Theory-Guided Machine Learning in Materials Science

Material descriptors for predicting thermoelectric performance

(2015) Jun Yan et al.   Energy & Environmental Science

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

Identification of phases, symmetries and defects through local crystallography

(2015) Alex Belianinov et al.   Nature Communications

Coupling and electrical control of structural, orbital and magnetic orders in perovskites

(2015) Julien Varignon et al.   Scientific Reports

Classification ofABO3perovskite solids: a machine learning study

(2015) G. Pilania et al.   Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials

Research Update: Towards designed functionalities in oxide-based electronic materials

(2015) James M. Rondinelli et al.   APL Materials

Proxies from Ab Initio Calculations for Screening Efficient Ce3+ Phosphor Hosts

(2013) Jakoah Brgoch et al.   Journal of Physical Chemistry C

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Compressive sensing as a paradigm for building physics models

(2013) Lance J. Nelson et al.   PHYSICAL REVIEW B

Identifying the 'inorganic gene' for high-temperature piezoelectric perovskites through statistical learning

(2011) P. V. Balachandran et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Mode crystallography of distorted structures

(2010) J. M. Perez-Mato et al.   ACTA CRYSTALLOGRAPHICA SECTION A

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

What is principal component analysis?

(2008) Markus Ringnér   NATURE BIOTECHNOLOGY