A data-driven approach to approximate the correlation functions in cluster variation method

Perspective on integrating machine learning into computational chemistry and materials science

(2021) Julia Westermayr et al.   JOURNAL OF CHEMICAL PHYSICS

Editorial: Machine Learning and Data Mining in Materials Science

(2020) Norbert Huber et al.   Frontiers in Materials

Towards data-driven next-generation transmission electron microscopy

(2020) Steven R. Spurgeon et al.   NATURE MATERIALS

Polynomial functions for configurational correlation functions in Gibbs energies of solid solutions using cluster variation method

(2020) Rajendra Prasad Gorrey et al.   COMPUTATIONAL MATERIALS SCIENCE

Integration of machine learning with phase field method to model the electromigration induced Cu6Sn5 IMC growth at anode side Cu/Sn interface

(2020) Anil Kunwar et al.   JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY

Machine learning based a priori prediction on powder samples of sintering-driven abnormal grain growth

(2020) Abhimanyu Swaroop et al.   COMPUTATIONAL MATERIALS SCIENCE

An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions

(2019) Sehmus Fidan et al.   Advances in Materials Science and Engineering

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Unsupervised learning for local structure detection in colloidal systems

(2019) Emanuele Boattini et al.   JOURNAL OF CHEMICAL PHYSICS

Evaluation of the genetic algorithm performance for the optimization of the grand potential in the cluster variation method

(2018) Y. Tamerabet et al.   CALPHAD-COMPUTER COUPLING OF PHASE DIAGRAMS AND THERMOCHEMISTRY

Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain

(2018) Mohsen Botlani et al.   JOURNAL OF CHEMICAL PHYSICS

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Unsupervised machine learning for detection of phase transitions in off-lattice systems. II. Applications

(2018) R. B. Jadrich et al.   JOURNAL OF CHEMICAL PHYSICS

Machine-learning the configurational energy of multicomponent crystalline solids

(2018) Anirudh Raju Natarajan et al.   npj Computational Materials

Approximate solutions to the cluster variation free energies by the variable basis cluster expansion

(2016) J.M. Sanchez et al.   COMPUTATIONAL MATERIALS SCIENCE

Big Data of Materials Science: Critical Role of the Descriptor

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

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

(2013) James E. Saal et al.   JOM

Modeling of thermotransport phenomenon in metal alloys using artificial neural networks

(2012) Seshasai Srinivasan et al.   APPLIED MATHEMATICAL MODELLING

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

Thermodynamics of dilute binary solid solutions using the cluster variation method

(2012) Bandikatla N. Sarma et al.   International Journal of Materials Research

A high-throughput infrastructure for density functional theory calculations

(2011) Anubhav Jain et al.   COMPUTATIONAL MATERIALS SCIENCE