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Spillover of adsorbed species from one active site to another is a key step in heterogeneous catalysis. However, the factors controlling this step, particularly the spillover of polyatomic species, have rarely been studied. Herein, we investigate the spillover dynamics of H* and CH3* species on a single‐atom alloy surface (Rh/Cu(111)) upon the dissociative chemisorption of methane (CH4), using molecular dynamics that considers both surface phonons and electron‐hole pairs. These dynamical calculations are made possible by a high‐dimensional potential energy surface machine learned from density functional theory data. Our results provide compelling evidence that the H* and CH3* can spill over on the metal surface at experimental temperatures and reveal novel dynamical features involving an internal motion during diffusion for CH3*. Increasing surface temperature has a minor effect on promoting spillover, as geminate recombinative desorption becomes more prevalent. However, the poisoning of the active site can be mitigated by the frequent gaseous molecular collisions that occur under ambient pressure in real‐world catalysis, which transfer energy to the trapped adsorbates. Interestingly, the bulky CH3* exhibits a significant spillover advantage over the light H* due to its larger size, which facilitates energy acquisition. These insights help to advance our understanding of spillover in heterogeneous catalysis. [ABSTRACT FROM AUTHOR]

In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method's performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies. [ABSTRACT FROM AUTHOR]

The potential energy surfaces and photoelectron spectra of the interstellar molecule H2S and its cation H2S+ were calculated using ab initio methods based on the MOLPRO software package. First, the potential energy surfaces of H2S expanded along the normal coordinates were obtained at the（U）CCSD/cc-pVQZ level of theory. The potential energy surfaces visually describe the effect of different vibrational modes coupling on the molecular energy change, and the combined effect of the asymmetric stretching vibration of the S-H bond and the in-plane bending vibration makes the potential energy change range of the system significantly larger. The vibrational multi-reference configuration interaction method was used to calculate the anharmonic vibrational frequencies and vibrational spectrum, and the calculations showed that strong Fermi resonances appear between the overtones and combination bands, resulting in a significant enhancement of the infrared intensity at the corresponding bands. Finally, the photoelectron spectrum of H2S X¹A1→ H2S+ X²B1 was calculated for the first time using the Raman wave function and the contracted invariant Krylov subspaces method. This study will help to further understand the internal structure of interstellar molecules and provide a reference for experimental studies and interstellar observations. [ABSTRACT FROM AUTHOR]

Inspired by the brilliant photochemical and photophysical properties of organic molecule containing the halide substitution that could be potentially applied across various disciplines, in this work, effects of atomic electronegativity of halogen (F, Cl, and Br) on excited state hydrogen bond effects and excited state intramolecular proton transfer (ESIPT) reaction. We present the characteristic 4-chloro-2-[1-(4-methoxy-phenyl)-4,5-diphenyl-1H-imidazol-2-yl]-phenol (CMDIP) that is the main objective of this study to explore in detail the influence of the change of atomic electronegativity by photoexcitation. By comparing the structural changes and infrared (IR) vibrational spectra of the CMDIP derivatives (CMDIP-F, CMDIP-Cl, and CMDIP-Br) fluorophores in S0 and S1 states, combined with the preliminary detection of hydrogen bond interaction by core-valence bifurcation (CVB) index, we can conclude that the hydrogen bond could be strengthened in S1 state, which is favorable for the occurrence of ESIPT reactions. The charge recombination behavior of hydrogen bond induced by photoexcitation also further illustrates this point. Via constructing potential energy curves (PESs) based on restrictive optimization and searching transition state (TS) form, we confirm the change of atomic electronegativity has a regulatory effect on the ESIPT behavior for CMDIP derivatives, that is, the higher the atomic electronegativity is more conducive to the ESIPT reaction. [ABSTRACT FROM AUTHOR]

In the previous work, a novel and yet complicated mathematical model to predict the presence of triply degenerate seam was presented and was illustrated its efficiency using some physical models. Here in this work, a discussion on the ability of the model to account for the cusping features found in the potential energy surfaces (PESs) is presented in addition to a discussion on the derivatives and its discontinuities and infiniteness at a specific point. Analytical evaluation of derivatives is possible in principle but in practice, numerical approximation to the exact derivatives is the only way out to solve this problem. Two examples (one being physical and the other being the chemically realistic) are considered to probe the above-mentioned properties of the simultaneous model. The integration of the model function gives finite values everywhere disregarding the presence and absence of degeneracy. This is just opposite to derivative evaluation. A noteworthy point is that the analytical model fits everywhere equally well all over the topography of PESs including the vicinity of seam. This quality makes the model an ideal choice for modelling purpose. [ABSTRACT FROM AUTHOR]

Chemical reactions between Be+ ions and H2 molecules have significance in the fields of ultracold chemistry and astrophysics, but the corresponding dynamics studies on the ground-state reaction have not been reported because of the lack of a global potential energy surface (PES). Herein, a globally accurate ground-state BeH2+ PES is constructed using the neural network model based on 18,657 ab initio points calculated by the multi-reference configuration interaction method with the aug-cc-PVQZ basis set. On the newly constructed PES, the state-to-state quantum dynamics calculations of the Be+(2S) + H2(v0 = 0; j0 = 0) and Be+(2S) + D2(v0 = 0; j0 = 0) reactions are performed using the time-dependent wave packet method. The calculated results suggest that the two reactions are dominated by the complex-forming mechanism and the direct abstraction process at relatively low and high collision energies, respectively, and the isotope substitution has little effect on the reaction dynamics characteristics. The new PES can be used to further study the reaction dynamics of the BeH2+ system, such as the effects of rovibrational excitations and alignment of reactant molecules, and the present dynamics data could provide an important reference for further experimental studies at a finer level. [ABSTRACT FROM AUTHOR]

Motivated by the captivating allure of exquisitely regulated characteristics exhibited by 2‐(2‐hydroxyphenyl)‐benzoxazole and its derivatives in the domains of photochemistry and photophysics, our current endeavor primarily focuses on delving into the intricacies of photo‐induced excited state reactions for derivatives of 2,5‐bis(benzoxazol‐2‐yl)‐thiophene‐3,4‐diol (BTD). Given the profound impact of chalcogen element doping, our primary focus lies in investigating the excited state behaviors of BTD‐O, BTD‐S, and BTD‐Se fluorophores. Through simulations encompassing variations in geometry and vertical excitation charge reorganization, we unveil atomic‐electronegativity‐dependent hydrogen bonding interactions and photoexcitation‐induced charge recombination that can significantly augment the intramolecular double proton transfer (ESDPT) reaction in the excited state for BTD‐O, BTD‐S, and BTD‐Se fluorophores. By constructing potential energy surfaces and identifying transition state forms, we elucidate the ultrafast stepwise ESDPT mechanism facilitated by the low potential barriers. Moreover, we rigorously validate the chalcogen atomic electronegativity‐driven regulation of the stepwise ESDPT mechanism. We sincerely anticipate that manipulating solvent polarity will pave the way for groundbreaking advancements in luminescent materials. [ABSTRACT FROM AUTHOR]

Density functional theory was employed to investigate methylidyne radical reaction on to the unsaturation of dipyrromethanol moiety through cycloaddition forming a six membered di(pyridin-2-yl)methanol followed by H-elimination via ring expansion. Time-dependent density functional theory (TD-DFT) simulations were performed to figure out the ultraviolet–visible spectrum of dipyrromethanol and di(pyridin-2-yl)methanol. Ultraviolet–visible spectrum of dipyrromethanol and di(pyridin-2-yl)methanol shows that two sharp absorption peaks were obtained at λmax = 161 nm and 206 nm for the reactant dipyrromethanol while a sharp peak was observed for the product di(pyridin-2-yl)methanol at shorter wavelength (λmax = 170 nm). The theoretical IR spectrum of dipyrromethanol and di(pyridin-2-yl)methanol shows that a significant red shift of 129 cm−1 was observed in the case of di(pyridin-2-yl)methanol due to presence of two strong intra-molecular hydrogen bonds, which indicates its higher stability than the dipyrromethanol. HOMO–LUMO energy gap calculations and natural bond orbital (NBO) analysis endorse the type of electronic transition for the desired product di(pyridin-2-yl)methanol is π–π*. The global reactivity analysis shows the electron donor and acceptor nature of dipyrromethanol and methylidyne radical, respectively. A potential energy surface is constructed via methylidyne radical addition, ring expansion, and hydrogen elimination transition states. Thermodynamics study and potential energy surface of the title reaction indicates its highly exothermicity and spontaneous in nature. [ABSTRACT FROM AUTHOR]

In this study, the mechanism of the reaction between 4-methyl aniline and hydroxyl free radicals was computed using the M06-2X and CCSD(T) methods, along with the 6-311++G(3df,2p) basis set. The kinetics of the reaction were calculated utilizing the transition state theory and the microcanonical Rice–Ramsperger–Kassel–Marcus theory. The calculated results revealed that NH-C6H4-CH3 was the key product of the system. The total rate coefficient of the system, k_total = 2.04 × 10−18 T2.07 exp[(11.2 kJ/mol)/RT] cm3/s, was found under the 300–2000 K interval, with P = 760 Torr. At the ambient conditions, the velocity of this reaction was about ten times larger than that of the reaction between C6H5CH3 and hydroxyl free radicals, but it was smaller than the aniline + OH rate.

Vibrational spectroscopy enables critical insight into the structural and dynamic properties of molecules. Presently, the majority of theoretical approaches to spectroscopy employ wavefunction‐based ab initio or density functional methods that rely on the harmonic approximation. This approximation breaks down for large molecules with strongly anharmonic bonds or for molecules with large internuclear separations. An alternative to these methods involves generating molecular anharmonic potential energy surfaces (potentials) and using them to extrapolate the vibrational frequencies. This study examines the efficacy of density functional theory (DFT) and the correlation consistent Composite Approach (ccCA) in generating anharmonic frequencies from potentials of small main group molecules. Vibrational self‐consistent field Theory (VSCF) and post‐VSCF methods were used to calculate the fundamental frequencies of these molecules from their potentials. Functional choice, basis set selection, and mode‐coupling are also examined as factors in influencing accuracy. The absolute deviations for the calculated frequencies using potentials at the ccCA level of theory were lower than the potentials at the DFT level. With DFT resulting in bending modes that are better described than those of ccCA, a multilevel DFT:ccCA approach where DFT potentials are used for single vibrational mode potentials and ccCA is used for vibrational mode‐mode couplings can be utilized for larger polyatomic systems. The frequencies obtained with this multilevel approach using VCIPSI‐PT2 were closer to experimental frequencies than the scaled harmonic frequencies, indicating the success of utilizing post‐VSCF methods to generate more accurate representations of computed infrared spectra. [ABSTRACT FROM AUTHOR]

Inspired by the brilliant photophysical properties of novel organic molecules containing chalcogenide substitution, in this work, effects of atomic electronegativity of chalcogen (O, S and Se) on hydrogen bond interactions and proton transfer (PT) reaction are focused. We present the characteristic CN-BTN is the main objective to explore the influence of atomic electronegativity on the hydrogen bond interaction and excited state intramolecular proton transfer (ESIPT) behaviour by photoexcitation. By comparing the structural changes and infrared (IR) vibrational spectra of the CN-BTN derivatives (CN-BTN-O, CN-BTN-S and CN-BTN-Se) fluorophores in S0 and S1 states, combined with the preliminary detection of hydrogen bond interaction by core-valence bifurcation (CVB) index, we can conclude that hydrogen bond is strengthened in S1 state, which is favourable for the occurrence of ESIPT reaction. The charge recombination behaviour of hydrogen bond induced by photoexcitation also further illustrates this point. Via constructing potential energy curves (PECs) based on restrictive optimisation and searching transition state (TS) configuration, we finally clarify the ESIPT mechanism for CN-BTN derivatives. Specially, we confirm change of atomic electronegativity has a regulatory effect on the ESIPT behaviour of CN-BTN derivatives, that is, the lower the atomic electronegativity is more conducive to the ESIPT reaction. [ABSTRACT FROM AUTHOR]

In this study, the mechanism of the reaction between 4-methyl aniline and hydroxyl free radicals was computed using the M06-2X and CCSD(T) methods, along with the 6-311++G(3df,2p) basis set. The kinetics of the reaction were calculated utilizing the transition state theory and the microcanonical Rice–Ramsperger–Kassel–Marcus theory. The calculated results revealed that NH-C6H4-CH3 was the key product of the system. The total rate coefficient of the system, k_total = 2.04 × 10−18 T2.07 exp[(11.2 kJ/mol)/RT] cm3/s, was found under the 300–2000 K interval, with P = 760 Torr. At the ambient conditions, the velocity of this reaction was about ten times larger than that of the reaction between C6H5CH3 and hydroxyl free radicals, but it was smaller than the aniline + OH rate. [ABSTRACT FROM AUTHOR]

An accurate interatomic potential energy surface for ground state of SH2− is reported at the multireference configuration interaction level of theory using aug–cc–pVQZ basis set. The calculated ab initio energy points are utilized, after semi-empirically correcting the dynamical correlation by the many-body expansion-scaled external correlation method. To verify the accuracy of the potential energy surface, molecular reaction dynamics, including the relationship between reaction probability and collision energy, thermal rate constant, and integral scattering cross section are calculated by using the quantum time-dependent wave packet method. The final results show that our potential energy surface is accurate and the molecular reaction dynamics results are satisfactory, and the accurate potential energy surface and its dynamics results can also lay a foundation for the future experimental research of SH−-containing molecules. This study provides a reasonable ionic chemical dissociation channel for the dynamics, A global many-body expansion (MEB) PES is reported by fitting high-precision ab initio energies calculated at the multireference configuration interaction level with Davidson correction (MRCI(Q)) and the aug-cc-pVQZ basis set (AVQZ). By comparing with the integral cross section with experimental result, which have agreement with the previous experimental and theoretical values. Our theoretical results can provide effective support for future dynamic experiments. [ABSTRACT FROM AUTHOR]

Recently, surface‐hopping ab initio molecular dynamics (SH‐AIMD) simulations have come to be used to discuss the mechanisms and dynamics of excited‐state chemical reactions, including internal conversion and intersystem crossing. In dynamics simulations involving intersystem crossing, there are two potential energy surfaces (PESs) governing the motion of nuclei: PES in a spin‐pure state and PES in a spin‐mixed state. The former gives wrong results for molecular systems with large spin‐orbit coupling (SOC), while the latter requires a potential gradient that includes a change in SOC at each point, making the computational cost very high. In this study, we systematically investigate the extent to which the magnitude of SOC affects the results of the spin‐pure state‐based dynamics simulations for the hydride MH2 (M = Si, Ge, Sn, Pb) by performing SH‐AIMD simulations based on spin‐pure and spin‐mixed states. It is clearly shown that spin‐mixed state PESs are indispensable for the dynamics simulation of intersystem crossing in systems containing elements Sn and Pb from the fifth period onward. Furthermore, in addition to the widely used Tully's fewest switches (TFS) algorithm, the Zhu‐Nakamura (ZN) global switching algorithm, which is computationally less expensive, is applied to SH for comparison. The results from TFS‐ and ZN‐SH‐AIMD methods are in qualitative agreement, suggesting that the less expensive ZN‐SH‐AIMD can be successfully utilized to investigate the dynamics of photochemical reactions based on quantum chemical calculations. [ABSTRACT FROM AUTHOR]

Gas-phase reactions involving simplest Criegee intermediate (CH2OO) have been the current hot topic due to its vital role in atmospheric chemistry. In this study, high-level ab initio calculations are used to investigate the energetics and kinetics for the reaction of CH2OO + ROH → ROCHO + H2O (R=CH3, CH3CH2 and (CH3)2CH). Energies of the stationary points are computed at the CCSD(T)/M06-2X/6-311++G(3d,3pd)//M06-2X/6-311++G(3d,3pd) level of theory. Reaction is going through a 1,2-addition and water elimination step leading to the formation of alkoxymethyl hydroperoxides and alkyl formates, respectively. The barrier heights for the 1,2-addition step with methanol, ethanol, and isopropanol were found to be − 3.1, − 3.7, and − 4.8 kcal mol−1, and water elimination steps were found to be 2.2, 1.5, and 1.6 kcal mol−1, respectively, relative to the energies of the starting reactants. The rate constants for addition and elimination channels were calculated using canonical variational transition state theory in conjugation with small-curvature tunneling and the interpolated single point energy method between the temperature range of 200 and 500 K. In addition, the thermochemistry analysis indicates that addition and elimination channels are thermodynamically feasible and the formation of alkyl formates is entropically more favored when compared to the formation of alkoxymethyl hydroperoxide along the reaction path in the potential energy surface. The pressure-dependent microcanonical rate constants for both addition and elimination channels were also estimated using the Rice–Ramsperger–Kassel–Marcus theory and discussed in this study. [ABSTRACT FROM AUTHOR]

The energy pathways of possible decomposition and isomerization reactions of iso-propyl (i-C3H7) and n-propyl (n-C3H7) radicals are studied by computational methods of quantum chemistry. The B3LYP, M062X, MP2, and CBS-QB3 methods are used to localize stationary points on the potential energy surface of a system containing propyl radicals. A number of intermediate compounds formed during the isomerization and decomposition of propyl radicals are identified and information is obtained on their structure and thermochemical parameters. Based on the results of the research, a diagram of the energy levels of the system under consideration is constructed. [ABSTRACT FROM AUTHOR]

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Transition state (TS) on the potential energy surface (PES) plays a key role in determining the kinetics and thermodynamics of chemical reactions. Inspired by the fact that the dynamics of complex systems are always driven by rare but significant transition events, we herein propose a TS search method in accordance with the Q‐learning algorithm. Appropriate reward functions are set for a given PES to optimize the reaction pathway through continuous trial and error, and then the TS can be obtained from the optimized reaction pathway. The validity of this Q‐learning method with reasonable settings of Q‐value table including actions, states, learning rate, greedy rate, discount rate, and so on, is exemplified in 2 two‐dimensional potential functions. In the applications of the Q‐learning method to two chemical reactions, it is demonstrated that the Q‐learning method can predict consistent TS and reaction pathway with those by ab initio calculations. Notably, the PES must be well prepared before using the Q‐learning method, and a coarse‐to‐fine PES scanning scheme is thus introduced to save the computational time while maintaining the accuracy of the Q‐learning prediction. This work offers a simple and reliable Q‐learning method to search for all possible TS and reaction pathway of a chemical reaction, which may be a new option for effectively exploring the PES in an extensive search manner. [ABSTRACT FROM AUTHOR]

The status of machine learning in chemistry and related disciplines is becoming increasingly prominent, but its application in undergraduate chemistry experiments has yet to be seen. Existing computational chemistry experiments mostly focus on the application of quantum chemistry methods to study molecular properties and chemical reactions. In order to popularize machine learning as a powerful tool, this article will introduce a comprehensive computational chemistry experiment suitable for senior undergraduate students or graduate students. Through this experiment, students can gain a preliminary understanding of the basic principles and operational procedures of machine learning, while also develop their ability to apply quantum chemistry and machine learning to solve chemical problems. [ABSTRACT FROM AUTHOR]

In the last years, state-to-state molecular dynamics simulations of some basic elementary processes, occurring at the gas–surface interface in a wide range of temperatures and collision energies, have been performed by adopting new potential energy surfaces. In this contribution, our attention is mostly addressed to the role of long-range forces, determining the physisorption of gaseous molecules on the surface. Such forces, formulated in terms of the improved Lennard–Jones interaction potential model, control the formation of precursor or pre-reactive state that plays a crucial role in the dynamical evolution of molecules impinging on the surface in the range of low–intermediate collision kinetic energies. The study focuses on the collisions of H2, O2, N2 and CO, initially in their ground and excited vibro-rotational levels, on a graphite surface. The resulting dispersion coefficients, which control the capture of impinging molecules, are compared and found in good agreement with those available in the literature. New selectivity and peculiarities of scattered molecules, crucial to control the kinetics of elementary chemical processes occurring at the gas–surface interfaces under thermal and sub-thermal conditions, of interest in different applied fields, are highlighted. [ABSTRACT FROM AUTHOR]

SO2 (Sulfur dioxide) is the major precursor to the production of sulfuric acid (H2SO4), contributing to acid rain and atmospheric aerosols. Sulfuric acid formed from SO2 generates light-reflecting sulfate aerosol particles in the atmosphere. This property has prompted recent geoengineering proposals to inject sulfuric acid or its precursors into the Earth’s atmosphere to increase the planetary albedo to counteract global warming. SO2 oxidation in the atmosphere by the hydroxyl radical HO to form HOSO2 is a key rate-limiting step in the mechanism for forming acid rain. However, the dynamics of the HO + SO2 → HOSO2 reaction and its slow rate in the atmosphere are poorly understood to date. Herein, we use photoelectron spectroscopy of cryogenically cooled HOSO2-anion to access the neutral HOSO2 radical near the transition state of the HO+SO2 reaction. Spectroscopic and dynamic calculations are conducted on the first ab initio-based full-dimensional potential energy surface to interpret the photoelectron spectra of HOSO2-and to probe the dynamics of the HO + SO2 reaction. In addition to the finding of a unique pre-reaction complex (HO⋯SO2) directly connected to the transition state, dynamic calculations reveal that the accessible phase space for the HO + SO2 → HOSO2 reaction is extremely narrow, forming a key reaction bottleneck and slowing the reaction rate in the atmosphere, despite the low reaction barrier. This study underlines the importance of understanding the full multidimensional potential energy surface to elucidate the dynamics of complex bimolecular reactions involving polyatomic reactants. [ABSTRACT FROM AUTHOR]

This study proposes a new approach for constructing intermolecular potential energy surfaces (PESs) of van der Waals (vdW) complexes using neural networks. The descriptors utilized in this neural network model are split into two parts: radial parts representing the intermolecular stretching vibrations between monomers and angular parts describing the relative orientation of these molecules. Specifically, the parity-adapted rotational basis functions used in the bound state calculation are taken as the angular descriptors, which ensure the correct symmetry of the PES. The number of orthogonal rotational basis functions is controlled by the maximum value of the angular momentum quantum number. In addition, the symmetry of monomer molecules is achieved by restricting the quantum number of the rotational basis function. The descriptors for five types of van der Waals complexes, including atom-linear, atom-nonlinear, linear-linear, linear-nonlinear and nonlinear-nonlinear molecules complexes, have been derived in this work. The neural network models with these newly developed descriptors were then applied to construct PESs of two van der Waals complexes, Ar-NaCl and N2-OCS. The root-mean-square error values between the fitted and ab initio energies are found to be 0.11 cm−1 and 0.26 cm−1 for Ar-NaCl and N2-OCS, respectively. These results indicate that this method is accurate and effective for constructing high-precision PESs of vdW complexes. [ABSTRACT FROM AUTHOR]

The impact of the chalcogen atomic electronegativity (O, S, and Se atoms) of new organic molecules on excited-state dynamical reactions is self-evident. Inspired by this kind of distinguished photochemical characteristic, in this work, we performed a computational investigation of chalcogen-substituted 3,6-bis(4,5-dihydroxyoxazo-2-yl)benzene-1,2-diol (BDYBD) derivatives (i.e., BDYBD-O, BDYBD-S, and BDYBD-Se). In this paper, we pay close attention to characteristic BDYBD derivatives that contain intramolecular double hydrogen bonds (O1–H2···N3 and O4–H5···N6). The main goal of this study was to explore how changes in atomic electronegativity affect the way hydrogen bonds interact and how excited molecules affect transfer protons. We go into further detail in the main text of the paper. By fixing our attention to geometrical variations and infrared (IR) vibrational spectra between the S0 and S1 states, exploring hydrogen bonding behaviors using the core-valence bifurcation (CVB) index, and simulating hydrogen bonding energy (EHB) via the atom in molecule (AIM) method, we clarified the photo-induced strengthened dual hydrogen bonding interactions that facilitate the excited-state dual-proton transfer (ESDPT) behavior of BDYBD derivatives. The reorganization of charge stemming from photoexcitation further verifies the tendencies of ESDPT reactions. We relied on constructing potential energy surfaces (PESs) by adopting a restrictive optimization approach, and herein, we finally clarify the gradual ESDPT mechanism of BDYBD derivatives. Particularly, we confirm that the variation in chalcogen atomic electronegativity has a regulatory effect on the ESDPT behavior of BDYBD derivatives; that is, the lower the atomic electronegativity, the more favorable it is for the gradual ESDPT reaction. [ABSTRACT FROM AUTHOR]

In the teaching of university-level physical chemistry, the Arrhenius formula elucidates the relationship between the rate constant of a reaction, temperature, and activation energy. However, it is difficult for students to understand and master the formula simply from its expression. Taking the reaction of isopropyl nitrate with Cl atoms, OH and NO3 radicals as examples, the rate constants of the reactions at different temperatures were calculated based on the potential energy surface by the Transition State Theory. The change trends of rate constants with temperature for the reactions with different energy barriers have been visually analyzed. We hope to help students understand Arrhenius formula in essence by analyzing specific cases, so as to broaden their knowledge and cultivate their scientific inquiry ability. [ABSTRACT FROM AUTHOR]

Dynamical reweighting techniques aim to recover the correct molecular dynamics from a simulation at a modified potential energy surface. They are important for unbiasing enhanced sampling simulations of molecular rare events. Here, we review the theoretical frameworks of dynamical reweighting for modified potentials. Based on an overview of kinetic models with increasing level of detail, we discuss techniques to reweight two-state dynamics, multistate dynamics, and path integrals. We explore the natural link to transition path sampling and how the effect of nonequilibrium forces can be reweighted. We end by providing an outlook on how dynamical reweighting integrates with techniques for optimizing collective variables and with modern potential energy surfaces. [ABSTRACT FROM AUTHOR]

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning. [ABSTRACT FROM AUTHOR]

The effect of noise in the input data for learning potential energy surfaces (PESs) based on neural networks for chemical applications is assessed. Noise in energies and forces can result from aleatoric and epistemic errors in the quantum chemical reference calculations. Statistical (aleatoric) noise arises for example due to the need to set convergence thresholds in the self consistent field (SCF) iterations whereas systematic (epistemic) noise is due to, inter alia, particular choices of basis sets in the calculations. The two molecules considered here as proxies are H2CO and HONO which are examples for single- and multi-reference problems, respectively, for geometries around the minimum energy structure. For H2CO it is found that adding noise to energies and forces with magnitudes representative of single-point calculations does not deteriorate the quality of the final PESs whereas increasing the noise level commensurate with electronic structure calculations for more complicated, e.g. metal-containing, systems is expected to have a more notable effect. On the other hand, for HONO which requires a multi-reference treatment, a clear correlation between model quality and the degree of multi-reference character as measured by the T1 amplitude is found. It is concluded that for chemically “simple” cases the effect of aleatoric and epistemic errors is manageable without evident deterioration of the trained model, but more care needs to be exercised for situations in which multi-reference effects are present.

Developing full-dimensional machine-learned potentials with the current “gold-standard” coupled-cluster (CC) level is challenging for medium-sized molecules due to the high computational cost. Consequently, researchers are often bound to use lower-level electronic structure methods such as density functional theory or second-order Møller–Plesset perturbation theory (MP2). Here, we demonstrate on a representative example that gold-standard potentials can now be effectively constructed for molecules of 15 atoms using off-the-shelf hardware. This is achieved by accelerating the CCSD(T) computations via the accurate and cost-effective frozen natural orbital (FNO) approach. The Δ-machine learning (Δ-ML) approach is employed with the use of permutationally invariant polynomials to fit a full-dimensional potential energy surface of the acetylacetone molecule, but any other effective descriptor and ML approach can similarly benefit from the accelerated data generation proposed here. Our benchmarks for the global minima, H-transfer TS, and many high-lying configurations show the excellent agreement of FNO-CCSD(T) results with conventional CCSD(T) while achieving a significant time advantage of about a factor of 30–40. The obtained Δ-ML PES shows high fidelity from multiple perspectives including energetic, structural, and vibrational properties. We obtain the symmetric double well H-transfer barrier of 3.15 kcal/mol in excellent agreement with the direct FNO-CCSD(T) barrier of 3.11 kcal/mol as well as with the benchmark CCSD(F12*)(T+)/CBS value of 3.21 kcal/mol. Furthermore, the tunneling splitting due to H-atom transfer is calculated using a 1D double-well potential, providing improved estimates over previous ones obtained using an MP2-based PES. The methodology introduced here represents a significant advancement in the efficient and precise construction of potentials at the CCSD(T) level for molecules above the current limit of 15 atoms.

The interaction between water and graphene is important for understanding the thermodynamic and kinetic properties of water on hydrophobic surfaces. In this study, we constructed a high-dimensional potential energy surface (PES) for the water-graphene system using the many-body expansion scheme and neural network fitting. By analyzing the landscape of the PES, we found that the water molecule exhibits a weak physisorption behavior with a binding energy of about − 1000 cm−1 and a very low diffusion barrier. Furthermore, extensive molecular dynamics were performed to investigate the adsorption and diffusion dynamics of a single water on a graphene surface at temperatures ranging from 50 to 300 K. Potential-of-mean-forces were computed from the trajectories, providing a comprehensive and accurate description of the water-graphene interaction kinetics.

In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method’s performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies.

Chemical reactions between Be+ ions and H2 molecules have significance in the fields of ultracold chemistry and astrophysics, but the corresponding dynamics studies on the ground-state reaction have not been reported because of the lack of a global potential energy surface (PES). Herein, a globally accurate ground-state BeH2+ PES is constructed using the neural network model based on 18,657 ab initio points calculated by the multi-reference configuration interaction method with the aug-cc-PVQZ basis set. On the newly constructed PES, the state-to-state quantum dynamics calculations of the Be+(2S) + H2(v0 = 0; j0 = 0) and Be+(2S) + D2(v0 = 0; j0 = 0) reactions are performed using the time-dependent wave packet method. The calculated results suggest that the two reactions are dominated by the complex-forming mechanism and the direct abstraction process at relatively low and high collision energies, respectively, and the isotope substitution has little effect on the reaction dynamics characteristics. The new PES can be used to further study the reaction dynamics of the BeH2+ system, such as the effects of rovibrational excitations and alignment of reactant molecules, and the present dynamics data could provide an important reference for further experimental studies at a finer level.

The current state of the protein folding problem and other biopolymers folding is discussed. The concept of a multidimensional potential energy surface and free energy surface for linear polymers is detailed, taking into account the topology of the configuration space and the presence of symmetry elements with respect to the rearrangement of identical monomer units. The presence of kinematic connections for conformational movements in a viscous medium leads to a tendency for the formation of helical structures of linear polymers. The dynamic effects of viscosity also lead to an almost uniform distribution of energy dissipation rates across the nodes of the chain. The combination of free energy surface topography and viscosity effects provides a physical basis for advancing folding theory toward interpreting a variety of experimental observations and elucidating principles of amino acid code formation for 3D protein structures. The relationship between the denaturation temperature of the folded state of the biopolymer and the energy of nonvalent interactions between monomers in the chain is analyzed. [ABSTRACT FROM AUTHOR]

Accurate potential energy surface (PES) calculation is the basis of molecular dynamics research. Using deep learning (DL) methods can improve the speed of PES calculation while achieving competitive accuracy to ab initio methods. However, the performance of DL model is extremely sensitive to the distribution of training data. Without sufficient training data, the DL model suffers from overfitting issues that lead to catastrophic performance degradation on unseen samples. To solve this problem, based on the message passing paradigm of graph neural networks, we innovatively propose an edge-aggregate-attention mechanism, which specifies the weight based on node and edge information. Experiments on MDI7 and QM9 datasets show that our model not only achieves higher PES calculation accuracy but also has better generalization ability compared with Schnet, which demonstrates that edge-aggregate-attention can better capture the inherent features of equilibrium and non-equilibrium molecular conformations. [ABSTRACT FROM AUTHOR]

A case study has been performed on the adaptability of electron-localization strategy in designing clusters with planar tetracoordinate nitrogen (ptN) through the detailed comparison between ptN clusters NLi3E+ (E=N, P, As) and their planar tetracoordinate carbon (ptC) counterparts CLi3E (E=N, P, As). The results revealed that NLi3E+ clusters possessed similar planar geometries to CLi3E, which are both determined by the existence of a localized π bond. Nevertheless, NLi3E+ clusters possess the obviously higher electronic, thermodynamic, and dynamic stabilities than CLi3E clusters, as reflected by wider HOMO-LUMO gaps (4.58-4.68 eV versus 2.10–2.74 eV), the overall lower-lying positions on potential energy surfaces verified at the CCSD(T)/aug-cc-pVTZ level, and better rigidity during the molecular dynamic simulations at the PBE/DZVP level. Therefore, our results suggest that electron-localization strategy may be more suitable for designing the clusters, whose central atom possesses relatively higher electronegativity and more favours the localized bond. Due to the good stability, the cationic NLi3E+ clusters designed in this work may be suitable for gas phase generation, mass-selection, and spectroscopic characterization. [ABSTRACT FROM AUTHOR]

Hydrogen spillover, involving the surface migration of dissociated hydrogen atoms from active metal sites to the relatively inert catalyst support, plays a crucial role in hydrogen‐involved catalytic processes. However, a comprehensive understanding of how H atoms are driven to spill over from active sites onto the catalyst support is still lacking. Here, we examine the atomic‐scale perspective of the H spillover process on a Pt/Cu(111) single atom alloy surface using machine‐learning accelerated molecular dynamics calculations based on density functional theory. Our results show that when an impinging H2 dissociates at an active Pt site, the Pt atom undergoes deactivation due to the dissociated hydrogen atoms that attach to it. Interestingly, collisions between H2 and sticking H atoms facilitate H spillover onto the host Cu, leading to the reactivation of the Pt atom and the realization of a continuous H spillover process. This work underscores the importance of the interaction between gas molecules and adsorbates as a driving force in elucidating chemical processes under a gaseous atmosphere, which has so far been underappreciated in thermodynamic studies. [ABSTRACT FROM AUTHOR]

Central‐atom effects on bimolecular nucleophilic substitution (SN2) reactions are well‐known in chemistry, however, the atomic‐level SN2 dynamics at phosphorous (P) centers has never been studied. We investigate the dynamics of the F−+PH2Cl reaction with the quasi‐classical trajectory method on a novel full‐dimensional analytical potential energy surface fitted on high‐level ab initio data. Our computations reveal intermediate dynamics compared to the F−+CH3Cl and the F−+NH2Cl SN2 reactions: phosphorus as central atom leads to a more indirect SN2 reaction with extensive complex‐formation with respect to the carbon‐centered one, however, the title reaction is more direct than its N‐centered pair. Stereospecificity, characteristic at C‐center, does not appear here either, due to the submerged front‐side‐attack retention path and the repeated entrance‐channel inversional motion, whereas the multi‐inversion mechanism discovered at nitrogen center is also undermined by the deep Walden‐well. At low collision energies, 6 % of the PH2F products form with retained configuration, mostly through complex‐mediated mechanisms, while this ratio reaches 24 % at the highest energy due to the increasing dominance of the direct front‐side mechanism and the smaller chance for hitting the deep Walden‐inversion minimum. Our results suggest pronounced central‐atom effects in SN2 reactions, which can fundamentally change their (stereo)dynamics. [ABSTRACT FROM AUTHOR]

Lithium has been paid great attention in recent years thanks to its significant applications for battery and lightweight alloy. Developing a potential model with high accuracy and efficiency is important for theoretical simulation of lithium materials. Here, we build a deep learning potential (DP) for elemental lithium based on a concurrent-learning scheme and DP representation of the density-functional theory (DFT) potential energy surface (PES), the DP model enables material simulations with close-to DFT accuracy but at much lower computational cost. The simulations show that basic parameters, equation of states, elasticity, defects and surface are consistent with the first principles results. More notably, the liquid radial distribution function based on our DP model is found to match well with experiment data. Our results demonstrate that the developed DP model can be used for the simulation of lithium materials. [ABSTRACT FROM AUTHOR]

Bulk layered materials, such as graphite and molybdenum disulfide, have long been used as solid lubricants in various industrial applications. The weak interlayer van der Waals interactions in these materials generate a low shear slip-plane, which reduces the interfacial friction. The cumulative trends toward device miniaturization have increased the need for basic knowledge of the nanoscale friction of contact-mode devices containing layered materials. Further, the decomposition and degradation of bulk layered solids subjected to shear forces are detrimental to their lubricating characteristics. Layered-structure materials, such as graphene, hexagonal boron nitride, and MXenes consisting of single or few atomic layers, behave as a new class of lubricious substances when deposited at a sliding interface. The exceptional mechanical strength, thermal conductivity, electronic properties, large theoretical specific area, and chemical inertness of these materials make them ideal antifriction materials for continuous sliding interfaces, especially when operated at elevated temperatures. These properties hold great promise for widespread applications both in dry environments, such as solid film lubrication for micro/nano-electromechanical systems, nanocomposite materials, space lubrication, and optical devices, as well as in wet environments, such as desalination membranes, lubricant additives, and nanofluidic transporters. However, accurate and reliable prediction of the frictional behavior of layered-structure materials is challenging due to the complex physicochemical transformations encountered under tribostress. The presence of a liquid in the vicinity of a surface in wet-environment applications further complicates the lubrication behavior of layered-structure materials. Furthermore, insight into the origins of interfacial friction and adhesion due to localized contact interactions can be accomplished by atomic-level experimental techniques and computational methods, such as atomic force microscope (AFM) in combination with molecular dynamics (MD) and density functional theory (DFT). The AFM setup mimics asperity-asperity contact at the atomic level and can measure the friction force of layered-structure materials, whereas MD and DFT can provide insight into the chemomechanical transformations commencing at hidden interfaces, which cannot be detected by experimental methods. The objective of this review article is threefold. First, the relationship between friction and potential energy surface is examined for different layered-structure material systems, and the parameters that mainly affect the energy corrugation are interpreted in the context of reported results. Second, the atomic-scale friction mechanisms of layered-structure materials in dry or vacuum environments are discussed in light of experimental and theoretical findings, focusing on the most crucial frictional energy dissipation mechanisms. Third, the complex mechanisms affecting the nanosccale friction of layered-structure materials incorporated in liquid media are introduced for ionic, polar, and non-polar solutions.

Machine learning (ML) techniques are already widely and increasingly used in diverse applications in science and technology, including computational chemistry. Specifically in computational chemistry, neural networks (NN) and kernel methods such as Gaussian process regressions (GPR) have been increasingly used for the construction of potential functions and functionals for density functional theory. While ML techniques have a number of advantages vs intuition-based models, notably their generality and black-box nature, they are still challenged when faced with high dimensionality of the feature space or low and uneven data density – in part because of their general nature. We review recent works using methods such as NNs and GPR as building blocks of composite methods in the framework of an expansion over orders of coupling. We introduce models using NN or GPR-based components as part of HDMR (high-dimensional model representations)-based structures. HDMR is a formalization of orders-of-coupling representations that include the many-body and N-mode representations well known in computational chemistry and allows, in particular, building all terms from one dataset of arbitrarily distributed data. The resulting HDMR-NN and HDMR-GPR combinations and NN with HDMR-GPR derived neuron activation functions not requiring non-linear optimization enhance machine learning capabilities in high dimensional spaces and or with sparse data.