Computational Catalysis: Volume 48 (Catalysis Series) - Tapa dura

Acerca de los autores

The focus of Asthagiri's research group is in developing and applying multi-scale modeling methods to predict material properties entirely from atomistic simulations. Specific topics of focus include the atomic-scale studies of catalyst reactivity, electromechanical properties of ceramic materials, growth of metal and semiconductor nanostructures, and atomistic modeling of the aqueous-solid interface. Asthagiri has organized symposia on catalysis and surface science for the centennial AIChE meeting in Philadelphia, PA (2008). He has been active in the area of computational catalysis for the last 5 years and has funding from National Science Foundation, American Chemical Society Petroleum Research Fund, and Department of Energy for catalysis-related projects.

Prof. Michael Janik's research uses computational, atomistic modeling methods to investigate and design catalysts for alternative energy conversion systems. Janik earned his Ph.D. (2006, U. Virginia) in the field of heterogeneous catalysis under the joint-supervision of Prof. Robert J. Davis and Prof. Matthew Neurock. His thesis work used experimental and computational methods to examine acid catalysis of alkylation reactions. He completed post-doctoral study examining electrocatalyst design for direct methanol fuel cells under the advisement of Matthew Neurock. He began his appointment as an Assistant Professor of Chemical Engineering at PSU in August, 2006. Current research activities focus on fuel cells and electrochemical systems as well as fuel processing for hydrogen and synthesis gas production. Recent research activities are funded by the Department of Energy, National Science Foundation, and the American Chemical Society Petroleum Research Fund. Janik is affiliated with the PSU Electrochemical Engine Center, PSU Institutes of Energy and the Environment, and the PSU Energy Institute. He has published 28 articles in peer reviewed journals.

De la contraportada

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 method development in areas such as 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.