Learning physical descriptors for materials science by compressed sensing

Abstract

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.

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• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
• Representation of compounds for machine-learning prediction of physical properties
• Symbolic Regression in Materials Science
• Advances of Machine Learning in Materials Science: Ideas and Techniques
• Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
• Machine Learning Study of the Magnetic Ordering in 2D Materials
• Examination of computed aluminum grain boundary structures and interface energies that span the 5D space of crystallographic character
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• Sparse Modeling in Quantum Many-Body Problems
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• Advancing descriptor search in materials science: feature engineering and selection strategies
• On the Optimality of Backward Regression: Sparse Recovery and Subset Selection
• Topological descriptors for the electron density of inorganic solids
• TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions