[HTML][HTML] Recent advances and applications of machine learning in solid-state materials science
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …
is machine learning. This collection of statistical methods has already proved to be capable …
[HTML][HTML] Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Towards stacking fault energy engineering in FCC high entropy alloys
Abstract Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the
plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for …
plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for …
[HTML][HTML] Bayesian optimization with adaptive surrogate models for automated experimental design
Bayesian optimization (BO) is an indispensable tool to optimize objective functions that
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
[HTML][HTML] AI applications through the whole life cycle of material discovery
We provide a review of machine learning (ML) tools for material discovery and sophisticated
applications of different ML strategies. Although there have been a few published reviews on …
applications of different ML strategies. Although there have been a few published reviews on …
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
The use of statistical/machine learning (ML) approaches to materials science is
experiencing explosive growth. Here, we review recent work focusing on the generation and …
experiencing explosive growth. Here, we review recent work focusing on the generation and …
[HTML][HTML] Bayesian optimization with active learning of design constraints using an entropy-based approach
The design of alloys for use in gas turbine engine blades is a complex task that involves
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
Materials informatics: From the atomic-level to the continuum
In recent years materials informatics, which is the application of data science to problems in
materials science and engineering, has emerged as a powerful tool for materials discovery …
materials science and engineering, has emerged as a powerful tool for materials discovery …
[HTML][HTML] Digital twins for materials
Digital twins are emerging as powerful tools for supporting innovation as well as optimizing
the in-service performance of a broad range of complex physical machines, devices, and …
the in-service performance of a broad range of complex physical machines, devices, and …