Research Topic
force field
10 publications exploring this topic
2021
Teaching a neural network to attach and detach electrons from molecules
Nature Communications, 12 (2021)
Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.
Teaching a Neural Network to Attach and Detach Electrons from Molecules
(2021)
Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.
2020
Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
(2020)
Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles.
Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials
(2020)
Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials.
Teaching a Neural Network to Attach and Detach Electrons from Molecules
(2020)
Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.
2019
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Nature Communications, 10 (2019)
Abstract Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset.
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
(2019)
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
(2019)
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.
2018
Discovering a Transferable Charge Assignment Model Using Machine Learning
(2018)
Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics.
2017
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Chemical Science, 8, 3192–3203 (2017)
We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT calculated energies can learn an accurate and transferable atomistic potential for organic molecules containing H, C, N, and O atoms.