[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290812-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290812-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","machine-learning-driven-global-optimization-of-surface-adsorbate-geometries","Machine-learning driven global optimization of surface adsorbate geometries","","Adsorption energies of molecular adsorbates on catalyst surfaces serve as key descriptors in computational catalysis, but larger and flexible intermediates create many local minima and complex potential energy surfaces, making global optimization difficult and uncertainty high. A global optimization protocol is presented that trains an on-the-fly surrogate machine-learning potential, iteratively updating its training set with configurations discovered by the algorithm. The method supports arbitrary surface models and adsorbates while reducing manual input and the number of DFT calculations, with demonstrated efficiency for adsorbates on Rh(111) and (211) surfaces.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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optimization of surface adsorbate geometries difficult for larger reactants?","Question",{"text":63,"@type":64},"Larger adsorbates have many possible binding motifs, leading to numerous local minima on the potential energy surface, and their internal flexibility makes convergence strongly dependent on the initial geometry.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What problem do brute-intuition and brute-force screening introduce?",{"text":68,"@type":64},"Brute-intuition is biased by user choice of starting geometries, while brute-force screening becomes computationally prohibitive due to the large number of viable candidate structures.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the proposed method reduce DFT calculations and human intervention?",{"text":72,"@type":64},"It trains an on-the-fly surrogate machine-learning potential and iteratively updates the training set using configurations explored by the optimization algorithm, avoiding exhaustive enumeration 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driven global optimization of surface adsorbate geometries  \nHyunwook Jung 1, Lena Sauerland1, Sina Stocker1, Karsten Reuter1 and Johannes T. Margraf1 ✉  \n\n| | The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of possible binding motifs leads to complex potential energy surfaces (PES), however. This implies that finding the optimal structure is a difficult global optimization problem, which leads to significant uncertainty about the stability of many intermediates. To tackle this issue, we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential onthe-fly. The approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the algorithm We | |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  | demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111) and (211) surfaces. |  |\n|  | npj Computational Materials (2023)9:114; [https://doi.org/10.1038/s41524-023-01065-w](https://doi.org/10.1038/s41524-023-01065-w) |  |\n|  |  |  |\n\n.  \nINTRODUCTION  \nThe adsorption energies of key reactants are the most commonly used descriptors for the activity of heterogeneous catalysts, and thus play an important role in understanding and designing catalysts1,2. In the case of small adsorbates (OHx, CHx, NHx) and ideal metal surfaces, adsorption energies are relatively straightforward to calculate with first-principles methods. In this case, theadsorbates feature simple monodentate binding motives and the number of symmetry inequivalent surface sites on low-index crystalline facets is small. Indeed, one can even benefit from simple linear scaling relations to estimate adsorption energies across different surfaces3.  \nThe situation is different when larger reactants are involved in the process. A prime example of this is synthesis gas (syngas) conversion, which is an important industrial process that can be used to produce valuable chemicals and fuels such as ethanol and higher hydrocarbons. Here, the reaction network features a wide range of differently sized intermediates4–7. Understanding the selectivity of syngas conversion on a given catalyst therefore inevitably requires at least an approximate notion of the adsorption energies of these larger molecules.  \nThe main problem with this is that there are typically many local minima on the adsorbate binding energy surface for larger adsorbates. This is due to the fact that the possible binding motives on a catalyst surface are numerous in this case, including multidentate geometries. Furthermore, the adsorbates may display significant internal flexibility, so that the convergence of a local geometry optimization will strongly depend on the details of the initial geometry used to set up the calculation. These issues are exacerbated when working with nontrivial surface models (e.g., high-index, defected or amorphous surfaces) . In combination, these factors presently hinder the robust and reproducible prediction of the adsorption energies of large, flexible molecules on catalyst surfaces.  \nThe most common way of addressing this issue is so-called“brute-intuition”8. This basically means constructing a number of reasonable starting geometries by hand and carrying out local relaxation for each in order to find the most stable adsorbate  \ngeometry. However, this approach is clearly biased by the intuition of the user. To avoid this, a full “brute-force” search can be performed, which amounts to the high-throughput screening of all viable candidate","cbCainKNr7Nv6Qp9","https://ap.wps.com/l/cbCainKNr7Nv6Qp9","pdf",1880833,"English","# INTRODUCTION\n## Adsorption energies as descriptors in heterogeneous catalysis\n## Challenges from larger, flexible adsorbates and complex PES\n## Limitations of brute-intuition and brute-force searches\n## Global optimization algorithms and computational expense with DFT\n## ML surrogate models to accelerate unbiased search","[{\"question\":\"Why is global optimization of surface adsorbate geometries difficult for larger reactants?\",\"answer\":\"Larger adsorbates have many possible binding motifs, leading to numerous local minima on the potential energy surface, and their internal flexibility makes convergence strongly dependent on the initial geometry.\"},{\"question\":\"What problem do brute-intuition and brute-force screening introduce?\",\"answer\":\"Brute-intuition is biased by user choice of starting geometries, while brute-force screening becomes computationally prohibitive due to the large number of viable candidate structures.\"},{\"question\":\"How does the proposed method reduce DFT calculations and human intervention?\",\"answer\":\"It trains an on-the-fly surrogate machine-learning potential and iteratively updates the training set using configurations explored by the optimization algorithm, avoiding exhaustive enumeration and lowering the number of required DFT evaluations.\"}]","Machine-learning driven global optimization of surface adsorbate geometries | PDF"]