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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 …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
Machine-learned potentials for next-generation matter simulations
The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …
fundamental trade-off: bridging large time-and length-scales with highly accurate …
Machine learning force fields
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …
numerous advances previously out of reach due to the computational complexity of …
Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …
predict or elucidate the relationship between the atomic-scale structure of matter and its …
Representations of materials for machine learning
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 …
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 …
Electronic-structure methods for materials design
The accuracy and efficiency of electronic-structure methods to understand, predict and
design the properties of materials has driven a new paradigm in research. Simulations can …
design the properties of materials has driven a new paradigm in research. Simulations can …
Unsupervised word embeddings capture latent knowledge from materials science literature
The overwhelming majority of scientific knowledge is published as text, which is difficult to
analyse by either traditional statistical analysis or modern machine learning methods. By …
analyse by either traditional statistical analysis or modern machine learning methods. By …
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 …