Deep dive into machine learning density functional theory for materials science and chemistry

Density functional theory in materials science

Materials science is a highly interdisciplinary field. It is devoted to the understanding of the relationship between (a) fundamental physical and chemical properties governing processes at the atomistic scale with (b) typically macroscopic properties required of materials in engineering applications. For many materials, this relationship is not only determined by chemical composition, but stro...

Theory-Guided Machine Learning in Materials Science

Materials scientists are increasingly adopting the use of machine learning tools to discover hidden trends in data and make predictions. Applying concepts from data science without foreknowledge of their limitations and the unique qualities of materials data, however, could lead to errant conclusions. The differences that exist between various kinds of experimental and calculated data require c...

Advanced Density Functional Theory Methods for Materials Science

In this work we chiefly deal with two broad classes of problems in computational materials science, determining the doping mechanism in a semiconductor and developing an extreme condition equation of state. While solving certain aspects of these questions is well-trodden ground, both require extending the reach of existing methods to fully answer them. Here we choose to build upon the framework...

Trends in Computational Materials Science Based on Density Functional Theory

This review deals with computational treatments of subatomic levels of matter based on density functional theory (DFT), and tries to identify several current trends, which are largely consequences of the ever-increasing power of computers, which has substantially extended the performance of conventional DFT beyond its original scope. This review mainly focuses on the conceptual outline, rather ...

Machine Learning in Materials Science: Recent Progress and Emerging Applications