[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 …
A critical review of machine learning of energy materials
Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …
change landscapes for physics and chemistry. With its ability to solve complex tasks …
Autonomous discovery in the chemical sciences part I: Progress
This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …
discovery in the chemical sciences. In this first part, we describe a classification for …
Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
Materials informatics employs machine learning (ML) models to map the relationship
between a targeted property and various materials descriptors, providing new avenues to …
between a targeted property and various materials descriptors, providing new avenues to …
[HTML][HTML] Accelerating high-throughput virtual screening through molecular pool-based active learning
Structure-based virtual screening is an important tool in early stage drug discovery that
scores the interactions between a target protein and candidate ligands. As virtual libraries …
scores the interactions between a target protein and candidate ligands. As virtual libraries …
[HTML][HTML] Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
[HTML][HTML] Bayesian optimization for materials design with mixed quantitative and qualitative variables
Abstract Although Bayesian Optimization (BO) has been employed for accelerating materials
design in computational materials engineering, existing works are restricted to problems …
design in computational materials engineering, existing works are restricted to problems …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Machine learning assisted composition effective design for precipitation strengthened copper alloys
Optimizing the composition and improving the conflicting mechanical and electrical
properties of multiple complex alloys has always been difficult by traditional trial-and-error …
properties of multiple complex alloys has always been difficult by traditional trial-and-error …
Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning
A key challenge in guiding experiments toward materials with desired properties is to
effectively navigate the vast search space comprising the chemistry and structure of allowed …
effectively navigate the vast search space comprising the chemistry and structure of allowed …