article Machine Learning Potentials

Machine Learning Interatomic Potentials and Long-Range Physics

Dylan M. Anstine, Olexandr Isayev

J. Phys. Chem. A Vol. 127(11) pp. 2417–2431 2023 143 citations
View on Publisher

Highlight

Machine Learning Interatomic Potentials and Long-Range Physics.

Keywords

machine learning potentials long-range interactions physics

Cite This Paper

@article{Anstine2023mlpotentials,
  author = {Anstine, Dylan M. and Isayev, Olexandr},
  title = {Machine Learning Interatomic Potentials and Long-Range Physics},
  year = {2023},
  journal = {J. Phys. Chem. A},
  volume = {127},
  number = {11},
  pages = {2417--2431},
  doi = {10.1021/acs.jpca.2c06778},
  keywords = {machine learning potentials, long-range interactions, physics},
  researchAreas = {ml-potentials},
  researchArea = {ml-potentials},
  highlight = {Machine Learning Interatomic Potentials and Long-Range Physics.},
  citations = {143}
}

Related Research Areas

Machine Learning Potentials

Related Publications

2024
cited 85

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Zhang S., Makoś M. Z., Jadrich R. B., Kraka E., Barros K., Nebgen B. T., Tretiak S., Isayev O., Lubbers N., Messerly R. A., et al.

Nature Chemistry 16 , 727–734

Machine Learning Potentials Experiment Automation

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.

DOI
View full details
2022
cited 42

Learning molecular potentials with neural networks

Gokcan H., Isayev O.

WIREs Comput. Mol. Sci. 12 , e1564

Machine Learning Potentials Quantum Chemistry

AbstractThe potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry.

DOI
2021
cited 115

Artificial intelligence-enhanced quantum chemical method with broad applicability

Zheng P., Zubatyuk R., Wu W., Isayev O., Dral P. O.

Nature Communications 12

Quantum Chemistry Machine Learning Potentials

Abstract High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level.

DOI
2021
cited 94

Teaching a neural network to attach and detach electrons from molecules

Zubatyuk R., Smith J. S., Nebgen B. T., Tretiak S., Isayev O.

Nature Communications 12

Machine Learning Potentials Reactions & Reactivity Drug Discovery Quantum Chemistry

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.

DOI
2020
cited 259

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

Gao X., Ramezanghorbani F., Isayev O., Smith J. S., Roitberg A. E.

Journal of Chemical Information and Modeling 60 , 3408–3415

Machine Learning Potentials

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials.

DOI
← Back to all publications