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Machine learning unifies the modeling of materials and molecules
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian sta...
Tallennettuna:
| Julkaisussa: | Sci Adv |
|---|---|
| Päätekijät: | , , , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
American Association for the Advancement of Science
2017
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5729016/ https://ncbi.nlm.nih.gov/pubmed/29242828 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1126/sciadv.1701816 |
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