Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
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 …
Data quantity governance for machine learning in materials science
Y Liu, Z Yang, X Zou, S Ma, D Liu… - National Science …, 2023 - academic.oup.com
Data-driven machine learning (ML) is widely employed in the analysis of materials structure–
activity relationships, performance optimization and materials design due to its superior …
activity relationships, performance optimization and materials design due to its superior …
Innovative materials science via machine learning
C Gao, X Min, M Fang, T Tao, X Zheng… - Advanced Functional …, 2022 - Wiley Online Library
Nowadays, the research on materials science is rapidly entering a phase of data‐driven
age. Machine learning, one of the most powerful data‐driven methods, have been being …
age. Machine learning, one of the most powerful data‐driven methods, have been being …
Inverse design of solid-state materials via a continuous representation
The non-serendipitous discovery of materials with targeted properties is the ultimate goal of
materials research, but to date, materials design lacks the incorporation of all available …
materials research, but to date, materials design lacks the incorporation of all available …
Representations of materials for machine learning
J Damewood, J Karaguesian, JR Lunger… - Annual Review of …, 2023 - annualreviews.org
High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …
given rise to a new era of computational materials science by learning the relations between …
Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering
The fields of machining learning and artificial intelligence are rapidly expanding, impacting
nearly every technological aspect of society. Many thousands of published manuscripts …
nearly every technological aspect of society. Many thousands of published manuscripts …
Crystal structure prediction via deep learning
We demonstrate the application of deep neural networks as a machine-learning tool for the
analysis of a large collection of crystallographic data contained in the crystal structure …
analysis of a large collection of crystallographic data contained in the crystal structure …
Machine learning for renewable energy materials
Achieving the 2016 Paris agreement goal of limiting global warming below 2° C and
securing a sustainable energy future require materials innovations in renewable energy …
securing a sustainable energy future require materials innovations in renewable energy …
How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics
Most discoveries in materials science have been made empirically, typically through one-
variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based …
variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based …