engineer molecular structures relies on having accurate descriptions of the energy as a function of atomic coordinates. Here, we outline a new paradigm for deriving energy functions of hyperdimensional molecular systems, which involves generating data for low-dimensional systems in virtual reality (VR) to then efficiently train atomic neural networks (ANNs). This generates high-quality data for specific areas of interest within the hyperdimensional space that characterizes a molecule's potential energy surface (PES). We demonstrate the utility of this approach by gathering data within VR to train ANNs on chemical reactions involving fewer than eight heavy atoms. This strategy enables us to predict the energies of much higher-dimensional systems, e.g., containing nearly 100 atoms. Training on datasets containing only 15k geometries, this approach generates mean absolute errors around 2 kcal mol -1 . This represents one of the first times that an ANN-PES for a large reactive radical has been generated using such a small dataset. Our results suggest that VR enables the intelligent curation of high-quality data, which accelerates the learning process.

Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5-10 minutes. Following a brief training phase, our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Å RMSD of the crystallographic binding pose. These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypotheses., Competing Interests: DRG acknowledges cloud computing resources were provided from Oracle Corporation via a Bristol University Partnership Cloud award. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products to declare.

The diffusion of small molecules through viscous matrices formed by large organic molecules is important across a range of domains, including pharmaceutical science, materials chemistry, and atmospheric science, impacting on, for example, the formation of amorphous and crystalline phases. Here we report significant breakdowns in the Stokes-Einstein (SE) equation from measurements of the diffusion of water (spanning 5 decades) and viscosity (spanning 12 decades) in saccharide aerosol droplets. Molecular dynamics simulations show water diffusion is not continuous, but proceeds by discrete hops between transient cavities that arise and dissipate as a result of dynamical fluctuations within the saccharide lattice. The ratio of transient cavity volume to solvent volume increases with size of molecules making up the lattice, increasing divergence from SE predictions. This improved mechanistic understanding of diffusion in viscous matrices explains, for example, why organic compounds equilibrate according to SE predictions and water equilibrates more rapidly in aerosols., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) machine learning system provides an efficient and accurate method for the prediction of NMR parameters from 3-dimensional molecular structures. Here we demonstrate that machine learning predictions of NMR parameters, trained on quantum chemical computed values, can be as accurate as, but computationally much more efficient (tens of milliseconds per molecular structure) than, quantum chemical calculations (hours/days per molecular structure) starting from the same 3-dimensional structure. Training the machine learning system on quantum chemical predictions, rather than experimental data, circumvents the need for the existence of large, structurally diverse, error-free experimental databases and makes IMPRESSION applicable to solving 3-dimensional problems such as molecular conformation and stereoisomerism., (This journal is © The Royal Society of Chemistry 2020.)

Most chemical transformations (reactions or conformational changes) that are of interest to researchers have many degrees of freedom, usually too many to visualize without reducing the dimensionality of the system to include only the most important atomic motions. In this article, we describe a method of using Principal Component Analysis (PCA) for analyzing a series of molecular geometries ( e.g. , a reaction pathway or molecular dynamics trajectory) and determining the reduced dimensional space that captures the most structural variance in the fewest dimensions. The software written to carry out this method is called PathReducer , which permits (1) visualizing the geometries in a reduced dimensional space, (2) determining the axes that make up the reduced dimensional space, and (3) projecting the series of geometries into the low-dimensional space for visualization. We investigated two options to represent molecular structures within PathReducer : aligned Cartesian coordinates and matrices of interatomic distances. We found that interatomic distance matrices better captured non-linear motions in a smaller number of dimensions. To demonstrate the utility of PathReducer , we have carried out a number of applications where we have projected molecular dynamics trajectories into a reduced dimensional space defined by an intrinsic reaction coordinate. The visualizations provided by this analysis show that dynamic paths can differ greatly from the minimum energy pathway on a potential energy surface. Viewing intrinsic reaction coordinates and trajectories in this way provides a quick way to gather qualitative information about the pathways trajectories take relative to a minimum energy path. Given that the outputs from PCA are linear combinations of the input molecular structure coordinates ( i.e. , Cartesian coordinates or interatomic distances), they can be easily transferred to other types of calculations that require the definition of a reduced dimensional space ( e.g. , biased molecular dynamics simulations)., (This journal is © The Royal Society of Chemistry 2019.)

As molecular scientists have made progress in their ability to engineer nanoscale molecular structure, we face new challenges in our ability to engineer molecular dynamics (MD) and flexibility. Dynamics at the molecular scale differs from the familiar mechanics of everyday objects because it involves a complicated, highly correlated, and three-dimensional many-body dynamical choreography which is often nonintuitive even for highly trained researchers. We recently described how interactive molecular dynamics in virtual reality (iMD-VR) can help to meet this challenge, enabling researchers to manipulate real-time MD simulations of flexible structures in 3D. In this article, we outline various efforts to extend immersive technologies to the molecular sciences, and we introduce "Narupa," a flexible, open-source, multiperson iMD-VR software framework which enables groups of researchers to simultaneously cohabit real-time simulation environments to interactively visualize and manipulate the dynamics of molecular structures with atomic-level precision. We outline several application domains where iMD-VR is facilitating research, communication, and creative approaches within the molecular sciences, including training machines to learn potential energy functions, biomolecular conformational sampling, protein-ligand binding, reaction discovery using "on-the-fly" quantum chemistry, and transport dynamics in materials. We touch on iMD-VR's various cognitive and perceptual affordances and outline how these provide research insight for molecular systems. By synergistically combining human spatial reasoning and design insight with computational automation, technologies such as iMD-VR have the potential to improve our ability to understand, engineer, and communicate microscopic dynamical behavior, offering the potential to usher in a new paradigm for engineering molecules and nano-architectures.

While the primary bottleneck to a number of computational workflows was not so long ago limited by processing power, the rise of machine learning technologies has resulted in an interesting paradigm shift, which places increasing value on issues related to data curation-that is, data size, quality, bias, format, and coverage. Increasingly, data-related issues are equally as important as the algorithmic methods used to process and learn from the data. Here we introduce an open-source graphics processing unit-accelerated neural network (NN) framework for learning reactive potential energy surfaces (PESs). To obtain training data for this NN framework, we investigate the use of real-time interactive ab initio molecular dynamics in virtual reality (iMD-VR) as a new data curation strategy that enables human users to rapidly sample geometries along reaction pathways. Focusing on hydrogen abstraction reactions of CN radical with isopentane, we compare the performance of NNs trained using iMD-VR data versus NNs trained using a more traditional method, namely, molecular dynamics (MD) constrained to sample a predefined grid of points along the hydrogen abstraction reaction coordinate. Both the NN trained using iMD-VR data and the NN trained using the constrained MD data reproduce important qualitative features of the reactive PESs, such as a low and early barrier to abstraction. Quantitative analysis shows that NN learning is sensitive to the data set used for training. Our results show that user-sampled structures obtained with the quantum chemical iMD-VR machinery enable excellent sampling in the vicinity of the minimum energy path (MEP). As a result, the NN trained on the iMD-VR data does very well predicting energies that are close to the MEP but less well predicting energies for "off-path" structures. The NN trained on the constrained MD data does better predicting high-energy off-path structures, given that it included a number of such structures in its training set.

Methodologies for creating reactive potential energy surfaces from molecular mechanics force-fields are becoming increasingly popular. To date, molecular mechanics force-fields in biochemistry and small molecule organic chemistry tend to use harmonic expressions to treat bonding stretches, which is a poor approximation in reactive and nonequilibirum molecular dynamics simulations since bonds are often displaced significantly from their equilibrium positions. For such applications there is need for a better treatment of anharmonicity. In this contribution, Morse bonding potentials have been extensively parametrized for the atom types in the MM3 force field of Allinger and co-workers using high level CCSD(T)(F12*) energies. To our knowledge this is among the first instances of a comprehensive parametrization of Morse potentials in a popular organic chemistry force field. In the context of molecular dynamics simulations, these data will: (1) facilitate the fitting of reactive potential energy surfaces using empirical valence bond approaches and (2) enable more accurate treatments of energy transfer.

The problem of observing rare events is pervasive among the molecular dynamics community and an array of different types of methods are commonly used to accelerate these long time scale processes. Typically, rare event acceleration methods require an a priori specification of the event to be accelerated. In recent work, we have demonstrated the application of boxed molecular dynamics to energy space, as a way to accelerate rare events in the stochastic chemical master equation. Here we build upon this work and apply the boxed molecular dynamics algorithm to the energy space of a molecule in classical trajectory simulations. Through this new BXD in energy (BXDE) approach we demonstrate that generic rare events (in this case chemical reactions) may be accelerated by multiple orders of magnitude compared to unbiased simulations. Furthermore, we show that the ratios of products formed from the BXDE simulations are similar to those formed in unbiased simulations at the same temperature.

We describe a framework for interactive molecular dynamics in a multiuser virtual reality (VR) environment, combining rigorous cloud-mounted atomistic physics simulations with commodity VR hardware, which we have made accessible to readers (see isci.itch.io/nsb-imd). It allows users to visualize and sample, with atomic-level precision, the structures and dynamics of complex molecular structures "on the fly" and to interact with other users in the same virtual environment. A series of controlled studies, in which participants were tasked with a range of molecular manipulation goals (threading methane through a nanotube, changing helical screw sense, and tying a protein knot), quantitatively demonstrate that users within the interactive VR environment can complete sophisticated molecular modeling tasks more quickly than they can using conventional interfaces, especially for molecular pathways and structural transitions whose conformational choreographies are intrinsically three-dimensional. This framework should accelerate progress in nanoscale molecular engineering areas including conformational mapping, drug development, synthetic biology, and catalyst design. More broadly, our findings highlight the potential of VR in scientific domains where three-dimensional dynamics matter, spanning research and education.

The chemical master equation is a powerful theoretical tool for analyzing the kinetics of complex multiwell potential energy surfaces in a wide range of different domains of chemical kinetics spanning combustion, atmospheric chemistry, gas-surface chemistry, solution phase chemistry, and biochemistry. There are two well-established methodologies for solving the chemical master equation: a stochastic "kinetic Monte Carlo" approach and a matrix-based approach. In principle, the results yielded by both approaches are identical; the decision of which approach is better suited to a particular study depends on the details of the specific system under investigation. In this Article, we present a rigorous method for accelerating stochastic approaches by several orders of magnitude, along with a method for unbiasing the accelerated results to recover the "true" value. The approach we take in this paper is inspired by the so-called "boxed molecular dynamics" (BXD) method, which has previously only been applied to accelerate rare events in molecular dynamics simulations. Here we extend BXD to design a simple algorithmic strategy for accelerating rare events in stochastic kinetic simulations. Tests on a number of systems show that the results obtained using the BXD rare event strategy are in good agreement with unbiased results. To carry out these tests, we have implemented a kinetic Monte Carlo approach in MESMER, which is a cross-platform, open-source, and freely available master equation solver.

The action of fluoroacetate as a broad-spectrum mammalian pesticide depends on the 'lethal synthesis' of fluorocitrate by citrate synthase, through a subtle enantioselective enolization of fluoroacetyl-coenzyme A. In this work, we demonstrate how a projection-based embedding method can be applied to calculate coupled cluster (CCSD(T)) reaction profiles from quantum mechanics/molecular mechanics optimized pathways for this enzyme reaction. Comparison of pro- R and pro- S proton abstraction in citrate synthase at the CCSD(T)-in-DFT//MM level yields the correct enantioselectivity. We thus demonstrate the potential of projection-based embedding for determining stereoselectivity in enzymatic systems. We further show that the method is simple to apply, eliminates variability due to the choice of density functional theory functional and allows the efficient calculation of CCSD(T) quality enzyme reaction barriers., Competing Interests: The authors declare no competing interests.

The physicochemical changes experienced by organic aerosol particles undergoing dehydration into the surrounding gas phase can be drastic, forcing rapid vitrification of the particle and suppressing internal diffusion. Until recently, experimental studies have concentrated on quantifying diffusional mixing of either water or non-volatile components, while relatively little attention has been paid to the role of semivolatile organic component (SVOC) diffusion and volatilisation in maintaining the equilibrium between the gas and particle phases. Here we present methods to simultaneously investigate diffusivities and volatilities in studies of evolving single ternary aerosol particle size and composition. Analysing particles of ternary composition must account for the multiple chemical species that volatilise in response to a step change in gas phase water activity. In addition, treatments of diffusion in multicomponent mixtures are necessary to represent evolving heterogeneities in particle composition. We find that the contributions to observed size behaviour from volatilisation of water and a SVOC can be decoupled and treated separately. Employing Fickian diffusion modelling, we extract the compositional dependence of the diffusion constant of water and compare the results to recently published parametrisations in binary aerosol particles. The treatment of ideality and activity in each case is discussed, with reference to use in multicomponent core shell models. Meanwhile, the evaporation of an SVOC into an unsaturated gas flow may be treated by Maxwell's equation, with slow diffusional transport manifesting as a suppression in the extracted vapour pressure.

The calculation of accurate excitation energies using ab initio electronic structure methods such as standard equation of motion coupled cluster singles and doubles (EOM-CCSD) has been cost prohibitive for large systems. In this work, we use a simple projector-based embedding scheme to calculate the EOM-CCSD excitation energies of acrolein solvated in water molecules modeled using density functional theory (DFT). We demonstrate the accuracy of this approach gives excitation energies within 0.01 eV of full EOM-CCSD, but with significantly reduced computational cost. This approach is also shown to be relatively invariant to the choice of functional used in the environment and allows for the description of systems with large numbers of basis functions (>1000) to be treated using state-of-the-art wave function methods. The flexibility of embedding to select orbitals to add to the excited-state method provides insights into the origins of the excitations and can reduce artifacts that could arise in traditional linear response time-dependent DFT (LR-TDDFT).

We recently outlined an efficient multi-tiered parallel ab initio excitonic framework that utilizes time dependent density functional theory (TDDFT) to calculate ground and excited state energies and gradients of large supramolecular complexes in atomistic detail - enabling us to undertake non-adiabatic simulations which explicitly account for the coupled anharmonic vibrational motion of all the constituent atoms in a supramolecular system. Here we apply that framework to the 27 coupled bacterio-chlorophyll-a chromophores which make up the LH2 complex, using it to compute an on-the-fly nonadiabatic surface-hopping (SH) trajectory of electronically excited LH2. Part one of this article is focussed on calibrating our ab initio exciton Hamiltonian using two key parameters: a shift δ, which corrects for the error in TDDFT vertical excitation energies; and an effective dielectric constant ε, which describes the average screening of the transition-dipole coupling between chromophores. Using snapshots obtained from equilibrium molecular dynamics simulations (MD) of LH2, we tune the values of both δ and ε through fitting to the thermally broadened experimental absorption spectrum, giving a linear absorption spectrum that agrees reasonably well with experiment. In part two of this article, we construct a time-resolved picture of the coupled vibrational and excitation energy transfer (EET) dynamics in the sub-picosecond regime following photo-excitation. Assuming Franck-Condon excitation of a narrow eigenstate band centred at 800 nm, we use surface hopping to follow a single nonadiabatic dynamics trajectory within the full eigenstate manifold. Consistent with experimental data, this trajectory gives timescales for B800→B850 population transfer (τ B800→B850 ) between 650-1050 fs, and B800 population decay (τ 800→ ) between 10-50 fs. The dynamical picture that emerges is one of rapidly fluctuating LH2 eigenstates that are delocalized over multiple chromophores and undergo frequent crossing on a femtosecond timescale as a result of the atomic vibrations of the constituent chromophores. The eigenstate fluctuations arise from disorder that is driven by vibrational dynamics with multiple characteristic timescales. The scalability of our ab initio excitonic computational framework across massively parallel architectures opens up the possibility of addressing a wide range of questions, including how specific dynamical motions impact both the pathways and efficiency of electronic energy-transfer within large supramolecular systems.

The extent to which vibrational energy transfer dynamics can impact reaction outcomes beyond the gas phase remains an active research question. Molecular dynamics (MD) simulations are the method of choice for investigating such questions; however, they can be extremely expensive, and therefore it is worth developing cheaper models that are capable of furnishing reasonable results. This paper has two primary aims. First, we investigate the competition between energy relaxation and reaction at 'hotspots' that form on the surface of diamond during the chemical vapour deposition process. To explore this, we developed an efficient reactive potential energy surface by fitting an empirical valence bond model to higher-level ab initio electronic structure theory. We then ran 160 000 NVE trajectories on a large slab of diamond, and the results are in reasonable agreement with experiment: they suggest that energy dissipation from surface hotspots is complete within a few hundred femtoseconds, but that a small fraction of CH 3 does in fact undergo dissociation prior to the onset of thermal equilibrium. Second, we developed and tested a general procedure to formulate and solve the energy-grained master equation (EGME) for surface chemistry problems. The procedure we outline splits the diamond slab into system and bath components, and then evaluates microcanonical transition-state theory rate coefficients in the configuration space of the system atoms. Energy transfer from the system to the bath is estimated using linear response theory from a single long MD trajectory, and used to parametrize an energy transfer function which can be input into the EGME. Despite the number of approximations involved, the surface EGME results are in reasonable agreement with the NVE MD simulations, but considerably cheaper. The results are encouraging, because they offer a computationally tractable strategy for investigating non-equilibrium reaction dynamics at surfaces for a broader range of systems.This article is part of the themed issue 'Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces'., (© 2017 The Authors.)

The past decade has seen the development of a new class of rare event methods in which molecular configuration space is divided into a set of boundaries/interfaces, and then short trajectories are run between boundaries. For all these methods, an important concern is how to generate boundaries. In this paper, we outline an algorithm for adaptively generating boundaries along a free energy surface in multi-dimensional collective variable (CV) space, building on the boxed molecular dynamics (BXD) rare event algorithm. BXD is a simple technique for accelerating the simulation of rare events and free energy sampling which has proven useful for calculating kinetics and free energy profiles in reactive and non-reactive molecular dynamics (MD) simulations across a range of systems, in both NVT and NVE ensembles. Two key developments outlined in this paper make it possible to automate BXD, and to adaptively map free energy and kinetics in complex systems. First, we have generalized BXD to multidimensional CV space. Using strategies from rigid-body dynamics, we have derived a simple and general velocity-reflection procedure that conserves energy for arbitrary collective variable definitions in multiple dimensions, and show that it is straightforward to apply BXD to sampling in multidimensional CV space so long as the Cartesian gradients ∇CV are available. Second, we have modified BXD to undertake on-the-fly statistical analysis during a trajectory, harnessing the information content latent in the dynamics to automatically determine boundary locations. Such automation not only makes BXD considerably easier to use; it also guarantees optimal boundaries, speeding up convergence. We have tested the multidimensional adaptive BXD procedure by calculating the potential of mean force for a chemical reaction recently investigated using both experimental and computational approaches - i.e., F + CD 3 CN → DF + D 2 CN in both the gas phase and a strongly coupled explicit CD 3 CN solvent. The results obtained using multidimensional adaptive BXD agree well with previously published experimental and computational results, providing good evidence for its reliability.

We describe a parallelized linear-scaling computational framework developed to implement arbitrarily large multi-state empirical valence bond (MS-EVB) calculations within CHARMM and TINKER. Forces are obtained using the Hellmann-Feynman relationship, giving continuous gradients, and good energy conservation. Utilizing multi-dimensional Gaussian coupling elements fit to explicitly correlated coupled cluster theory, we built a 64-state MS-EVB model designed to study the F + CD3CN → DF + CD2CN reaction in CD3CN solvent (recently reported in Dunning et al. [Science 347(6221), 530 (2015)]). This approach allows us to build a reactive potential energy surface whose balanced accuracy and efficiency considerably surpass what we could achieve otherwise. We ran molecular dynamics simulations to examine a range of observables which follow in the wake of the reactive event: energy deposition in the nascent reaction products, vibrational relaxation rates of excited DF in CD3CN solvent, equilibrium power spectra of DF in CD3CN, and time dependent spectral shifts associated with relaxation of the nascent DF. Many of our results are in good agreement with time-resolved experimental observations, providing evidence for the accuracy of our MS-EVB framework in treating both the solute and solute/solvent interactions. The simulations provide additional insight into the dynamics at sub-picosecond time scales that are difficult to resolve experimentally. In particular, the simulations show that (immediately following deuterium abstraction) the nascent DF finds itself in a non-equilibrium regime in two different respects: (1) it is highly vibrationally excited, with ∼23 kcal mol(-1) localized in the stretch and (2) its post-reaction solvation environment, in which it is not yet hydrogen-bonded to CD3CN solvent molecules, is intermediate between the non-interacting gas-phase limit and the solution-phase equilibrium limit. Vibrational relaxation of the nascent DF results in a spectral blue shift, while relaxation of the post-reaction solvation environment results in a red shift. These two competing effects mean that the post-reaction relaxation profile is distinct from what is observed when Franck-Condon vibrational excitation of DF occurs within a microsolvation environment initially at equilibrium. Our conclusions, along with the theoretical and parallel software framework presented in this paper, should be more broadly applicable to a range of complex reactive systems.

Classical molecular dynamics simulations are reported for the deazetisation and ring opening of meso-2,3-difluoro-2,3-dimethyldiazocyclopropane in three solvents: CHCl3, CHFClBr and CH3CH(OH)CF3 (TFIPA). The achiral reactant leads to enantiomeric allene products, and the question addressed in the study is whether either of the chiral, enantiomerically pure solvents can induce significant enantiomeric excess in the products. The direct dynamics calculations use an empirical valence bond potential for the solute, with empirical parameters optimised against M06-2X/cc-pVTZ density functional results. The results reveal that the exothermic N2 loss and ring opening promote transient strong solvent-solute interactions within the first ∼100 fs of the reaction. Because of the bifurcating reaction path, these interactions occur at time when the "decision" about which enantiomer of the product to form has yet to be made (at least for many of the trajectories). Hence, it is possible in principle that the solvent could exert a larger-than-normal influence on the course of the reaction. In fact, the results reveal no such effect for CHFClBr but do predict that TFIPA should induce 15.2 ± 2.1% enantiomeric excess. This is roughly an order of magnitude larger than solvent-induced enantiomeric excesses found experimentally in reactions where the conversion of reactant(s) to enantiomeric products occur over separate transition states.

Solvent-solute interactions influence the mechanisms of chemical reactions in solution, but the response of the solvent is often slower than the reactive event. Here, we report that exothermic reactions of fluorine (F) atoms in d3-acetonitrile and d2-dichloromethane involve efficient energy flow to vibrational motion of the deuterium fluoride (DF) product that competes with dissipation of the energy to the solvent bath, despite strong solvent coupling. Transient infrared absorption spectroscopy and molecular dynamics simulations show that after DF forms its first hydrogen bond on a subpicosecond time scale, DF vibrational relaxation and further solvent restructuring occur over more than 10 picoseconds. Characteristic dynamics of gas-phase F-atom reactions with hydrogen-containing molecules persist in polar organic solvents, and the spectral evolution of the DF products serves as a probe of solvent reorganization induced by a chemical reaction., (Copyright © 2015, American Association for the Advancement of Science.)

Conspectus Although advances in computer hardware and algorithms tuned for novel computer architectures are leading to significant increases in the size and time scale for molecular simulations, it remains true that new methods and algorithms will be needed to address some of the problems in complex chemical systems, such as electrochemistry, excitation energy transport, proton transport, and condensed phase reactivity. Ideally, these new methods would exploit the strengths of emerging architectures. Fragment based approaches for electronic structure theory decompose the problem of solving the electronic Schrodinger equation into a series of much smaller problems. Because each of these smaller problems is largely independent, this strategy is particularly well-suited to parallel architectures. It appears that the most significant advances in computer architectures will be toward increased parallelism, and therefore fragment-based approaches are an ideal match to these trends. When the computational effort involved scales with the third (or higher) power of the molecular size, there is a large benefit to fragment-based approaches even on serial architectures. This is the case for many of the well-known methods for solving the electronic structure theory problem, especially when wave function-based approaches including electron correlation are considered. A major issue in fragment-based approaches is determining or improving their accuracy. Since the Achilles' heel of any such method lies in the approximations used to stitch the smaller problems back together (i.e., in the treatment of the cross-fragment interactions), it can often be important to ensure that the size of the smaller problems is "large enough." Thus, there are two frontiers that need to be extended in order to enable molecular simulations for large systems and long times: the strongly coupled problem of medium sized molecules (100-500 atoms) and the more weakly coupled problem of decomposing ("fragmenting") a molecular system and then stitching it back together. In this Account, we address both of these problems, the first by using graphical processing units (GPUs) and electronic structure algorithms tuned for these architectures and the second by using an exciton model as a framework in which to stitch together the solutions of the smaller problems. The multitiered parallel framework outlined here is aimed at nonadiabatic dynamics simulations on large supramolecular multichromophoric complexes in full atomistic detail. In this framework, the lowest tier of parallelism involves GPU-accelerated electronic structure theory calculations, for which we summarize recent progress in parallelizing the computation and use of electron repulsion integrals (ERIs), which are the major computational bottleneck in both density functional theory (DFT) and time-dependent density functional theory (TDDFT). The topmost tier of parallelism relies on a distributed memory framework, in which we build an exciton model that couples chromophoric units. Combining these multiple levels of parallelism allows access to ground and excited state dynamics for large multichromophoric assemblies. The parallel excitonic framework is in good agreement with much more computationally demanding TDDFT calculations of the full assembly.

In this paper, we briefly review the boxed molecular dynamics (BXD) method which allows analysis of thermodynamics and kinetics in complicated molecular systems. BXD is a multiscale technique, in which thermodynamics and long-time dynamics are recovered from a set of short-time simulations. In this paper, we review previous applications of BXD to peptide cyclization, solution phase organic reaction dynamics and desorption of ions from self-assembled monolayers (SAMs). We also report preliminary results of simulations of diamond etching mechanisms and protein unfolding in atomic force microscopy experiments. The latter demonstrate a correlation between the protein's structural motifs and its potential of mean force. Simulations of these processes by standard molecular dynamics (MD) is typically not possible, because the experimental time scales are very long. However, BXD yields well-converged and physically meaningful results. Compared with other methods of accelerated MD, our BXD approach is very simple; it is easy to implement, and it provides an integrated approach for simultaneously obtaining both thermodynamics and kinetics. It also provides a strategy for obtaining statistically meaningful dynamical results in regions of configuration space that standard MD approaches would visit only very rarely.

Here we provide an introduction and overview of current progress in the field of molecular simulation and visualization, touching on the following topics: (1) virtual and augmented reality for immersive molecular simulations; (2) advanced visualization and visual analytic techniques; (3) new developments in high performance computing; and (4) applications and model building.

With advances in computational power, the rapidly growing role of computational/simulation methodologies in the physical sciences, and the development of new human-computer interaction technologies, the field of interactive molecular dynamics seems destined to expand. In this paper, we describe and benchmark the software algorithms and hardware setup for carrying out interactive molecular dynamics utilizing an array of consumer depth sensors. The system works by interpreting the human form as an energy landscape, and superimposing this landscape on a molecular dynamics simulation to chaperone the motion of the simulated atoms, affecting both graphics and sonified simulation data. GPU acceleration has been key to achieving our target of 60 frames per second (FPS), giving an extremely fluid interactive experience. GPU acceleration has also allowed us to scale the system for use in immersive 360° spaces with an array of up to ten depth sensors, allowing several users to simultaneously chaperone the dynamics. The flexibility of our platform for carrying out molecular dynamics simulations has been considerably enhanced by wrappers that facilitate fast communication with a portable selection of GPU-accelerated molecular force evaluation routines. In this paper, we describe a 360° atmospheric molecular dynamics simulation we have run in a chemistry/physics education context. We also describe initial tests in which users have been able to chaperone the dynamics of 10-alanine peptide embedded in an explicit water solvent. Using this system, both expert and novice users have been able to accelerate peptide rare event dynamics by 3-4 orders of magnitude.

Protein dynamics have controversially been proposed to be at the heart of enzyme catalysis, but identification and analysis of dynamical effects in enzyme-catalyzed reactions have proved very challenging. Here, we tackle this question by comparing an enzyme with its heavy ((15)N, (13)C, (2)H substituted) counterpart, providing a subtle probe of dynamics. The crucial hydride transfer step of the reaction (the chemical step) occurs more slowly in the heavy enzyme. A combination of experimental results, quantum mechanics/molecular mechanics simulations, and theoretical analyses identify the origins of the observed differences in reactivity. The generally slightly slower reaction in the heavy enzyme reflects differences in environmental coupling to the hydride transfer step. Importantly, the barrier and contribution of quantum tunneling are not affected, indicating no significant role for "promoting motions" in driving tunneling or modulating the barrier. The chemical step is slower in the heavy enzyme because protein motions coupled to the reaction coordinate are slower. The fact that the heavy enzyme is only slightly less active than its light counterpart shows that protein dynamics have a small, but measurable, effect on the chemical reaction rate.

Molecular dynamics (MD) methods are increasingly widespread, but simulation of rare events in complex molecular systems remains a challenge. We recently introduced the boxed molecular dynamics (BXD) method, which accelerates rare events, and simultaneously provides both kinetic and thermodynamic information. We illustrate how the BXD method may be used to obtain high-resolution kinetic data from explicit MD simulations, spanning picoseconds to microseconds. The method is applied to investigate the loop formation dynamics and kinetics of cyclisation for a range of polypeptides, and recovers a power law dependence of the instantaneous rate coefficient over six orders of magnitude in time, in good agreement with experimental observations. Analysis of our BXD results shows that this power law behaviour arises when there is a broad and nearly uniform spectrum of reaction rate coefficients. For the systems investigated in this work, where the free energy surfaces have relatively small barriers, the kinetics is very sensitive to the initial conditions: strongly non-equilibrium conditions give rise to power law kinetics, while equilibrium initial conditions result in a rate coefficient with only a weak dependence on time. These results suggest that BXD may offer us a powerful and general algorithm for describing kinetics and thermodynamics in chemical and biochemical systems.

The most commonly used theoretical models for describing chemical kinetics are accurate in two limits. When relaxation is fast with respect to reaction time scales, thermal transition state theory (TST) is the theoretical tool of choice. In the limit of slow relaxation, an energy resolved description like RRKM theory is more appropriate. For intermediate relaxation regimes, where much of the chemistry in nature occurs, theoretical approaches are somewhat less well established. However, in recent years master equation approaches have been successfully used to analyze and predict nonequilibrium chemical kinetics across a range of intermediate relaxation regimes spanning atmospheric, combustion, and (very recently) solution phase organic chemistry. In this article, we describe a Master Equation Solver for Multi-Energy Well Reactions (MESMER), a user-friendly, object-oriented, open-source code designed to facilitate kinetic simulations over multi-well molecular energy topologies where energy transfer with an external bath impacts phenomenological kinetics. MESMER offers users a range of user options specified via keywords and also includes some unique statistical mechanics approaches like contracted basis set methods and nonadiabatic RRKM theory for modeling spin-hopping. It is our hope that the design principles implemented in MESMER will facilitate its development and usage by workers across a range of fields concerned with chemical kinetics. As accurate thermodynamics data become more widely available, electronic structure theory is increasingly reliable, and as our fundamental understanding of energy transfer improves, we envision that tools like MESMER will eventually enable routine and reliable prediction of nonequilibrium kinetics in arbitrary systems.

Bimolecular reactions in Earth's atmosphere are generally assumed to proceed between reactants whose internal quantum states are fully thermally relaxed. Here, we highlight a dramatic role for vibrationally excited bimolecular reactants in the oxidation of acetylene. The reaction proceeds by preliminary adduct formation between the alkyne and OH radical, with subsequent O(2) addition. Using a detailed theoretical model, we show that the product-branching ratio is determined by the excited vibrational quantum-state distribution of the adduct at the moment it reacts with O(2). Experimentally, we found that under the simulated atmospheric conditions O(2) intercepts ~25% of the excited adducts before their vibrational quantum states have fully relaxed. Analogous interception of excited-state radicals by O(2) is likely common to a range of atmospheric reactions that proceed through peroxy complexes.

Transient, broadband infra-red absorption spectroscopy with picosecond time resolution has been used to study the dynamics of reactions of CN radicals with tetrahydrofuran (THF) and d(8)-THF in liquid solutions ranging from neat THF to 0.5 M THF in chlorinated solvents (CDCl(3) and CD(2)Cl(2)). HCN and DCN products were monitored via their v(1) (C≡N stretching) and v(3) (C-H(D) stretching) vibrational absorption bands. Transient spectral features indicate formation of vibrationally excited HCN and DCN, and the onsets of absorption via the fundamental bands of HCN and DCN show short (5-15 ps) delays consistent with vibrational relaxation within the nascent reaction products. This interpretation is confirmed by non-equilibrium molecular dynamics simulations employing a newly derived analytic potential energy surface for the reaction in explicit THF solvent. The rate coefficient for reactive formation of HCN (as determined from measurements on both the 1(1)(0) and 3(1)(0) fundamental bands) decreases with increasing dilution of the THF in CDCl(3) or CD(2)Cl(2), showing pseudo-first order kinetic behaviour for THF concentrations in the range 0.5-4.5 M, and a bimolecular rate coefficient of (1.57 ± 0.12) × 10(10) M(-1) s(-1) is derived. Simultaneous analysis of time-dependent HCN 1(1)(0) and 3(1)(0) band intensities following reaction of CN with THF (3.0 M) in CD(2)Cl(2) suggests that C-H stretching mode excitation is favoured, and this deduction is supported by the computer simulations. The results extend our recent demonstration of nascent vibrational excitation of the products of bimolecular reactions in liquid solution to a different, and more strongly interacting class of organic solvents. They serve to reinforce the finding that dynamics (and thus the topology of the reactive potential energy surface) play an important role in determining the nascent product state distributions in condensed phase reactions.

The first excited electronic state of molecular oxygen, O(2)(a(1)Δ(g)), is formed in the upper atmosphere by the photolysis of O(3). Its lifetime is over 70 min above 75 km, so that during the day its concentration is about 30 times greater than that of O(3). In order to explore its potential reactivity with atmospheric constituents produced by meteoric ablation, the reactions of Mg, Fe, and Ca with O(2)(a) were studied in a fast flow tube, where the metal atoms were produced either by thermal evaporation (Ca and Mg) or by pulsed laser ablation of a metal target (Fe), and detected by laser induced fluorescence spectroscopy. O(2)(a) was produced by bubbling a flow of Cl(2) through chilled alkaline H(2)O(2), and its absolute concentration determined from its optical emission at 1270 nm (O(2)(a(1)Δ(g) - X(3)Σ(g) (-)). The following results were obtained at 296 K: k(Mg + O(2)(a) + N(2) → MgO(2) + N(2)) = (1.8 ± 0.2) × 10(-30) cm(6) molecule(-2) s(-1); k(Fe + O(2)(a) → FeO + O) = (1.1 ± 0.1) × 10(-13) cm(3) molecule(-1) s(-1); k(Ca + O(2)(a) + N(2) → CaO(2) + N(2)) = (2.9 ± 0.2) × 10(-28) cm(6) molecule(-2) s(-1); and k(Ca + O(2)(a) → CaO + O) = (2.7 ± 1.0) × 10(-12) cm(3) molecule(-1) s(-1). The total uncertainty in these rate coefficients, which mostly arises from the systematic uncertainty in the O(2)(a) concentration, is estimated to be ±40%. Mg + O(2)(a) occurs exclusively by association on the singlet surface, producing MgO(2)((1)A(1)), with a pressure dependent rate coefficient. Fe + O(2)(a), on the other hand, shows pressure independent kinetics. FeO + O is produced with a probability of only ∼0.1%. There is no evidence for an association complex, suggesting that this reaction proceeds mostly by near-resonant electronic energy transfer to Fe(a(5)F) + O(2)(X). The reaction of Ca + O(2)(a) occurs in an intermediate regime with two competing pressure dependent channels: (1) a recombination to produce CaO(2)((1)A(1)), and (2) a singlet∕triplet non-adiabatic hopping channel leading to CaO + O((3)P). In order to interpret the Ca + O(2)(a) results, we utilized density functional theory along with multireference and explicitly correlated CCSD(T)-F12 electronic structure calculations to examine the lowest lying singlet and triplet surfaces. In addition to mapping stationary points, we used a genetic algorithm to locate minimum energy crossing points between the two surfaces. Simulations of the Ca + O(2)(a) kinetics were then carried out using a combination of both standard and non-adiabatic Rice-Ramsperger-Kassel-Marcus (RRKM) theory implemented within a weak collision, multiwell master equation model. In terms of atmospheric significance, only in the case of Ca does reaction with O(2)(a) compete with O(3) during the daytime between 85 and 110 km.