Materials Screening
7 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
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
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
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
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
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.
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.