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Found 5 papers, 3 papers with code
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.
Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies
Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals.
Materials Science
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
Graph Convolutional Neural Networks for Polymers Property Prediction
A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design.
Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials
We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties.
