Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Theory-guided experimental design in battery materials research
A reliable energy storage ecosystem is imperative for a renewable energy future, and
continued research is needed to develop promising rechargeable battery chemistries. To …
continued research is needed to develop promising rechargeable battery chemistries. To …
Cation disorder engineering yields AgBiS2 nanocrystals with enhanced optical absorption for efficient ultrathin solar cells
Strong optical absorption by a semiconductor is a highly desirable property for many
optoelectronic and photovoltaic applications. The optimal thickness of a semiconductor …
optoelectronic and photovoltaic applications. The optimal thickness of a semiconductor …
The role of machine learning in the understanding and design of materials
Develo** algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …
enable us to systematically find novel materials, which can have huge technological and …
[HTML][HTML] Roadmap on organic–inorganic hybrid perovskite semiconductors and devices
Metal halide perovskites are the first solution processed semiconductors that can compete in
their functionality with conventional semiconductors, such as silicon. Over the past several …
their functionality with conventional semiconductors, such as silicon. Over the past several …
Machine learning for materials scientists: an introductory guide toward best practices
This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …
machine learning-centered research. We cover broad guidelines and best practices …
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 …
Materials Cloud, a platform for open computational science
Materials Cloud is a platform designed to enable open and seamless sharing of resources
for computational science, driven by applications in materials modelling. It hosts (1) archival …
for computational science, driven by applications in materials modelling. It hosts (1) archival …
Machine learning meets with metal organic frameworks for gas storage and separation
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …