Machine learning of molecular properties: Locality and active learning

Journal Article · 18 April 2018 · Journal of Chemical Physics

DOI: https://doi.org/10.1063/1.5005095 · OSTI ID:1433745

Gubaev, Konstantin ; Podryabinkin, Evgeny V. ; Shapeev, Alexander V.

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

• Sponsoring Organization: USDOE
• OSTI ID: 1433745
• Journal Information: Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 24 Vol. 148; ISSN 0021-9606
• Publisher: American Institute of PhysicsCopyright Statement
• Country of Publication: United States
• Language: English