Accurate force field for molybdenum by machine learning large materials data

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

(2017) Nicholas J. Browning et al.   Journal of Physical Chemistry Letters

Machine learning: New tool in the box

(2017) Lenka Zdeborová   Nature Physics

Machine learning phases of matter

(2017) Juan Carrasquilla et al.   Nature Physics

Solving the quantum many-body problem with artificial neural networks

(2017) Giuseppe Carleo et al.   SCIENCE

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

(2017) Zachary W. Ulissi et al.   Nature Communications

Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12

(2017) Chi Chen et al.   Scientific Reports

Computational study of metallic dopant segregation and embrittlement at molybdenum grain boundaries

(2016) Richard Tran et al.   ACTA MATERIALIA

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

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

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Surface energies of elemental crystals

(2016) Richard Tran et al.   Scientific Data

The ReaxFF reactive force-field: development, applications and future directions

(2016) Thomas P Senftle et al.   npj Computational Materials

FireWorks: a dynamic workflow system designed for high-throughput applications

(2015) Anubhav Jain et al.   CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

A molecular dynamics study of oxygen ion diffusion in A-site ordered perovskite PrBaCo2O5.5: data mining the oxygen trajectories

(2015) Chi Chen et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Charting the complete elastic properties of inorganic crystalline compounds

(2015) Maarten de Jong et al.   Scientific Data

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

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

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

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

(2013) Anubhav Jain et al.   APL Materials

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

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

Ab initiobased empirical potential used to study the mechanical properties of molybdenum

(2012) Hyoungki Park et al.   PHYSICAL REVIEW B

Comment on “Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning”

(2012) Jonathan E. Moussa   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics

(2011) Roberto Olivares-Amaya et al.   Energy & Environmental Science

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Recent advances in differential evolution: a survey and experimental analysis

(2009) Ferrante Neri et al.   ARTIFICIAL INTELLIGENCE REVIEW

First-principles calculations of the ferroelastic transition between rutile-type andCaCl2-typeSiO2at high pressures

(2008) Atsushi Togo et al.   PHYSICAL REVIEW B