Predicting the Curie temperature of ferromagnets using machine learning

Compositional optimization of hard-magnetic phases with machine-learning models

(2018) Johannes J. Möller et al.   ACTA MATERIALIA

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Screening of rare-earth-lean intermetallic 1-11 and 1-11-X compounds of YNi 9 In 2 -type for hard-magnetic applications

(2018) Wolfgang Körner et al.   SCRIPTA MATERIALIA

Machine learning modeling of superconducting critical temperature

(2018) Valentin Stanev et al.   npj Computational Materials

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Accelerated discovery of new magnets in the Heusler alloy family

(2017) Stefano Sanvito et al.   Science Advances

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

(2016) Anubhav Jain et al.   JOURNAL OF MATERIALS RESEARCH

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Enthalpies of formation of selected Fe2YZ Heusler compounds

(2015) Ming Yin et al.   INTERMETALLICS

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Big Data of Materials Science: Critical Role of the Descriptor

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

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Inorganic Materials Database for Exploring the Nature of Material

(2013) Yibin Xu et al.   JAPANESE JOURNAL OF APPLIED PHYSICS

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

(2012) Shyue Ping Ong et al.   COMPUTATIONAL MATERIALS SCIENCE

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Simple rules for the understanding of Heusler compounds

(2011) Tanja Graf et al.   PROGRESS IN SOLID STATE CHEMISTRY

Energies and Lifetimes of Magnons in Complex Ferromagnets: A First-Principle Study of Heusler Alloys

(2009) Paweł Buczek et al.   PHYSICAL REVIEW LETTERS