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Benson RL, Yadavalli SS, Stamatakis M. Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations. J Phys Chem A 2023; 127:10307-10319. [PMID: 37988475 PMCID: PMC11065322 DOI: 10.1021/acs.jpca.3c05581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023]
Abstract
Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The Zacros software package (https://zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in Zacros. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages.
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Affiliation(s)
- Raz L. Benson
- Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.
| | - Sai Sharath Yadavalli
- Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.
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Savva GD, Benson RL, Christidi IA, Stamatakis M. Exact distributed kinetic Monte Carlo simulations for on-lattice chemical kinetics: lessons learnt from medium- and large-scale benchmarks. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220235. [PMID: 37211035 DOI: 10.1098/rsta.2022.0235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/30/2023] [Indexed: 05/23/2023]
Abstract
Kinetic Monte Carlo (KMC) simulations have been instrumental in multiscale catalysis studies, enabling the elucidation of the complex dynamics of heterogeneous catalysts and the prediction of macroscopic performance metrics, such as activity and selectivity. However, the accessible length- and time-scales have been a limiting factor in such simulations. For instance, handling lattices containing millions of sites with 'traditional' sequential KMC implementations is prohibitive owing to large memory requirements and long simulation times. We have recently established an approach for exact, distributed, lattice-based simulations of catalytic kinetics which couples the Time-Warp algorithm with the Graph-Theoretical KMC framework, enabling the handling of complex adsorbate lateral interactions and reaction events within large lattices. In this work, we develop a lattice-based variant of the Brusselator system, a prototype chemical oscillator pioneered by Prigogine and Lefever in the late 60s, to benchmark and demonstrate our approach. This system can form spiral wave patterns, which would be computationally intractable with sequential KMC, while our distributed KMC approach can simulate such patterns 15 and 36 times faster with 625 and 1600 processors, respectively. The medium- and large-scale benchmarks thus conducted, demonstrate the robustness of the approach, and reveal computational bottlenecks that could be targeted in further development efforts. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.
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Affiliation(s)
- Giannis D Savva
- Thomas Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Raz L Benson
- Thomas Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Ilektra-Athanasia Christidi
- Research Software Development Group, Advanced Research Computing Centre, University College London, Gower Street, London WC1E 6BT, UK
| | - Michail Stamatakis
- Thomas Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
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Mou T, Han X, Zhu H, Xin H. Machine learning of lateral adsorbate interactions in surface reaction kinetics. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Pineda M, Stamatakis M. Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges. J Chem Phys 2022; 156:120902. [DOI: 10.1063/5.0083251] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.
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Affiliation(s)
- M. Pineda
- Thomas Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
| | - M. Stamatakis
- Thomas Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
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Goswami A, Ma H, Schneider WF. Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions. J Catal 2022. [DOI: 10.1016/j.jcat.2021.12.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Abstract
The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.
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Affiliation(s)
- Benjamin W J Chen
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lang Xu
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Manos Mavrikakis
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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Ravipati S, d'Avezac M, Nielsen J, Hetherington J, Stamatakis M. A Caching Scheme To Accelerate Kinetic Monte Carlo Simulations of Catalytic Reactions. J Phys Chem A 2020; 124:7140-7154. [PMID: 32786994 DOI: 10.1021/acs.jpca.0c03571] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Kinetic Monte Carlo (KMC) simulations have been instrumental in advancing our fundamental understanding of heterogeneously catalyzed reactions, with particular emphasis on structure sensitivity, ensemble effects, and the interplay between adlayer structure and adsorbate-adsorbate lateral interactions in shaping the observed kinetics. Yet, the computational cost of KMC remains high, thereby motivating the development of acceleration schemes that would improve the simulation efficiency. We present an exact such scheme, which implements a caching algorithm along with shared-memory parallelization to improve the computational performance of simulations incorporating long-range adsorbate-adsorbate lateral interactions. This scheme is based on caching information about the energetic interaction patterns associated with the products of each possible lattice process (adsorption, desorption, reaction etc.). Thus, every time a reaction occurs ("ongoing reaction"), it enables fast updates of the rate constants of "affected reactions", i.e., possible reactions in the region of influence of the "ongoing reaction". Benchmarks on KMC simulations of NOx oxidation/reduction, yielded acceleration factors of up to 20, when comparing single-thread runs without caching to runs on 16 threads with caching, for simulations with a cluster expansion Hamiltonian that incorporates up to 8th-nearest-neighbor interactions.
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Affiliation(s)
- Srikanth Ravipati
- Thomas Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Mayeul d'Avezac
- Research Software Development Group, Research IT Services, University College London, Torrington Place, London WC1E 6BT, United Kingdom
| | - Jens Nielsen
- Research Software Development Group, Research IT Services, University College London, Torrington Place, London WC1E 6BT, United Kingdom
| | - James Hetherington
- Research Software Development Group, Research IT Services, University College London, Torrington Place, London WC1E 6BT, United Kingdom
| | - Michail Stamatakis
- Thomas Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
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