[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126692-en":3,"doc-seo-126692-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126692,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Accurate energy barriers for catalytic reaction pathways - an automatic training protocol for machine learning force fields","A training protocol for machine learning force fields (MLFFs) enables accurate determination of energy barriers across catalytic reaction pathways. The method is validated on CO2 hydrogenation to methanol over indium oxide using active learning. The final MLFF reproduces density functional theory (DFT) energy barriers within 0.05 eV while substantially accelerating in-silico catalytic workflows. The approach revises the rate-limiting step, lowering activation energy by 40%, and includes finite-temperature free-energy barriers. Transferability is demonstrated on an experimentally relevant but previously unexplored reduced indium oxide top-layer surface, reducing reliance on direct ab-initio simulations.","[www.nature.com/npjcompumats](www.nature.com/npjcompumats)  \nARTICLE OPEN   \nAccurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields  \nLars L. Schaaf1 ✉, Edvin Fako 2, Sandip De 2 ✉, Ansgar Schäfer2 and Gábor Csányi 1  \n\n|  | We introduce a training protocol for developing machine learning force fields (MLFFs), capable of accurately determining energy barriers in catalytic reaction pathways. The protocol is validated on the extensively explored hydrogenation of carbon dioxide tomethanol over indium oxide. With the help of active learning, the final force field obtains energy barriers within 0.05 eV of Density Functional Theory. Thanks to the computational speedup, not only do we reduce the cost of routine in-silico catalytic tasks, but also find an alternative path for the previously established rate-limiting step, with a 40% reduction in activation energy. Furthermore, we illustrate the importance of finite temperature effects and compute free energy barriers. The transferability of the protocol is demonstrated on the experimentally relevant, yet unexplored, top-layer reduced indium oxide surface. The ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab- |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| initio simulations. |  |  |\n|  | npj Computational Materials (2023)9:180; [https://doi.org/10.1038/s41524-023-01](https://doi.org/10.1038/s41524-023-01)124-2 |  |\n|  |  |  |\n\nINTRODUCTION  \nComputational modeling plays a central role in understanding heterogeneous catalysis at an atomic scale. By complementing experimental observations, simulations are used to discover detailed reaction mechanisms, rationalize catalytic trends, and guide the design of catalytic materials1,2. Limited by the computational cost of ab-initio methods, however, catalytic systems are often represented by small idealized surfaces with individual molecules adsorbed at a specific active site. Under these approximations, Density Functional Theory (DFT) is extensively used to draw connections between adsorption properties and catalytic activity3–5. However, neglecting more complex effects, such as interactions between different adsorbates, poisoning, finite temperature, and complex surface morphology, can severely limit the relevance of computational studies to experimental observations4,6–10. Furthermore, selecting viable reaction paths and specific intermediate configurations requires expert knowledge or extensive trial and error. Finding all relevant reaction mechanisms and accurately predicting reaction rates would require extensive screening11. For large reaction networksand complex surfaces, this goes beyond DFT capabilities.  \nAn alternative is to use empirically parameterized force fields, which are orders of magnitude faster. However, while there exist reactive force fields12, their parameters need to be adjusted for every novel system and often deviate significantly from the true PES due to their limited expressivity13.  \nMachine learning force fields (MLFFs) offer a way to bridge this gap. Rather than starting a new electronic structure calculation for each step in a simulation, the MLFF predicts energy and forces for novel configurations using a model trained on a set of reference configurations. Apart from being orders of magnitude faster for small systems, local MLFFs scale linearly with system size, are reactive, and systematically improvable by augmenting the training set. Recent innovations in describing atomic environments and training frameworks have allowed MLFF to describe more and more complex interactions in both molecules and  \nmaterials14–20. In the context of heterogeneous catalysis, MLFFshave evolved from capturing low dimensional cuts of the PES to the direct simulation at the micron-scale13, 17,21. The pioneering neural network po","cbCaidQwYczaU2o7","https://ap.wps.com/l/cbCaidQwYczaU2o7","pdf",2293936,1,10,"English","en",105,"# Introduction\n## Computational modeling in heterogeneous catalysis\n## Limitations of ab-initio and force fields\n## Role of machine learning force fields (MLFFs)\n## Goals and training protocol overview","[{\"question\":\"What does the proposed protocol achieve for catalytic reaction pathways?\",\"answer\":\"It trains an MLFF that can accurately determine energy barriers along catalytic reaction pathways, matching DFT-quality results in the relevant regions of the potential energy surface.\"},{\"question\":\"How is active learning used in the MLFF training workflow?\",\"answer\":\"Active learning iteratively selects informative reference configurations so the MLFF converges toward the DFT potential energy surface where adsorption energies and energy barriers matter for the reaction path.\"},{\"question\":\"How does the method affect the rate-limiting step and activation energy?\",\"answer\":\"By enabling an alternative reaction pathway compared with the previously established one, it reduces the activation energy by 40%.\"}]","Accurate energy barriers for catalytic reaction pathways - 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