Journal
SCIENTIFIC DATA
Volume 4, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/sdata.2017.193
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Funding
- University of Florida
- NIH [GM110077]
- DOD-ONR [N00014-16-1-2311]
- Eshelman Institute for Innovation award
- National Science Foundation (NSF)
- National Science Foundation [DMR110088, ACI-1053575]
- U.S. Department of Energy through the LANL/LDRD Program
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One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community.
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