Equation of State of Fluid Methane from First Principles with Machine Learning Potentials-Reference-Cited by-同舟云学术

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Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

Published:2019-02-22 Issue:4 Volume:15 Page:2574-2586
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Veit Max¹^ORCID,Jain Sandeep Kumar²,Bonakala Satyanarayana²,Rudra Indranil²,Hohl Detlef³,Csányi Gábor¹

Affiliation:

1. Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom

2. Shell India Markets Pvt. Ltd., Bengaluru 562149, Karnataka, India

3. Shell Global Solutions International BV, Grasweg 31, 1031 HW Amsterdam, The Netherlands

Funder

Shell

Engineering and Physical Sciences Research Council

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.8b01242

Reference133 articles.

1. Mapping and classifying molecules from a high-throughput structural database

2. Machine learning unifies the modeling of materials and molecules

3. Alchemical and structural distribution based representation for universal quantum machine learning

4. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

5. Machine learning of molecular properties: Locality and active learning

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2. Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices;The Journal of Physical Chemistry C;2024-03-20

3. High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane;Journal of Chemical Theory and Computation;2023-10-30

4. Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent;npj Computational Materials;2023-08-17

5. FluxNet: A physics-informed learning-based Riemann solver for transcritical flows with non-ideal thermodynamics;Computer Methods in Applied Mechanics and Engineering;2023-06

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