Materials Screening

6 papers with code • 1 benchmarks • 1 datasets

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Datasets


Most implemented papers

Predicting materials properties without crystal structure: Deep representation learning from stoichiometry

CompRhys/roost 1 Oct 2019

Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost.

Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

peterbjorgensen/msgnet 15 May 2019

The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set.

Database, Features, and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds

MTD-group/mit_model_code 26 Oct 2020

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics.

Crystal Graph Neural Networks for Data Mining in Materials Science

Tony-Y/cgnn Technical report, RIMCS LLC 2019

This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

PV-Lab/Benchmarking 23 May 2021

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.

GT4SD: Generative Toolkit for Scientific Discovery

gt4sd/gt4sd-core 8 Jul 2022

With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery at every step of the scientific method.