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 …
Machine learning in materials informatics: recent applications and prospects
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …
developments and the resounding successes of data-driven efforts in other domains …
Data‐driven materials science: status, challenges, and perspectives
Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …
the new resource, and knowledge is extracted from materials datasets that are too big or …
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 …
Machine learning assisted materials design and discovery for rechargeable batteries
Y Liu, B Guo, X Zou, Y Li, S Shi - Energy Storage Materials, 2020 - Elsevier
Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …
process for novel electrochemical energy storage materials. This review aims to provide the …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
Symbolic regression (SR) is an approach of interpretable machine learning for building
mathematical formulas that best fit certain datasets. In this work, SR is used to guide the …
mathematical formulas that best fit certain datasets. In this work, SR is used to guide the …
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 …
The latest process and challenges of microwave dielectric ceramics based on pseudo phase diagrams
H Yang, S Zhang, H Yang, Q Wen, Q Yang… - Journal of Advanced …, 2021 - Springer
The explosive process of 5G communication evokes the urgent demand of miniaturized and
integrated dielectric ceramics filter. It is a pressing need to advance the development of …
integrated dielectric ceramics filter. It is a pressing need to advance the development of …
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 …