Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration-Reference-Cited by-同舟云学术

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Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration

Published:2024-08-16 Issue: Volume: Page:
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Gubler Moritz¹^ORCID,Finkler Jonas A.¹,Schäfer Moritz R.²³,Behler Jörg²³^ORCID,Goedecker Stefan¹

Affiliation:

1. Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland

2. Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany

3. Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780 Bochum, Germany

Funder

Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung

Deutsche Forschungsgemeinschaft

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00334

Reference47 articles.

1. Perspective: Machine learning potentials for atomistic simulations

2. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

3. Machine Learning for Molecular Simulation

4. Machine Learning Force Fields

5. Machine learning potentials for extended systems: a perspective

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1. Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis;Precision Chemistry;2024-09-11

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