[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122981-en":3,"doc-seo-122981-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122981,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials","Solid-state materials rely on surface properties to determine key functionality, particularly when nanoscale effects control performance. The synthesis and operating conditions set thermodynamic driving forces and kinetic rates that yield the observed surface structure and morphology. Computational surface science links these conditions to surface phase stability, supporting fields such as heterogeneous catalysis and thin film growth. This review introduces first-principles approaches for surface phase diagrams and then highlights data-driven methods, especially learned interatomic potentials, for modeling complex inorganic surfaces at the nanoscale.","Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials  \nKyle Noordhoek 1, Christopher J. Bartel 1 *  \n1Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, USA 55455  \n* [correspondence to cbartel@umn.edu](correspondence to cbartel@umn.edu)  \nAbstract  \nThe surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material ’s synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.  \nIntroduction  \nSurface science and nanoscale synthesis are key driving factors in many current technological applications including catalysis 1 and microelectronics.2 For catalysis applications, surface reactivity is dictated by the structure of exposed surfaces on nanoparticles or thin films. Understanding the phase stability of relevant surfaces is therefore paramount for catalyst design. In thin-film devices, interfacial interactions between substrates and vapor-deposited materials dictate phase stability and, again, the observed properties are highly dependent upon the surface or interfacial structure. Hence, accurately capturing which surfaces are likely to be observed under relevant conditions plays an important role in the design of nanostructured solid-state materials.  \nBefore we can discuss the broader methods for understanding surface stability, it is necessary to introduce the terminology commonly used to distinguish plausible surfaces. An inorganic surface is modeled as a slab – an infinite 2D sheet of material formed by slicing a bulk (3D) crystal using a particular 2D plane. The cleavage of the conventional unit cell through a designated Miller plane produces a single facet. Surface facets are nominally referred to using Miller index notation to indicate the plane used to perform the slice with respect to the conventional unit cell. A facet (Miller index) alone does not define a slab as the position in the 3D crystal where the cut is made can lead to different “terminations” of the slab (i.e., different atomic species at the surface) . It is typical for multiple possible terminations per facet to be generated when computing surface  \nproperties. For a more detailed description of how surface slabs with varying terminations can be systematically generated as a starting point for first-principles calculations, see the thorough explanation given by Sun and Ceder.3 After the generation of a facet with a particular termination, the “dangling bonds” formed by slicing the bulk material can induce a rearrangement of atomic positions at/near the surface in a process known as reconstruction. Reconstructions are often denoted using Wood’s notation,4 which describes modifications ofthe surface unit cell compared with the bulk (e.g., the well-known 7×7 reconstruction of Si) .5,6 Understanding the surface structure is critical for countless applications, and the relative energies of these var","cbCais2o2QCYhUYg","https://ap.wps.com/l/cbCais2o2QCYhUYg","pdf",1084704,1,25,"English","en",105,"# Introduction\n## Surface modeling terminology\n## Computational thermodynamics of surfaces","[{\"question\":\"Why are surface properties crucial for inorganic solid-state functionality?\",\"answer\":\"Surface properties largely determine functionality, especially when nanoscale effects become important in applications such as catalysis and thin-film growth.\"},{\"question\":\"What role do synthesis or operating conditions play in surface structure formation?\",\"answer\":\"They set thermodynamic driving forces and kinetic rates that govern the formation of the surface structure and morphology observed under given conditions.\"},{\"question\":\"How does the review connect first-principles methods to machine learning for surfaces?\",\"answer\":\"It first introduces first-principles approaches for computing surface phase diagrams, then focuses on machine-learning approaches—predominantly learned interatomic potentials—to study complex inorganic surfaces.\"}]","Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials | 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are surface properties crucial for inorganic solid-state functionality?","Question",{"text":76,"@type":77},"Surface properties largely determine functionality, especially when nanoscale effects become important in applications such as catalysis and thin-film growth.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role do synthesis or operating conditions play in surface structure formation?",{"text":81,"@type":77},"They set thermodynamic driving forces and kinetic rates that govern the formation of the surface structure and morphology observed under given conditions.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the review connect first-principles methods to machine learning for surfaces?",{"text":85,"@type":77},"It first introduces first-principles approaches for computing surface phase diagrams, then focuses on machine-learning approaches—predominantly learned interatomic potentials—to study complex inorganic 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