Aggregating data to accelerate alloy design
Extracting, cleaning and analyzing and visualizing MPEA data.
Five High-Impact Research Areas in Machine Learning for Materials Science
Over the past several years, the field of materials informatics has grown dramatically. Applications of machine learning (ML) and artificial intelligence (AI) to materials science are now commonplace. As materials informatics has matured from a niche area of research into an established discipline, distinct frontiers of this discipline have come into focus, and best practices […]
Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities
Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics […]
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmation of ML predictions, either via experiment or via physics-based simulations. In this review, we first identify the core components common to materials informatics discovery pipelines, […]
Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials
Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications such as printed electronics, organic solar cells, and image sensors. In order to discover new molecules that might show improved charge mobility, combined density functional theory (DFT) and molecular dynamics (MD) calculations were performed, […]
Expanded dataset of mechanical properties and observed phases of multi-principal element alloys
This data article presents a compilation of mechanical properties of 630 multi-principal element alloys (MPEAs). Built upon recently published MPEA databases, this article includes updated records from previous reviews (with minor error corrections) along with new data from articles that were published since 2019. The extracted properties include reported composition, processing method, microstructure, density, hardness, yield […]
Accelerating electrochemical material discovery for sustainable chemical manufacturing
New electrochemical reactions are needed to reduce the carbon footprint of chemical production. Use AI to find new catalysts.
Mitigating Supply Chain Disruptions in the Materials and Chemicals Industry
How an AI-empowered workflow enables the agility needed to respond to supply chain issues.
Does machine learning work in materials?
Summary of peer reviewed, papers showing experimentally verified predictions of material properties.