Machine learning prediction of self-diffusion in Lennard-Jones fluids-Reference-Cited by-同舟云学术

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Machine learning prediction of self-diffusion in Lennard-Jones fluids

Published:2020-07-21 Issue:3 Volume:153 Page:034102
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Allers Joshua P.¹^ORCID,Harvey Jacob A.²^ORCID,Garzon Fernando H.³⁴^ORCID,Alam Todd M.¹^ORCID

Affiliation:

1. Department of Organic Materials Science, Albuquerque, New Mexico 87185, USA

2. Department of Geochemistry, Albuquerque, New Mexico 87185, USA

3. Department of Power Sources Research and Development, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA

4. Center of Micro-Engineered Materials, University of New Mexico, Albuquerque, New Mexico 87106, USA

Funder

Sandia National Laboratories

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/5.0011512

Reference81 articles.

1. New frontiers for the materials genome initiative

2. The Electrolyte Genome project: A big data approach in battery materials discovery

3. Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening

4. Machine Learning To Predict Standard Enthalpy of Formation of Hydrocarbons

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