Computational Catalysis
- Submission status
- Open
- Submission deadline
Computational catalysis has become instrumental to predicting the performance of catalytic systems and modeling their mechanistic behavior at the molecular level. This interdisciplinary field has grown considerably in scope and precision over the years, thus bringing to bear the power of first-principles calculations and data-centric methods in controlling the activity, selectivity, and durability of catalysts. This collection highlights major advances in the development of computational methods to simulate catalytic systems and identifies outstanding opportunities in the computational design of scalable and durable catalytic materials relevant to chemical processing, energy harvesting, and sustainability. The collection welcomes high-quality articles and reviews addressing topics at the frontier of computational catalysis, including but not limited to:
- Fundamental theory leading to computationally tractable descriptors of catalytic performance;
- Uncertainty quantification enabling reliable predictions of reaction steps and catalytic barriers;
- Embedded modeling approaches capturing the response of catalysts under realistic conditions;
- Non-equilibrium simulations predicting the transient and dynamic evolution of catalytic systems;
- Machine-learning methods advancing the modeling of catalysts across length and time scales;
- Data-informed experimentation enabling the combinatorial exploration of large families of catalysts.
Editors
-
Ismaila Dabo, PhD
Carnegie Mellon University, USA
-
Bryan R Goldsmith, PhD
University of Michigan, USA
-
Brandon C. Bukowski, PhD
Johns Hopkins University, USA
-
Alex Mironenko, PhD
the University of Illinois Urbana-Champaign, USA
