Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data

Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

(2019) Jordan Hoffmann et al.   Science Advances

Learning atoms for materials discovery

(2018) Quan Zhou et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

(2018) Fang Ren et al.   Science Advances

Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations

(2018) Yuping He et al.   Journal of Physical Chemistry Letters

Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials

(2018) Austin D. Sendek et al.   CHEMISTRY OF MATERIALS

Revealing the Spectrum of Unknown Layered Materials with Superhuman Predictive Abilities

(2018) Gowoon Cheon et al.   Journal of Physical Chemistry Letters

Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning

(2018) Paul Z. Hanakata et al.   PHYSICAL REVIEW LETTERS

Active learning for accelerated design of layered materials

(2018) Lindsay Bassman et al.   npj Computational Materials

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

(2017) Austin D. Sendek et al.   Energy & Environmental Science

Representations in neural network based empirical potentials

(2017) Ekin D. Cubuk et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2016) Logan Ward et al.   npj Computational Materials

Attainable Gravimetric and Volumetric Energy Density of Li–S and Li Ion Battery Cells with Solid Separator-Protected Li Metal Anodes

(2015) Bryan D. McCloskey   Journal of Physical Chemistry Letters

Deep learning

(2015) Yann LeCun et al.   NATURE

Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization

(2015) Atsuto Seko et al.   PHYSICAL REVIEW LETTERS

Big Data of Materials Science: Critical Role of the Descriptor

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

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

(2014) Atsuto Seko et al.   PHYSICAL REVIEW B

Novel stable compounds in the Mg–O system under high pressure

(2013) Qiang Zhu et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms

(2013) Koji Fujimura et al.   Advanced Energy Materials

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide

(2012) Nongnuch Artrith et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Data mining for materials: Computational experiments withABcompounds

(2012) Yousef Saad et al.   PHYSICAL REVIEW B

LIBSVM

(2012) Chih-Chung Chang et al.   ACM Transactions on Intelligent Systems and Technology

Two semi-empirical approaches for the prediction of oxide ionic conductivities in ABO3 perovskites

(2009) Liu Xu et al.   COMPUTATIONAL MATERIALS SCIENCE

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING