sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Title
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
Authors
Keywords
Machine learning potential, Machine learning force field, Ab initio molecular dynamics, Path integral molecular dynamics, Coupled cluster calculations, Molecular property prediction, Quantum chemistry, Gradient domain machine learning
Journal
COMPUTER PHYSICS COMMUNICATIONS
Volume -, Issue -, Pages -
Publisher
Elsevier BV
Online
2019-03-01
DOI
10.1016/j.cpc.2019.02.007

Ask authors/readers for more resources

Reprint

Contact the author

Find Funding. Review Successful Grants.

Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.

Explore

Find the ideal target journal for your manuscript

Explore over 38,000 international journals covering a vast array of academic fields.

Search