Predicting Materials Properties with Little Data Using Shotgun Transfer Learning.

ABSTRACT: There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called "transfer learning" has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, m

SUBMITTER: Yamada H 

PROVIDER: S-EPMC6813555 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature