Advancing data-driven chemistry by beating benchmarks
HS Stein - Trends in Chemistry, 2022 - cell.com
Enabled by data management and digitalization adoption in chemistry, machine learning
(ML) is accelerating chemistry through automated data analysis, materials embeddings …
(ML) is accelerating chemistry through automated data analysis, materials embeddings …
Toward autonomous design and synthesis of novel inorganic materials
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting
opportunity to revolutionize inorganic materials discovery and development. Herein, we …
opportunity to revolutionize inorganic materials discovery and development. Herein, we …
Combinatorial and high-throughput screening of materials libraries: review of state of the art
R Potyrailo, K Rajan, K Stoewe, I Takeuchi… - ACS combinatorial …, 2011 - ACS Publications
Rational materials design based on prior knowledge is attractive because it promises to
avoid time-consuming synthesis and testing of numerous materials candidates. However …
avoid time-consuming synthesis and testing of numerous materials candidates. However …
Functional material systems enabled by automated data extraction and machine learning
The development of new functional materials is crucial for addressing global challenges
such as clean energy or the discovery of new drugs and antibiotics. Functional material …
such as clean energy or the discovery of new drugs and antibiotics. Functional material …
Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis
Materials discovery lays the foundation for many technological advancements. The
prediction and discovery of new materials are not simple tasks. Here, we outline some basic …
prediction and discovery of new materials are not simple tasks. Here, we outline some basic …
Machine learning in experimental materials chemistry
B Selvaratnam, RT Koodali - Catalysis Today, 2021 - Elsevier
The development of advanced materials is an important aspect of modern life. However, the
discovery of novel materials involves searching the vast chemical space to find materials …
discovery of novel materials involves searching the vast chemical space to find materials …
By how much can closed-loop frameworks accelerate computational materials discovery?
The implementation of automation and machine learning surrogatization within closed-loop
computational workflows is an increasingly popular approach to accelerate materials …
computational workflows is an increasingly popular approach to accelerate materials …
Generative organic electronic molecular design informed by quantum chemistry
Generative molecular design strategies have emerged as promising alternatives to trial-and-
error approaches for exploring and optimizing within large chemical spaces. To date …
error approaches for exploring and optimizing within large chemical spaces. To date …
Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges
Organic molecules and polymers have a broad range of applications in biomedical,
chemical, and materials science fields. Traditional design approaches for organic molecules …
chemical, and materials science fields. Traditional design approaches for organic molecules …
[HTML][HTML] stk: An extendable Python framework for automated molecular and supramolecular structure assembly and discovery
Computational software workflows are emerging as all-in-one solutions to speed up the
discovery of new materials. Many computational approaches require the generation of …
discovery of new materials. Many computational approaches require the generation of …