Machine learning for chemical reactions
M Meuwly - Chemical Reviews, 2021 - ACS Publications
Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …
present contribution discusses applications ranging from small molecule reaction dynamics …
Neural network potential energy surfaces for small molecules and reactions
S Manzhos, T Carrington Jr - Chemical Reviews, 2020 - ACS Publications
We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …
interatomic potentials from discrete samples (such as ab initio energies) for applications in …
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
D Schwalbe-Koda, AR Tan… - Nature …, 2021 - nature.com
Neural network (NN) interatomic potentials provide fast prediction of potential energy
surfaces, closely matching the accuracy of the electronic structure methods used to produce …
surfaces, closely matching the accuracy of the electronic structure methods used to produce …
Searching configurations in uncertainty space: Active learning of high-dimensional neural network reactive potentials
Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic
simulations with ab initio accuracy. While constructing NN PESs, their training data points …
simulations with ab initio accuracy. While constructing NN PESs, their training data points …
Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: efficiency, representability, and …
Abstract Machine learning techniques have been widely applied in many fields of chemistry,
physics, biology, and materials science. One of the most fruitful applications is machine …
physics, biology, and materials science. One of the most fruitful applications is machine …
Rovibrational-Specific QCT and Master Equation Study on N2(X1Σg+) + O(3P) and NO(X2Π) + N(4S) Systems in High-Energy Collisions
This work presents a detailed investigation of the energy-transfer and dissociation
mechanisms in N2 (X1Σg+)+ O (3P) and NO (X2Π)+ N (4S) systems using rovibrational …
mechanisms in N2 (X1Σg+)+ O (3P) and NO (X2Π)+ N (4S) systems using rovibrational …
Data-Inspired and Physics-Driven Model Reduction for Dissociation: Application to the O2 + O System
This work presents an in-depth discussion on the nonequilibrium dissociation of O2
molecules colliding with O atoms, combining quasi-classical trajectory calculations, master …
molecules colliding with O atoms, combining quasi-classical trajectory calculations, master …
First-principles predictions for shear viscosity of air components at high temperature
P Valentini, AM Verhoff, MS Grover… - Physical Chemistry …, 2023 - pubs.rsc.org
The direct molecular simulation (DMS) method is used to obtain shear viscosity data for non-
reacting air and its components by simulating isothermal, plane Poiseuille subsonic flows …
reacting air and its components by simulating isothermal, plane Poiseuille subsonic flows …
Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory
D Kuntz, AK Wilson - Pure and Applied Chemistry, 2022 - degruyter.com
Abstract Machine learning and artificial intelligence are increasingly gaining in prominence
through image analysis, language processing, and automation, to name a few applications …
through image analysis, language processing, and automation, to name a few applications …
Plato: a high-fidelity tool for multi-component plasmas
View Video Presentation: https://doi. org/10.2514/6.2023-3490. vid This work presents plato
(PLAsmas in Thermodynamic nOn-equilibrium), a high-fidelity library for the …
(PLAsmas in Thermodynamic nOn-equilibrium), a high-fidelity library for the …