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Kouroudis I, Gößwein M, Gagliardi A. Utilizing Data-Driven Optimization to Automate the Parametrization of Kinetic Monte Carlo Models. J Phys Chem A 2023. [PMID: 37421601 DOI: 10.1021/acs.jpca.3c02482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2023]
Abstract
Kinetic Monte Carlo (kMC) simulations are a popular tool to investigate the dynamic behavior of stochastic systems. However, one major limitation is their relatively high computational costs. In the last three decades, significant effort has been put into developing methodologies to make kMC more efficient, resulting in an enhanced runtime efficiency. Nevertheless, kMC models remain computationally expensive. This is in particular an issue in complex systems with several unknown input parameters where often most of the simulation time is required for finding a suitable parametrization. A potential route for automating the parametrization of kinetic Monte Carlo models arises from coupling kMC with a data-driven approach. In this work, we equip kinetic Monte Carlo simulations with a feedback loop consisting of Gaussian Processes (GPs) and Bayesian optimization (BO) to enable a systematic and data-efficient input parametrization. We utilize the results from fast-converging kMC simulations to construct a database for training a cheap-to-evaluate surrogate model based on Gaussian processes. Combining the surrogate model with a system-specific acquisition function enables us to apply Bayesian optimization for the guided prediction of suitable input parameters. Thus, the amount of trial simulation runs can be considerably reduced facilitating an efficient utilization of arbitrary kMC models. We showcase the effectiveness of our methodology for a physical process of growing industrial relevance: the space-charge layer formation in solid-state electrolytes as it occurs in all-solid-state batteries. Our data-driven approach requires only 1-2 iterations to reconstruct the input parameters from different baseline simulations within the training data set. Moreover, we show that the methodology is even capable of accurately extrapolating into regions outside the training data set which are computationally expensive for direct kMC simulation. Concluding, we demonstrate the high accuracy of the underlying surrogate model via a full parameter space investigation eventually making the original kMC simulation obsolete.
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Affiliation(s)
- Ioannis Kouroudis
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Strasse 1/III, 85748 Garching bei München, Germany
| | - Manuel Gößwein
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Strasse 1/III, 85748 Garching bei München, Germany
| | - Alessio Gagliardi
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Strasse 1/III, 85748 Garching bei München, Germany
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Katzenmeier L, Gößwein M, Carstensen L, Sterzinger J, Ederer M, Müller-Buschbaum P, Gagliardi A, Bandarenka AS. Mass transport and charge transfer through an electrified interface between metallic lithium and solid-state electrolytes. Commun Chem 2023; 6:124. [PMID: 37322266 DOI: 10.1038/s42004-023-00923-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/05/2023] [Indexed: 06/17/2023] Open
Abstract
All-solid-state Li-ion batteries are one of the most promising energy storage devices for future automotive applications as high energy density metallic Li anodes can be safely used. However, introducing solid-state electrolytes needs a better understanding of the forming electrified electrode/electrolyte interface to facilitate the charge and mass transport through it and design ever-high-performance batteries. This study investigates the interface between metallic lithium and solid-state electrolytes. Using spectroscopic ellipsometry, we detected the formation of the space charge depletion layers even in the presence of metallic Li. That is counterintuitive and has been a subject of intense debate in recent years. Using impedance measurements, we obtain key parameters characterizing these layers and, with the help of kinetic Monte Carlo simulations, construct a comprehensive model of the systems to gain insights into the mass transport and the underlying mechanisms of charge accumulation, which is crucial for developing high-performance solid-state batteries.
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Affiliation(s)
- Leon Katzenmeier
- Technical University of Munich, TUM School of Natural Sciences, Department of Physics, Physics of Energy Conversion and Storage, James-Franck-Str. 1, 85748, Garching, Germany
- TUMint·Energy Research, Lichtenbergstr. 4, 85748, Garching bei München, Germany
| | - Manuel Gößwein
- Technical University of Munich, TUM School of Computation, Information and Technology, Department of Electrical and Computer Engineering, Hans-Piloty-Straße 1, 85748, Garching bei München, Germany
| | - Leif Carstensen
- Technical University of Munich, TUM School of Natural Sciences, Department of Physics, Physics of Energy Conversion and Storage, James-Franck-Str. 1, 85748, Garching, Germany
- TUMint·Energy Research, Lichtenbergstr. 4, 85748, Garching bei München, Germany
| | - Johannes Sterzinger
- TUMint·Energy Research, Lichtenbergstr. 4, 85748, Garching bei München, Germany
| | - Michael Ederer
- TUMint·Energy Research, Lichtenbergstr. 4, 85748, Garching bei München, Germany
| | - Peter Müller-Buschbaum
- Technical University of Munich, TUM School of Natural Sciences, Department of Physics, Chair for Functional Materials, James-Franck-Str. 1, 85748, Garching, Germany
- Heinz Maier-Leibnitz Zentrum (MLZ), Technical University of Munich, Lichtenbergstr. 1, 85748, Garching, Germany
| | - Alessio Gagliardi
- Technical University of Munich, TUM School of Computation, Information and Technology, Department of Electrical and Computer Engineering, Hans-Piloty-Straße 1, 85748, Garching bei München, Germany.
| | - Aliaksandr S Bandarenka
- Technical University of Munich, TUM School of Natural Sciences, Department of Physics, Physics of Energy Conversion and Storage, James-Franck-Str. 1, 85748, Garching, Germany.
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Gößwein M, Kaiser W, Gagliardi A. Local Temporal Acceleration Scheme to Couple Transport and Reaction Dynamics in Kinetic Monte Carlo Models of Electrochemical Systems. J Chem Theory Comput 2022; 18:2749-2763. [DOI: 10.1021/acs.jctc.1c01010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Manuel Gößwein
- Department of Electrical and Computer Engineering, Technical University of Munich, Karlstraße 45, 80333 Munich, Germany
| | - Waldemar Kaiser
- Department of Electrical and Computer Engineering, Technical University of Munich, Karlstraße 45, 80333 Munich, Germany
- Computational Laboratory for Hybrid/Organic Photovoltaics (CLHYO), Istituto CNR di Scienze e Tecnologie Chimiche “Giulio Natta” (CNR-SCITEC), 06123 Perugia, Italy
| | - Alessio Gagliardi
- Department of Electrical and Computer Engineering, Technical University of Munich, Karlstraße 45, 80333 Munich, Germany
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Abstract
Kinetic Monte Carlo (kMC) simulations are frequently used to study (electro-)chemical processes within science and engineering. kMC methods provide insight into the interplay of stochastic processes and can link atomistic material properties with macroscopic characteristics. Significant problems concerning the computational demand arise if processes with large time disparities are competing. Acceleration algorithms are required to make slow processes accessible. Especially, the accelerated superbasin kMC (AS-kMC) scheme has been frequently applied within chemical reaction networks. For larger systems, the computational overhead of the AS-kMC is significant as the computation of the superbasins is done during runtime and comes with the need for large databases. Here, we propose a novel acceleration scheme for diffusion and transport processes within kMC simulations. Critical superbasins are detected during the system initialization. Scaling factors for the critical rates within the superbasins, as well as a lower bound for the number of sightings, are derived. Our algorithm exceeds the AS-kMC in the required simulation time, which we demonstrate with a 1D-chain example. In addition, we apply the acceleration scheme to study the time-of-flight (TOF) of charge carriers within organic semiconductors. In this material class, time disparities arise due to a significant spread of transition rates. The acceleration scheme allows a significant acceleration up to a factor of 65 while keeping the error of the TOF values negligible. The computational overhead is negligible, as all superbasins only need to be computed once.
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Affiliation(s)
- Waldemar Kaiser
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Manuel Gößwein
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Alessio Gagliardi
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
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