MDTS: automatic complex materials design using Monte Carlo tree search

Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn

(2017) Tarak K. Patra et al.   ACS Combinatorial Science

Acceleration of stable interface structure searching using a kriging approach

(2016) Shin Kiyohara et al.   JAPANESE JOURNAL OF APPLIED PHYSICS

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Supercell program: a combinatorial structure-generation approach for the local-level modeling of atomic substitutions and partial occupancies in crystals

(2016) Kirill Okhotnikov et al.   Journal of Cheminformatics

Adaptive Strategies for Materials Design using Uncertainties

(2016) Prasanna V. Balachandran et al.   Scientific Reports

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

(2015) Atsuto Seko et al.   PHYSICAL REVIEW LETTERS

Probing and tuning inelastic phonon conductance across finite-thickness interface

(2014) Takuru Murakami et al.   Applied Physics Express

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

Importance of local force fields on lattice thermal conductivity reduction in PbTe1−xSexalloys

(2013) Takuru Murakami et al.   EPL

A Survey of Monte Carlo Tree Search Methods

(2012) Cameron B. Browne et al.   IEEE Transactions on Computational Intelligence and AI in Games

Genetic algorithms: A universal tool for solving computational tasks in Materials Science

(2008) Wojciech Paszkowicz et al.   COMPUTATIONAL MATERIALS SCIENCE