Related references
Note: Only part of the references are listed.Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods
Fleur Legrain et al.
JOURNAL OF PHYSICAL CHEMISTRY B (2018)
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Tian Xie et al.
PHYSICAL REVIEW LETTERS (2018)
Machine learning for the structure-energy-property landscapes of molecular crystals
Felix Musil et al.
CHEMICAL SCIENCE (2018)
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu et al.
CHEMICAL SCIENCE (2018)
Deep learning for computational chemistry
Garrett B. Goh et al.
JOURNAL OF COMPUTATIONAL CHEMISTRY (2017)
Mastering the game of Go without human knowledge
David Silver et al.
NATURE (2017)
Machine learning phases of matter
Juan Carrasquilla et al.
NATURE PHYSICS (2017)
Machine learning molecular dynamics for the simulation of infrared spectra
Michael Gastegger et al.
CHEMICAL SCIENCE (2017)
Classification of crystal structure using a convolutional neural network
Woon Bae Park et al.
IUCRJ (2017)
Accelerated discovery of new magnets in the Heusler alloy family
Stefano Sanvito et al.
SCIENCE ADVANCES (2017)
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad et al.
NPJ COMPUTATIONAL MATERIALS (2017)
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
Anton O. Oliynyk et al.
CHEMISTRY OF MATERIALS (2016)
Mastering the game of Go with deep neural networks and tree search
David Silver et al.
NATURE (2016)
Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia et al.
NATURE (2016)
Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals
Felix A. Faber et al.
PHYSICAL REVIEW LETTERS (2016)
Reproducibility in density functional theory calculations of solids
Kurt Lejaeghere et al.
SCIENCE (2016)
The thermodynamic scale of inorganic crystalline metastability
Wenhao Sun et al.
SCIENCE ADVANCES (2016)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward et al.
NPJ COMPUTATIONAL MATERIALS (2016)
Big Data of Materials Science: Critical Role of the Descriptor
Luca M. Ghiringhelli et al.
PHYSICAL REVIEW LETTERS (2015)
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Scott Kirklin et al.
npj Computational Materials (2015)
New Mn2-based Heusler Compounds
Guido Kreiner et al.
ZEITSCHRIFT FUR ANORGANISCHE UND ALLGEMEINE CHEMIE (2014)
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain et al.
APL MATERIALS (2013)
Sorting Stable versus Unstable Hypothetical Compounds: The Case of Multi-Functional ABX Half-Heusler Filled Tetrahedral Structures
Xiuwen Zhang et al.
ADVANCED FUNCTIONAL MATERIALS (2012)
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
Stefano Curtarolo et al.
COMPUTATIONAL MATERIALS SCIENCE (2012)
Recent Advances in Nanostructured Thermoelectric Half-Heusler Compounds
Wenjie Xie et al.
NANOMATERIALS (2012)
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid
Johannes Hachmann et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2011)
Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
Geoffroy Hautier et al.
CHEMISTRY OF MATERIALS (2010)
VESTA:: a three-dimensional visualization system for electronic and structural analysis
Koichi Momma et al.
JOURNAL OF APPLIED CRYSTALLOGRAPHY (2008)
Share Paper
