The ab initio calculation of exact quantum reaction rate constants comes at a high cost due to the required dynamics of reactants on multidimensional potential energy surfaces. In turn, this impedes the rapid design of the kinetics for large sets of coupled reactions. In an effort to overcome this hurdle, a deep neural network (DNN) was trained to predict the logarithm of quantum reaction rate constants multiplied by their reactant partition function - rate products. The training dataset was generated inhouse and contains ~1.5 million quantum reaction rate constants for single, double, symmetric and asymmetric one-dimensional potentials computed over a broad range of reactant masses and temperatures. The DNN was able to predict the logarithm of the rate product with a relative error of 1.1%. Further, when comparing the difference between the DNN prediction and classical transition state theory at temperatures below 300K a relative percent error of 31% was found with respect to the exact difference. Systems beyond the test set were also studied, these included the $\sf{H}$ + $\sf{H_2}$ reaction, the diffusion of hydrogen on Ni(100), the Menshutkin reaction of pyridine with $\sf{CH_3Br}$ in the gas phase, the reaction of formalcyanohydrin with $\sf{HS^-}$ in water and the F + HCl reaction. For these reactions, the DNN predictions were accurate at high temperatures and in good agreement with the exact rates at lower temperatures. This work shows that one can take advantage of a DNN to gain insight on reactivity in the quantum regime.
We propose a supervised machine learning algorithm, decision trees, to analyze molecular dynamics output. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-based algorithm aims to identify such features in an approach which is unbiased by human "chemical intuition". We demonstrate the method by analyzing proton exchange reactions in formic acid (FA) solvated in small water clusters. The simulations were performed with ab initio molecular dynamics combined with a method for generati…
A dataset of fully quantum flux-flux correlation functions and reaction rate constants was constructed for organic heterogeneous catalytic surface reactions. Gaussian process regressors were successfully fitted to training data to predict previously unseen test set reaction rate constant products and Cauchy fits of the flux-flux correlation function. The optimal regressor prediction mean absolute percent errors were on the order of 0.5% for test set reaction rate constant products and 1.0% for test set flux-flux correlation functions. The Gaussian process regressors were accurate both when loo…
Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platforms for reaction planning. ML-based techniques can be of particular interest for problems which involve both, computation and experiments. For one, Bayesian inference is a powerful approach to include knowledge from experiment in improving computational models. ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of stat…
Predicting the outcome of a chemical reaction using efficient computational models can be used to develop high-throughput screening techniques. This can significantly reduce the number of experiments needed to be performed in a huge search space, which saves time, effort and expense. Recently, machine learning methods have been bolstering conventional structure-activity relationships used to advance understanding of chemical reactions. We have developed a model to predict the products of catalytic reactions on the surface of oxygen-covered and bare gold using machine learning. Using experiment…
Bio-orthogonal click chemistry has become an indispensable part of the biochemist's toolbox. Despite the wide variety of applications that have been developed in recent years, only a limited number of bio-orthogonal click reactions have been discovered so far, most of them based on (substituted) azides. In this work, we present a computational workflow to discover new candidate bio-orthogonal click reactions. Sampling only around 0.05\% of an overall search space of over 10,000,000 dipolar cycloadditions, we develop a machine learning model able to predict DFT-computed activation and reaction …
Reaction constants in traditional Michaelis-Menten type enzyme kinetics are most often determined through a linear Lineweaver-Burk plot. While such a graphical plot is sometimes good to achieve the end, it is always better to go for a few numerical tests that can assess the quality of the data set being used and hence offer more reliable measures of the quantities sought, furnishing along with appropriate error estimates. In this context, we specifically highlight how cases may appear with negative reaction constants and explore the origin of such bizarre findings.
We propose a methodology for fully automated calculation of thermal rate coefficients of gas phase chemical reactions, which is based on combining the ring polymer molecular dynamics (RPMD) with the machine-learning interatomic potentials actively learning on-the-fly. Based on the original computational procedure implemented in the RPMDrate code, our methodology gradually and automatically constructs the potential energy surfaces (PESs) from scratch with the data set points being selected and accumulated during the RPMDrate simulation. Such an approach ensures that our final machine-learning m…
The reaction ensemble and the constant pH method are well-known chemical equilibrium approaches to simulate protonation and deprotonation reactions in classical molecular dynamics and Monte Carlo simulations. In this article, we show similarity between both methods {under certain conditions}. We perform molecular dynamics simulations of a weak polyelectrolyte in order to compare the titration curves obtained by both approaches. Our findings reveal a good agreement between the methods when the reaction ensemble is used to sweep the reaction constant. Pronounced differences between the reaction …
Hydrogen-atom roaming reactions in water clusters: Unveiling an unusual dimension of water reactivity through first-principles calculations and machine learning
Key Laboratory of Material Simulation Methods & Software of Ministry of Education, College of Physics, Jilin University（教育部材料模拟方法与软件重点实验室，吉林大学物理学院）；Institute of Theoretical Chemistry, College of Chemistry, Jilin University（吉林大学化学学院理论化学研究所）
Equilibrium Constant Differential Equations (ECDE) are derived for several nanoconfined elemental bimolecular reactions in the frameworks of statistical mechanics and the ideal gas model. The ECDEs complement the well-known equilibrium-constant ordinary equations that are used for macroscopic systems. Solving the ECDE numerically or analytically furnishes the average reaction extent, as well as its variance and skewness. This original theoretical-computational methodology fills the gap in studies of nanochemical equilibrium providing a consistent and convenient alternative to derivations based…
Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas- and in the condensed phase. Together with recently developed and currently pursued efforts in integrating and combining this with machine learning techniques provides a unique opportunity to bring such dynamics simulations closer to reality. This perspective delineates the present status of the field from efforts of others in the field and some of your own work and discusses open questions and future prospects.
The pathway of the oxygen evolution reaction at the Pt(111)/water interface is disclosed through constant Fermi level molecular dynamics. Upon the application of a positive bias potential H 2 O ads and OH ads adsorbates are found to arrange in a hexagonal lattice with an irregular alternation. Increasing further the electrode potential then induces the oxygen evolution reaction, which is found to proceed through a hydrogen peroxide intermediate. Calculation of the associated overpotential shows a reduction of 0.2 eV compared to the associative mechanism. This result highlights the forcefullnes…
A novel extension of a rapid compression machine (RCM), namely a Rapid Compression Expansion Machine (RCEM), is described and its use for studying chemical kinetics is demonstrated. Like conventional RCMs, the RCEM quickly compresses a fuel/air mixture by pushing a piston into a cylinder; the resulting high temperatures and pressures initiate chemical reactions. In addition, the machine can rapidly expand the compressed gas in a controlled way by pulling the piston outwards again. This freezes chemical activity after a pre-defined reaction duration, and therefore allows a convenient probe samp…
The temperature dependence of the thermal rate constant for the reaction Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl is calculated using a Gaussian Process machine learning (ML) approach to train on and predict thermal rate constants over a large temperature range. Following procedures developed in two previous reports, we use a training dataset of approximately 40 reaction/potential surface combinations, each of which is used to calculate the corresponding data base of rate constant at approximately eight temperatures. For the current application, we train on the entire dataset and then pre…
ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning
BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD – 柏林学习与数据基础研究所）；TU Berlin（柏林工业大学）；Department of Artificial Intelligence, Korea University（高丽大学人工智能系）；MPI for Informatics（马克斯·普朗克信息学研究所）
An idea for evaluating transition probabilities in chemical reaction systems is proposed, which is efficient for repeated calculations with various rate constants. The idea is based on duality relations; instead of direct time-evolutions of the original reaction system, the dual process is dealt with. Usually, if one changes rate constants of the original reaction system, the direct time-evolutions should be performed again, using the new rate constants. On the other hands, only one solution of an extended dual process can be re-used to calculate the transition probabilities for various rate c…
Canonical instanton theory is a widespread approach to describe the dynamics of chemical reactions in low temperature environments when tunneling effects become dominant. It is a semiclassical theory which requires locating classical periodic orbits on the upside-down potential energy surface, so-called instantons, and the computation of second order quantum corrections. The calculation of these corrections usually involves a matrix diagonalization. In this paper we present an alternative approach, which requires to solve only linear systems of equations involving sparse matrices. Furthermore,…
A major challenge in network science is to determine parameters governing complex network dynamics from experimental observations and theoretical models. In complex chemical reaction networks, for example, such as those describing processes in internal combustion engines and power generators, rate constant estimates vary significantly across studies despite substantial experimental efforts. Here, we examine the possibility that variability in measured constants can be largely attributed to the impact of missing network information on parameter estimation. Through the numerical simulation of me…
Clarifying NH2 + O(3P) Reaction Dynamics: A Full-Dimensional MRCI, Machine-Learned PES Unravels High-Temperature Kinetics
MIIT Key Laboratory of Semiconductor Microstructure and Quantum Sensing, Department of Applied Physics, School of Physics, Nanjing University of Science and Technology（南京理工大学应用物理学院）
Reaction rates of chemical reactions under nonequilibrium conditions can be determined through the construction of the normally hyperbolic invariant manifold (NHIM) [and moving dividing surface (DS)] associated with the transition state trajectory. Here, we extend our recent methods by constructing points on the NHIM accurately even for multidimensional cases. We also advance the implementation of machine learning approaches to construct smooth versions of the NHIM from a known high-accuracy set of its points. That is, we expand on our earlier use of neural nets, and introduce the use of Gauss…
Atomic oxygen in its first excited singlet state, O(1 D), is an important species in the photochemistry of several planetary atmospheres and has been predicted to be a potentially important reactive species on interstellar ices. Here, we report the results of a kinetic study of the reactions of O(1 D) with methanol, CH3OH, and acetonitrile, CH3CN, over the 50-296 K temperature range. A continuous supersonic flow reactor was used to attain these low temperatures coupled with pulsed laser photolysis and pulsed laser induced fluorescence to generate and monitor O(1 D) atoms respectively. Secondar…
OH radicals play a key role as an intermediate in the water formation chemistry of the interstellar medium. For example the reaction of OH radicals with H$_2$ molecules is among the final steps in the astrochemical reaction network starting from O, O$_2$, and O$_3$. Experimentally it was shown that even at 10 K this reaction occurs on ice surfaces. As the reaction has a high activation energy only atom tunneling can explain such experimental findings. In this study we calculated reaction rate constants for the title reaction on a water-ice I$_h$ surface. To our knowledge, low-temperature rate …
Ring polymer molecular dynamics (RPMD) is an accurate method for calculating thermal chemical reaction rates. It has recently been discovered that low-temperature calculations are strongly affected by the simulation parameters. Here, for the thermally activated reaction DH + H -> D + H2, we calculate the RPMD rate constants at T = 50, 100, and 300 K and demonstrate that for T >= 100 K the standard input parameters yield accurate results, but at low temperatures (e.g., 50 K) one must increase the asymptotic distance and force constant, and decrease the umbrella integration step.
Under temperature oscillation, cyclic molecular machines such as catalysts and enzymes could harness energy from the oscillatory bath and use it to drive other processes. Using a novel geometrical approach, under fast temperature oscillation, we derive a general design principle for obtaining the optimal catalytic energy landscape that can harness energy from a temperature-oscillatory bath and use it to invert a spontaneous reaction. By driving the reaction against the spontaneous direction, the catalysts convert low free energy product molecules to high free energy reactant molecules. The des…
Computing accurate rate constants for catalytic events occurring at the surface of a given material represents a challenging task with multiple potential applications in chemistry. To address this question, we propose an approach based on a combination of the rare event sampling method called Adaptive Multilevel Splitting (AMS) and ab initio molecular dynamics (AIMD). The AMS method requires a one dimensional reaction coordinate to index the progress of the transition. Identifying a good reaction coordinate is difficult, especially for high dimensional problems such a those encountered in cata…
Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep …
Radical recombination has been proposed to lead to the formation of complex organic molecules (COMs) in CO-rich ices in the early stages of star formation. These COMs can then undergo hydrogen addition and abstraction reactions leading to a higher or lower degree of saturation. Here, we have studied 14 hydrogen transfer reactions for the molecules glyoxal, glycoaldehyde, ethylene glycol, and methylformate and an additional three reactions where \ce{CH_nO} fragments are involved. Over-the-barrier reactions are possible only if tunneling is invoked in the description at low temperature. Therefor…
Del Norte High School（德尔诺特高中）；Brandeis University（布兰戴斯大学）；Lexington High School（列克星敦高中）
Stereoselective reactions (both chemical and enzymatic reactions) have been essential for origin of life, evolution, human biology and medicine. Since late 1960s, there have been numerous successes in the exciting new frontier of asymmetric catalysis. However, most industrial and academic asymmetric catalysis nowadays do follow the trial-and-error model, since the energetic difference for success or failure in asymmetric catalysis is incredibly small. Our current understanding about stereoselective reactions is mostly qualitative that stereoselectivity arises from differences in steric effects…
Prediction of CO2 reduction reaction intermediates and products on transition metal-doped r-GeSe monolayers:A combined DFT and machine learning approach
We present a combined experimental and theoretical study focussing on the quantum tunneling of atoms in the reaction between CH4 and OH. The importance of this reaction pathway is derived by investigating isotope substituted analogs. Quantitative reaction rates needed for astrochemical models at low temperature are currently unavailable both in the solid state and in the gas phase. Here, we study tunneling effects upon hydrogen abstraction in CH4 + OH by focusing on two reactions: CH4 + OD -> CH3 + HDO and CD4 + OH -> CD3 + HDO. The experimental study shows that the solid-state reaction rate R…
