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By: Aravind Asthagiri (Edited by) , Michael Janik (Edited by)
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First-principles-based modelling of catalysts is a growing field and the past decade has seen the range of applications for it increase. Improvements in computing power and developments in the areas of machine learning have made many exciting advances possible.
The new edition of Computational Catalysis provides an update on the contents of the previous edition whilst introducing new chapters on kinetic Monte Carlo, modelling solvent effects, machine learning for catalyst modelling and design, and modelling complex heterogeneous structures. Written to be accessible to anyone with a familiarity with quantum mechanical methods, this book is a valuable resource for both early career researchers and graduate students.
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