Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144583/1/aic16198.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144583/2/aic16198_am.pd
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
Technological advancements in recent decades have greatly transformed the field of material chemistr...
The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts....
Designing heterogeneous catalysts that have improved activity, selectivity and reduced cost are the ...
The development of machine learned potentials for catalyst discovery has predominantly been focused ...
The benefits of using machine learning approaches in the design, optimisation and understanding of h...
The discovery of new catalysts is one of the significant topics of computational chemistry as it has...
Heterogeneous catalysis is the central pillar of chemical industry, but they are mostly developed vi...
The design of catalyst products to reduce harmful emissions is currently an intensive process o...
Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descr...
Given the importance of catalysts in the chemical industry, they have been extensively investigated ...
The performance in heterogeneous catalysis is an example of a complex materials function, governed b...
In this tutorial we highlight the optimal working methodology for discovering novel heterogeneous ca...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
First-principles-based multiscale models are ever more successful in addressing the wide range of le...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
Technological advancements in recent decades have greatly transformed the field of material chemistr...
The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts....
Designing heterogeneous catalysts that have improved activity, selectivity and reduced cost are the ...
The development of machine learned potentials for catalyst discovery has predominantly been focused ...
The benefits of using machine learning approaches in the design, optimisation and understanding of h...
The discovery of new catalysts is one of the significant topics of computational chemistry as it has...
Heterogeneous catalysis is the central pillar of chemical industry, but they are mostly developed vi...
The design of catalyst products to reduce harmful emissions is currently an intensive process o...
Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descr...
Given the importance of catalysts in the chemical industry, they have been extensively investigated ...
The performance in heterogeneous catalysis is an example of a complex materials function, governed b...
In this tutorial we highlight the optimal working methodology for discovering novel heterogeneous ca...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
First-principles-based multiscale models are ever more successful in addressing the wide range of le...
Catalyst optimization for enantioselective transformations has traditionally relied on empirical eva...
Technological advancements in recent decades have greatly transformed the field of material chemistr...
The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts....
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