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Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models through Virtual Particles

Journal of Chemical Theory and Computation(2023)10.1021/acs.jctc.2c01183Source: DataRank Database

Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models through Virtual Particles is a research paper published in Journal of Chemical Theory and Computation (2023). On theSindex it has a DataRank of 0. It has been cited 31 times.

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0DataRank · unranked

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Open Access31 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)IDs (PubMed, PMC)

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Accessible (1/2)

Open Access

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Reusable (0/3)

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Authors (4)

Timothy D. Loose,Aleksander E. P. Durumeric ORCID,Gregory A. Voth ORCID,Patrick G. Sahrmann ORCID

Live enrichment

PubMed:36802592 PMC:PMC10373655

Division of Chemistry

Grant: DGE-1746045

Division of Chemistry

Grant: CHE-2102677

National Institute of General Medical Sciences

Grant: R01GM063796