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Found 7 papers, 2 papers with code
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning
Autonomous materials research labs require the ability to combine and learn from diverse data streams.
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison
The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used tool for predicting material performances.
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1].
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems.
Materials Science
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure.
