Related references
Note: Only part of the references are listed.Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds
Atsuto Seko et al.
PHYSICAL REVIEW MATERIALS (2018)
Representation of compounds for machine-learning prediction of physical properties
Atsuto Seko et al.
PHYSICAL REVIEW B (2017)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward et al.
NPJ COMPUTATIONAL MATERIALS (2016)
Combinatorial screening for new materials in unconstrained composition space with machine learning
B. Meredig et al.
PHYSICAL REVIEW B (2014)
Tetragonal Li10GeP2S12 and Li7GePS8 - exploring the Li ion dynamics in LGPS Li electrolytes
Alexander Kuhn et al.
ENERGY & ENVIRONMENTAL SCIENCE (2013)
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
James E. Saal et al.
JOM (2013)
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain et al.
APL MATERIALS (2013)
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
Stefano Curtarolo et al.
COMPUTATIONAL MATERIALS SCIENCE (2012)
Data Mined Ionic Substitutions for the Discovery of New Compounds
Geoffroy Hautier et al.
INORGANIC CHEMISTRY (2011)
Inorganic Materials Database for Exploring the Nature of Material
Yibin Xu et al.
JAPANESE JOURNAL OF APPLIED PHYSICS (2011)
A lithium superionic conductor
Noriaki Kamaya et al.
NATURE MATERIALS (2011)
Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
Geoffroy Hautier et al.
CHEMISTRY OF MATERIALS (2010)
Topology of the pyroxenes as a function of temperature, pressure, and composition as determined from the procrystal electron density
RT Downs
AMERICAN MINERALOGIST (2003)
Share Paper
