Automated reaction kinetics of gas-phase organic species over multiwell potential energy surfaces
Automation of rate-coefficient calculations for gas-phase organic species became possible
in recent years and has transformed how we explore these complicated systems …
in recent years and has transformed how we explore these complicated systems …
Analytical ab initio hessian from a deep learning potential for transition state optimization
Identifying transition states—saddle points on the potential energy surface connecting
reactant and product minima—is central to predicting kinetic barriers and understanding …
reactant and product minima—is central to predicting kinetic barriers and understanding …
Enhancing GPU-acceleration in the Python-based Simulations of Chemistry Framework
We describe our contribution as industrial stakeholders to the existing open-source
GPU4PySCF project (https://github. com/pyscf/gpu4pyscf), a GPU-accelerated Python …
GPU4PySCF project (https://github. com/pyscf/gpu4pyscf), a GPU-accelerated Python …
Evaluation of rate coefficients in the gas phase using machine-learned potentials
We assess the capability of machine-learned potentials to compute rate coefficients by
training a neural network (NN) model and applying it to describe the chemical landscape on …
training a neural network (NN) model and applying it to describe the chemical landscape on …
Sella, an open-source automation-friendly molecular saddle point optimizer
We present a new algorithm for the optimization of molecular structures to saddle points on
the potential energy surface using a redundant internal coordinate system. This algorithm …
the potential energy surface using a redundant internal coordinate system. This algorithm …
Pynta─ An Automated Workflow for Calculation of Surface and Gas–Surface Kinetics
Many important industrial processes rely on heterogeneous catalytic systems. However,
given all possible catalysts and conditions of interest, it is impractical to optimize most …
given all possible catalysts and conditions of interest, it is impractical to optimize most …
Geometry optimization: a comparison of different open-source geometry optimizers
Based on a series of energy minimizations with starting structures obtained from the Baker
test set of 30 organic molecules, a comparison is made between various open-source …
test set of 30 organic molecules, a comparison is made between various open-source …
Deep Learning of ab initio Hessians for Transition State Optimization
Identifying transition states--saddle points on the potential energy surface connecting
reactant and product minima--is central to predicting kinetic barriers and understanding …
reactant and product minima--is central to predicting kinetic barriers and understanding …
Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows
Microkinetic models for catalytic systems require estimation of many thermodynamic and
kinetic parameters that can be calculated for isolated species and transition states using ab …
kinetic parameters that can be calculated for isolated species and transition states using ab …
Force training neural network potential energy surface models
Abstract Machine learned chemical potentials have shown great promise as alternatives to
conventional computational chemistry methods to represent the potential energy of a given …
conventional computational chemistry methods to represent the potential energy of a given …