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 …
Isoreticular chemistry within metal–organic frameworks for gas storage and separation
W Fan, X Zhang, Z Kang, X Liu, D Sun - Coordination chemistry reviews, 2021 - Elsevier
Precise control of the pore size and environment of metal–organic framework (MOF) is a
necessary condition for achieving high performance of gas adsorption and separation. After …
necessary condition for achieving high performance of gas adsorption and separation. After …
ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis
We use prompt engineering to guide ChatGPT in the automation of text mining of metal–
organic framework (MOF) synthesis conditions from diverse formats and styles of the …
organic framework (MOF) synthesis conditions from diverse formats and styles of the …
Gas/vapour separation using ultra-microporous metal–organic frameworks: insights into the structure/separation relationship
The separation of related molecules with similar physical/chemical properties is of prime
industrial importance and practically entails a substantial energy penalty, typically …
industrial importance and practically entails a substantial energy penalty, typically …
An updated roadmap for the integration of metal–organic frameworks with electronic devices and chemical sensors
Metal–organic frameworks (MOFs) are typically highlighted for their potential application in
gas storage, separations and catalysis. In contrast, the unique prospects these porous and …
gas storage, separations and catalysis. In contrast, the unique prospects these porous and …
Advances, updates, and analytics for the computation-ready, experimental metal–organic framework database: CoRE MOF 2019
Over 14 000 porous, three-dimensional metal–organic framework structures are compiled
and analyzed as a part of an update to the Computation-Ready, Experimental Metal …
and analyzed as a part of an update to the Computation-Ready, Experimental Metal …
Data-driven strategies for accelerated materials design
Conspectus The ongoing revolution of the natural sciences by the advent of machine
learning and artificial intelligence sparked significant interest in the material science …
learning and artificial intelligence sparked significant interest in the material science …
Development of a Cambridge Structural Database subset: a collection of metal–organic frameworks for past, present, and future
We report the generation and characterization of the most complete collection of metal–
organic frameworks (MOFs) maintained and updated, for the first time, by the Cambridge …
organic frameworks (MOFs) maintained and updated, for the first time, by the Cambridge …
Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
25 years of reticular chemistry
At its core, reticular chemistry has translated the precision and expertise of organic and
inorganic synthesis to the solid state. While initial excitement over metal–organic …
inorganic synthesis to the solid state. While initial excitement over metal–organic …