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Water and ammonia are major constituents of icy planet interiors, however their phase behaviour under extreme conditions remain relatively unknown. Here, the authors show that ammonia monohydrate transforms under pressure into an alloy composed of molecules as well as ions, owing to a topological frustration.

The computational exploration of reactive processes is challenging due to the requirement of thorough sampling across the free energy landscape using accurate ab initio methods. To address these constraints, machine learning potentials are employed, yet their training for this kind of problem is still a laborious and tedious task. In this study, we present an efficient approach to train these potentials by cleverly using a single batch of unbiased trajectories that avoid the pitfalls of trajectories artificially biased along a suboptimal collective variable. This strategy, when integrated with current enhanced sampling techniques, allows to obtain free energy profiles and kinetic rates of ab initio quality, yet dramatically reducing the computational cost.

Identifying optimal collective variables to model transformations using atomic-scale simulations is a long-standing challenge. We propose a new method for the generation, optimization, and comparison of collective variables that can be thought of as a data-driven generalization of the path collective variable concept. It consists of a kernel ridge regression of the committor probability, which encodes a transformation's progress. The resulting collective variable is one-dimensional, interpretable, and differentiable, making it appropriate for enhanced sampling simulations requiring biasing. We demonstrate the validity of the method on two different applications: a precipitation model and the association of Li + and F - in water. For the former, we show that global descriptors such as the permutation invariant vector allow reaching an accuracy far from the one achieved via simpler, more intuitive variables. For the latter, we show that information correlated with the transformation mechanism is contained in the first solvation shell only and that inertial effects prevent the derivation of optimal collective variables from the atomic positions only.

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic rates can in principle be obtained by different techniques after projecting the high-dimensional atomic dynamics on one (or a few) collective variable. Even though it is well-known that the projected dynamics approximately follows - in a statistical sense - the generalized, underdamped or overdamped Langevin equations (depending on the time resolution), to date it is nontrivial to parameterize such equations starting from a limited, practically accessible amount of non-ergodic trajectories. In this work we focus on Markovian, underdamped Langevin equations, that arise naturally when considering, e.g., numerous water-solution processes at sub-picosecond resolution. After contrasting the advantages and pitfalls of different numerical approaches, we present an efficient parametrization strategy based on a limited set of molecular dynamics data, including equilibrium trajectories confined to minima and few hundreds transition path sampling-like trajectories. Employing velocity autocorrelation or memory kernel information for learning the friction and likelihood maximization for learning the free-energy landscape, we demonstrate the possibility to reconstruct accurate barriers and rates both for a benchmark system and for the interaction of carbon nanoparticles in water., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

Finding optimal reaction coordinates and predicting accurate kinetic rates for activated processes are two of the foremost challenges of molecular simulations. We introduce an algorithm that tackles the two problems at once: starting from a limited number of reactive molecular dynamics trajectories (transition paths), we automatically generate with a Monte Carlo approach a sequence of different reaction coordinates that progressively reduce the kinetic rate of their projected effective dynamics. Based on a variational principle, the minimal rate accurately approximates the exact one, and it corresponds to the optimal reaction coordinate. After benchmarking the method on an analytic double-well system, we apply it to complex atomistic systems: the interaction of carbon nanoparticles of different sizes in water.

Absorption in amine solutions is a well-established advanced technology for CO 2 capture. However, the fundamental aspects of the chemical reactions occurring in solution still appear to be unclear. Our previous investigation of aqueous monoethanolamine (MEA) and 2-amino-2-methyl-1,3-propanediol (AMPD), based on ab initio molecular dynamics simulations aided with metadynamics, provided new insights into the reaction mechanisms leading to CO 2 capture and release with carbamate formation and dissociation. In particular, the role of water-strongly underestimated in previous computational studies-was established as essential in determining the development of all relevant reactions. In this article, we apply the same simulation protocol to other relevant primary amines, namely, a sterically hindered amine (2-amino-2-methyl-1-propanol (AMP)) and an aromatic amine (benzylamine (BZA)). We also discuss the case of CO 2 capture with the formation of bicarbonate. New information is thus obtained that extends our understanding. However, quantitative predictions obtained using molecular simulations suffer from several methodological problems, and comparison among different chemical species is especially demanding. We clarify these problems further with a discussion of previous attempts to explain the different behaviors of AMP and MEA using other types of models and computations.

In the present letter, we investigate the double proton transfer (DPT) tautomerization process in guanine-cytosine (GC) DNA base pairs. In particular, we study the influence of the biological environment on the mechanism, the kinetics and thermodynamics of such DPT. To this end, we present a molecular dynamics (MD) study in the tight-binding density functional theory framework, and compare the reactivity of the isolated GC dimer with that of the same dimer embedded in a small DNA structure. The impact of nuclear quantum effects (NQEs) is also evaluated using Path Integral based MD. Results show that in the isolated dimer, the DPT occurs via a concerted mechanism, while in the model biological environment, it turns into a stepwise process going through an intermediate structure. One of the water molecules in the vicinity of the proton transfer sites plays an important role as it changes H-bond pattern during the DPT reaction. The inclusion of NQEs has the effect of speeding up the tautomeric-to-canonical reaction, reflecting the destabilization of both the tautomeric and intermediate forms.

The existence of a first-order phase transition between a low-density liquid (LDL) and a high-density liquid (HDL) form of supercooled water has been a central and highly debated issue of physics and chemistry for the last three decades. We present a computational study that allows us to determine the free-energy landscapes of supercooled water over a wide range of pressure and temperature conditions using the TIP4P/2005 force field. Our approach combines topology-based structural transformation coordinates, state-of-the-art free-energy calculation methods, and extensive unbiased molecular dynamics. All our diverse simulations cannot detect any barrier within the investigated timescales and system size, for a discontinuous transition between the LDL and HDL forms throughout the so-called "no man's land," until the onset of the solid, non-diffusive amorphous forms.

The nucleation of crystals is a prominent phenomenon in science and technology that still lacks a full atomic-scale understanding. Much work has been devoted to identifying order parameters able to track the process, from the inception of early nuclei to their maturing to critical size until growth of an extended crystal. We critically assess and compare two powerful distance-based collective variables, an effective entropy derived from liquid state theory and the path variable based on permutation invariant vectors using the Kob-Andersen binary mixture and a combination of enhanced-sampling techniques. Our findings reveal a comparable ability to drive nucleation when a bias potential is applied, and comparable free-energy barriers and structural features. Yet, we also found an imperfect correlation with the committor probability on the barrier top which was bypassed by changing the order parameter definition.

Reaction coordinates are an essential ingredient of theoretical studies of rare events in chemistry and physics because they carry information about reaction mechanism and allow the computation of free-energy landscapes and kinetic rates. We present a critical assessment of the merits and disadvantages of heuristic reaction coordinates, largely employed today, with respect to coordinates optimized on the basis of reliable transition-path sampling data. We take as a test bed multinanosecond ab initio molecular dynamics simulations of chloride S N 2 substitution on methyl chloride in explicit water. The computational protocol we devise allows the unsupervised optimization of agnostic coordinates able to account for solute and solvent contributions, yielding a free-energy reconstruction of quality comparable to the best heuristic coordinates without requiring chemical intuition.

The study of the thermodynamics, kinetics, and microscopic mechanisms of chemical reactions in solution requires the use of advanced free-energy methods for predictions to be quantitative. This task is however a formidable one for atomistic simulation methods, as the cost of quantum-based ab initio approaches, to obtain statistically meaningful samplings of the relevant chemical spaces and networks, becomes exceedingly heavy. In this work, we critically assess the optimal structure and minimal size of an ab initio training set able to lead to accurate free-energy profiles sampled with neural network potentials. The results allow one to propose an ab initio protocol where the ad hoc inclusion of a machine-learning (ML)-based task can significantly increase the computational efficiency, while keeping the ab initio accuracy and, at the same time, avoiding some of the notorious extrapolation risks in typical atomistic ML approaches. We focus on two representative, and computationally challenging, reaction steps of the classic Strecker-cyanohydrin mechanism for glycine synthesis in water solution, where the main precursors are formaldehyde and hydrogen cyanide. We demonstrate that indistinguishable ab initio quality results are obtained, thanks to the ML subprotocol, at about 1 order of magnitude less of computational load.

Simulations with adaptive time-dependent bias enable an efficient exploration of the conformational space of a system. However, the dynamic information is altered by the bias. Infrequent metadynamics recovers the transition rate of crossing a barrier, if the collective variables are ideal and there is no bias deposition near the transition state. Unfortunately, these conditions are not always fulfilled. To overcome these limitations, and inspired by single-molecule force spectroscopy, we use Kramers' theory for calculating the barrier-crossing rate when a time-dependent bias is added to the system. We assess the efficiency of collective variables parameter by measuring how efficiently the bias accelerates the transitions. We present approximate analytical expressions of the survival probability, reproducing the barrier-crossing time statistics and enabling the extraction of the unbiased transition rate even for challenging cases. We explore the limits of our method and provide convergence criteria to assess its validity.

We address the problem of constructing accurate mathematical models of the dynamics of complex systems projected on a collective variable. To this aim we introduce a conceptually simple yet effective algorithm for estimating the parameters of Langevin and Fokker-Planck equations from a set of short, possibly out-of-equilibrium molecular dynamics trajectories, obtained for instance from transition path sampling or as relaxation from high free-energy configurations. The approach maximizes the model likelihood based on any explicit expression of the short-time propagator, hence it can be applied to different evolution equations. We demonstrate the numerical efficiency and robustness of the algorithm on model systems, and we apply it to reconstruct the projected dynamics of pairs of C 60 and C 240 fullerene molecules in explicit water. Our methodology allows reconstructing the accurate thermodynamics and kinetics of activated processes, namely free energy landscapes, diffusion coefficients, and kinetic rates. Compared to existing enhanced sampling methods, we directly exploit short unbiased trajectories at a competitive computational cost.

SignificanceThe analysis of complex systems with many degrees of freedom generally involves the definition of low-dimensional collective variables more amenable to physical understanding. Their dynamics can be modeled by generalized Langevin equations, whose coefficients have to be estimated from simulations of the initial high-dimensional system. These equations feature a memory kernel describing the mutual influence of the low-dimensional variables and their environment. We introduce and implement an approach where the generalized Langevin equation is designed to maximize the statistical likelihood of the observed data. This provides an efficient way to generate reduced models to study dynamical properties of complex processes such as chemical reactions in solution, conformational changes in biomolecules, or phase transitions in condensed matter systems.

Atomic diffusion is at the basis of chemical ordering transformations in nanoalloys. Understanding the diffusion mechanisms at the atomic level is therefore a key issue in the study of the thermodynamic behavior of these systems and, in particular, of their evolution from out-of-equilibrium chemical ordering types often obtained in the experiments. Here, the diffusion is studied in the case of a single-atom impurity of Ag or Au moving within otherwise pure magic-size icosahedral clusters of Cu or Co by means of two different computational techniques, i.e., molecular dynamics and metadynamics. Our simulations reveal unexpected diffusion pathways, in which the displacement of the impurity is coupled with the creation of vacancies in the central part of the cluster. We show that the observed mechanism is quite different from the vacancy-mediated diffusion processes identified so far, and we demonstrate that it can be related to the presence of non-homogeneous compressive stress in the inner part of the icosahedral structure.

In both its gaseous and condensed forms, carbon dioxide has an ever-increasing impact on Earth's chemistry and human life and activities. However, many aspects of its high-pressure phase diagram remain unclear. In this work, we present a complete structural characterization of carbon dioxide fluids under geological conditions using extensive ab initio molecular dynamics simulations throughout a wide pressure and temperature range, corresponding to Earth's lower mantle. We identify and describe four different disordered regimes, including two polymeric forms and two molecular ones, all within the geothermal conditions of the lower mantle. At pressures below 40 GPa, we find that the molecular liquid becomes very reactive above 2000 K: the C-O double bond routinely breaks, resulting in small and transient chains composed of CO 2 units and frequently leading to an exchange of oxygen atoms between molecules. At higher pressures, in addition to the polymeric fluid previously reported at 3000 K, we find a polymeric system with glass-like behavior at lower temperatures, suggesting a complex interplay between kinetics and stability.

The need to chemically convert CO 2 at the interface of aqueous amine solutions has become particularly relevant for the development and the broad distribution of cost-effective and near-future devices for direct air capture working at low (e.g., ambient) partial pressure. Here, we have determined the stability of a CO 2 -monoethanolamine zwitterion and its chemical conversion into carbamate at the vapor/liquid water interface by first-principles molecular dynamics simulations coupled with a recently introduced enhanced sampling technique. Contrary to the bulk water case, our results show that both the zwitterion and carbamate ions are poorly stable at the vapor/amine aqueous interface, further stating the differences between the homogeneous and heterogeneous CO 2 chemical conversion. The design of novel and cost-effective capture systems, such as those offered by amine-based scrubbing solutions, working at low (e.g., ambient) CO 2 partial pressure should explore the use of novel solvents, different from aqueous mixtures, to overcome the limits of the current absorbents.

The amino acids synthesis from elementary precursors in abiotic conditions is traditionally described according to the Strecker reaction, thoroughly invoked to justify the observation of amino acids in extraterrestrial samples and their emergence in the primordial Earth. To this day, however, a quantitative microscopic description of the mechanism, thermodynamics, and kinetics of the multistep Strecker reaction is still lacking. In the present work we tackle this study by adopting a state-of-the-art ab initio computational approach, combining an efficient scheme of exploration of the relevant chemical networks with a rigorous determination of the underlying free energy and transition states. We determine the step-by-step chemical pathway from "Strecker precursors" to glycine in solution and calculate the corresponding full free energy landscape. Our results agree well with the scarce available experimental data and complete them, thus providing the first end-to-end study of this complex reaction, a crucial bottleneck for the emergence of life.

Water pollution by heavy metals is of increasing concern due to its devastating effects on the environment and on human health. For the removal of heavy metals from water sources, natural materials, such as spent-coffee-grains or orange/banana/chestnut peels, appear to offer a potential cheap alternative to more sophisticated and costly technologies currently in use. However, in order to employ them effectively, it is necessary to gain a deeper understanding - at the molecular level - of the heavy metals-bioorganic-water system and exploit the power of computer simulations. As a step in this direction, we investigate via atomistic simulations the capture of lead ions from water by hemicellulose - the latter being representative of the polysaccharides that are common components of vegetables and fruit peels - as well as the reverse process. A series of independent molecular dynamics simulations, both classical and ab initio , reveals a coherent scenario which is consistent with what one would expect of an efficient capture, i.e. that it be fast and irreversible: (i) binding of the metal ions via adsorption is found to happen spontaneously on both carboxylate and hydroxide functional groups; (ii) in contrast, metal ion desorption, leading to solvation in water, involves sizable free-energy barriers., Competing Interests: The authors declare no competing financial interest., (This journal is © The Royal Society of Chemistry.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Research in origins of life is an intrinsically multi-disciplinary field, aimed at finding answers to the formidably complex problem of understanding the emergence of life from the modern versions of Charles Darwin's celebrated "primordial soup". In the last few years, thanks to the increasing computational power and the development of sophisticated theoretical and numerical methods, several computational chemistry and physics groups have invested this field, providing new microscopic insights on fundamental prebiotic chemistry phenomena possibly occurring in the early Earth and outer space. This review presents the most successful and powerful approaches in computational chemistry, and the main results thus obtained in prebiotic chemistry and origins of life. The aim of this work is both to describe the state-of-the-art in computational prebiotic chemistry, possibly useful both to theorists and experimentalists in origins of life research, and to suggest future directions and new perspectives offered by modern simulation tools., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The ability to design synthesis processes that are out of equilibrium has opened the possibility of creating nanomaterials with remarkable physicochemical properties, choosing from a much richer palette of possible atomic architectures compared to equilibrium processes in extended systems. In this work, we employ atomistic simulations to demonstrate how to control polymorph selection via the cooling rate during nanoparticle freezing in the case of Ni 3 Al, a material with a rich structural landscape. State-of-the-art free-energy calculations allow us to rationalize the complex nucleation process, discovering a switch between two kinetic pathways, yielding the equilibrium structure at room temperature and an alternative metastable one at higher temperature. Our findings address the key challenge in the synthesis of nanoalloys for technological applications, i.e., rationally exploiting the competition between kinetics and thermodynamics by designing a treatment history that forces the system into desirable metastable states.

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

Methane hydrate was recently shown, both experimentally and through simulations, to be stable up to the remarkably high pressure of 150 GPa. A new methane hydrate high-pressure (MH-IV) phase, reminiscent of ice at ambient pressure, was described for pressures above approximately 40 GPa. We disentangle here the main contributions to the relative stability of the lower pressure, denoted MH-III, and the high-pressure MH-IV structures. Through several simulation techniques, including metadynamics and path integral molecular dynamics for nuclear quantum effects, we analyze the phase transition mechanism, which implies hydrogen bond breaking and reforming, as well as methane reordering. The transition pathway is far from trivial, and the quantum delocalization of the hydrogen nuclei plays a significant role.

We investigate a LiCl:6D 2 O water solution in the deep undercooled regime as a function of pressure by neutron diffraction, small angle neutron scattering, and molecular dynamics simulations. We probe the structure of the undercooled liquid and the existence of density fluctuations in the system along isotherms just above the observed first-order-like polymorphic transition in the solid between a high density amorphous (s-HDA) and a very high density amorphous (s-VHDA) form [L. E. Bove et al., Phys. Rev. Lett. 106, 125701 (2011)]. We observe a continuous pressure evolution of the structure factor of the liquid and the absence of density fluctuations when crossing the continuation line of the s-HDA and s-VHDA boundary. These results indicate that no liquid-liquid transition is observed in the system in correspondence with the solid polyamorphism.

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

Cryo-electron microscopy (cryo-EM) has recently provided invaluable experimental data about the full-length cystic fibrosis transmembrane conductance regulator (CFTR) 3D structure. However, this experimental information deals with inactive states of the channel, either in an apo, quiescent conformation, in which nucleotide-binding domains (NBDs) are widely separated or in an ATP-bound, yet closed conformation. Here, we show that 3D structure models of the open and closed forms of the channel, now further supported by metadynamics simulations and by comparison with the cryo-EM data, could be used to gain some insights into critical features of the conformational transition toward active CFTR forms. These critical elements lie within membrane-spanning domains but also within NBD1 and the N-terminal extension, in which conformational plasticity is predicted to occur to help the interaction with filamin, one of the CFTR cellular partners.

Understanding the mechanism of spontaneous formation of ribonucleotides under realistic prebiotic conditions is a key open issue of origins-of-life research. In cells, de novo and salvage nucleotide enzymatic synthesis combines 5-phospho-α-d-ribose-1-diphosphate (α-PRPP) and nucleobases. Interestingly, these reactants are also known as prebiotically plausible compounds. Combining ab initio molecular dynamics simulations with recently developed reaction exploration and enhanced sampling methods, we show that nucleobases and α-PRPP should spontaneously combine, under mild hydrothermal conditions, with an exothermic reaction and a facile mechanism, forming both purine and pyrimidine ribonucleotides. Surprisingly, this mechanism is very similar to the biological one and yields ribonucleotides with the same anomeric carbon chirality as in biological systems. Mass spectrometry experiments performed on solutions of adenine and PRPP in similar conditions support the formation of AMP. These results suggest that natural selection might have optimized, through enzymes, a pre-existing ribonucleotide formation mechanism, carrying it forward to modern life forms.