Machine learning unifies the modeling of materials and molecules.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 29242828.
- Also identified by DOI 10.1126/sciadv.1701816 and PMC identifier 5729016.
- Licence recorded as CC BY-NC.
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Abstract
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 statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules.