Entropy-based active learning of graph neural network surrogate models for materials properties

Transfer learning for materials informatics using crystal graph convolutional neural network

(2021) Joohwi Lee et al.   COMPUTATIONAL MATERIALS SCIENCE

Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites

(2021) Daniil Bash et al.   ADVANCED FUNCTIONAL MATERIALS

Physics-Inspired Structural Representations for Molecules and Materials

(2021) Felix Musil et al.   CHEMICAL REVIEWS

Benchmarking graph neural networks for materials chemistry

(2021) Victor Fung et al.   npj Computational Materials

Entropy-based active learning of graph neural network surrogate models for materials properties

(2021) Johannes Allotey et al.   JOURNAL OF CHEMICAL PHYSICS

Geometry optimization using Gaussian process regression in internal coordinate systems

(2020) Ralf Meyer et al.   JOURNAL OF CHEMICAL PHYSICS

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model

(2020) Malthe K. Bisbo et al.   PHYSICAL REVIEW LETTERS

Descriptors for dielectric constants of perovskite-type oxides by materials informatics with first-principles density functional theory

(2020) Yusuke Noda et al.   SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS

Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction

(2020) Gabriele Scalia et al.   Journal of Chemical Information and Modeling

Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery

(2020) Cheol Woo Park et al.   Physical Review Materials

Machine learning substitutional defect formation energies in ABO3 perovskites

(2020) Vinit Sharma et al.   JOURNAL OF APPLIED PHYSICS

Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization

(2020) Zachary del Rosario et al.   JOURNAL OF CHEMICAL PHYSICS

Modeling the dielectric constants of crystals using machine learning

(2020) Kazuki Morita et al.   JOURNAL OF CHEMICAL PHYSICS

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

(2020) Ali Raza et al.   Journal of Physical Chemistry C

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

(2020) Alexander Dunn et al.   npj Computational Materials

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

(2019) Turab Lookman et al.   npj Computational Materials

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage

(2019) N. Scott Bobbitt et al.   MOLECULAR SIMULATION

Machine-learned multi-system surrogate models for materials prediction

(2019) Chandramouli Nyshadham et al.   npj Computational Materials

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning

(2019) Daniel W. Davies et al.   CHEMISTRY OF MATERIALS

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

(2019) Nathaniel Raimbault et al.   NEW JOURNAL OF PHYSICS

Descriptors for Electron and Hole Charge Carriers in Metal Oxides

(2019) Daniel W. Davies et al.   Journal of Physical Chemistry Letters

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Exploration versus Exploitation in Global Atomistic Structure Optimization

(2018) Mathias S. Jørgensen et al.   JOURNAL OF PHYSICAL CHEMISTRY A

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

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

High-throughput screening of bimetallic catalysts enabled by machine learning

(2017) Zheng Li et al.   Journal of Materials Chemistry A

High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials

(2017) Ioannis Petousis et al.   Scientific Data

Computational Screening of All Stoichiometric Inorganic Materials

(2016) Daniel W. Davies et al.   Chem

Representation Learning: A Review and New Perspectives

(2013) Y. Bengio et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

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

(2013) Anubhav Jain et al.   APL Materials