[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125586-en":3,"doc-seo-125586-105":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":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},125586,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Fully First-Principles Surface Spectroscopy with Machine Learning","Fully first-principles surface spectroscopy with machine learning is developed to address limitations in modeling aqueous interfaces using vibrational sum-frequency generation (VSFG). Empirical force fields cannot capture bond breaking and complex electronic structure, while ab initio approaches are computationally costly and difficult to converge statistically. A combined machine learning strategy is proposed using high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to simulate SFG spectra of the water/air interface with ab initio accuracy.","arXiv :2305 .09321v1 [physics .chem-ph] 16 May 2023  \nAIP/123-QED  \nFully First-Principles Surface Spectroscopy with Machine Learning  \nYair Litman, 1, 2 Jinggang Lan,3, 4 Yuki Nagata,2 and David M. Wilkins5  \n1) Yusuf Hamied Department of Chemistry, University of Cambridge, Lens􀀌eld Road, Cambridge, CB2 1EW, UK  \n2) Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germanya)  \n3) Department of Chemistry, New York University, New York, NY, 10003, USA  \n4) Simons Center for Computational Physical Chemistry at New York University, New York, NY, 10003, USA  \n5) Centre for Quantum Materials and Technology, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdomb)  \nOur current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially in the last few decades due to the continuous development of surface-speci􀀌c spectroscopies, such as vibrational sum-frequency generation (VSFG) . Similarly to what happens in other spectroscopies, to extract all of the information encoded in the VSFG spectra we must turn to atomistic simulations. The latter are conventionally based either on empirical force 􀀌eld models, which cannot describe bond breaking and formation or systems with a complex electronic structure, or on ab initio calculations which are di􀀎cult to statistically converge due to their computational cost. These limitations ultimately hamper our understanding of aqueous interfaces. In this work, we overcome these constraints by combining two machine learning techniques, namely high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression, to simulate the SFG spectra of the water/air interface with ab initio accuracy. Leveraging a data-driven local decomposition of atomic environments, we develop a simple scheme that allows us to obtain VSFG spectra in agreement with current experiments. Moreover, we identify the main sources of inaccuracy and establish a clear pathway towards the modelling of surface-sensitive spectroscopy of complex interfaces.  \na)Electronic mail: [yl899@cam.ac.uk](yl899@cam.ac.uk)  \nb)Electronic mail: [d.wilkins@qub.ac.uk](d.wilkins@qub.ac.uk)  \nInterfaces of aqueous solutions are ubiquitous in nature and play a crucial role in many important processes, such as (electro)catalytic applications 1 , atmospheric aerosol{gas exchanges2 , and mineral dissolution3 . In particular, the water/air interface has received enormous attention from the scienti􀀌c community in the last few decades since it represents arguably the most simple and important interface with a hydrophobic surface and serves asa baseline from which more complex aqueous interfaces can be analyzed, interpreted and rationalized4 . Water presents many interesting properties, such as anomalously high surface tension and a non-monotonic temperature dependence of its density with a maximum at 4􀀎 C5 . These unique properties are attributed to its unusually strong hydrogen-bond (HB) networks. A fundamental understanding of the properties and reactivity of aqueous interfaces thus demands a molecular-level description that can capture the 􀀌ne energetic balances governing the structural and dynamical characteristics of the HB networks6,7 .  \nTechniques that isolate the signal from the relatively few surface molecules at the surfaces from the enormous contribution due to the bulk are essential to study interfaces. Vibrational sum frequency generation (VSFG) belongs to a selected class of spectroscopic techniques with the capability of probing such interfaces with molecular-level and chemical sensitivity8 . In VSFG experiments, IR and UV-visible pulses are spatially and temporally overlapped anda signal generated by the sample at the sum of the frequencies of the incoming radiations is measured. The signal is determined by the second order susceptibility of the sample, (2) . Since (2) is zero in ","cbCainCt2dlxxtyd","https://ap.wps.com/l/cbCainCt2dlxxtyd","pdf",2348301,1,39,"English","en",105,"# Introduction\n## Motivation: aqueous interfaces and VSFG\n## Challenges in theoretical VSFG calculations\n# Methods\n## Machine learning interatomic potentials\n## Symmetry-adapted Gaussian process regression\n## Obtaining VSFG spectra for the water/air interface\n# Results and Analysis\n## Agreement with experiments\n## Sources of inaccuracy and modeling roadmap","[{\"question\":\"Why are atomistic simulations needed to interpret VSFG spectra?\",\"answer\":\"VSFG signals originate from interfacial molecules, while experimental spectra can be broad and featureless. Atomistic simulations help disentangle spectral components and provide a microscopic explanation.\"},{\"question\":\"What limitations exist in conventional simulation approaches for aqueous interfaces?\",\"answer\":\"Empirical force fields cannot model bond breaking and formation or complex electronic structures, and ab initio calculations are expensive and statistically hard to converge over long simulation times.\"},{\"question\":\"How does the proposed machine learning approach improve accuracy?\",\"answer\":\"It combines high-dimensional neural network interatomic potentials with symmetry-adapted Gaussian process regression to simulate SFG spectra with ab initio accuracy and to generate VSFG spectra consistent with experiments.\"}]","Fully First-Principles Surface Spectroscopy with Machine Learning | PDF",1785900063,98,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fully-first-principles-surface-spectroscopy-with-machine-learning-125586","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fully-first-principles-surface-spectroscopy-with-machine-learning-125586/125586/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are atomistic simulations needed to interpret VSFG spectra?","Question",{"text":75,"@type":76},"VSFG signals originate from interfacial molecules, while experimental spectra can be broad and featureless. Atomistic simulations help disentangle spectral components and provide a microscopic explanation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations exist in conventional simulation approaches for aqueous interfaces?",{"text":80,"@type":76},"Empirical force fields cannot model bond breaking and formation or complex electronic structures, and ab initio calculations are expensive and statistically hard to converge over long simulation times.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed machine learning approach improve accuracy?",{"text":84,"@type":76},"It combines high-dimensional neural network interatomic potentials with symmetry-adapted Gaussian process regression to simulate SFG spectra with ab initio accuracy and to generate VSFG spectra consistent with experiments.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]