[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121705-en":3,"doc-seo-121705-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121705,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Fully First-Principles Surface Spectroscopy with Machine Learning","Molecular-level understanding of aqueous interfaces relies on surface-specific vibrational sum-frequency generation (VSFG) spectra, but extracting encoded information requires atomistic simulations. Existing approaches suffer from high computational cost, limiting accuracy for complex electronic structures and hindering statistical convergence. This work integrates high-dimensional neural-network interatomic potentials with symmetry-adapted Gaussian process regression to model VSFG with fully ab initio accuracy. The water/air interface example shows how to locate key sources of theoretical inaccuracy and provides a clear route toward modeling spectroscopy of complex interfaces.","This article is licensed under CC-BY 4.0   \n[pubs.acs.org/JPCL](pubs.acs.org/JPCL)  Letter   \nFully First-Principles Surface Spectroscopy with Machine Learning  \nYair Litman,* Jinggang Lan, Yuki Nagata, and David M. Wilkins *  \n Cite This: J. Phys. Chem. Lett. 2023, 14, 8175−8182  \nRead Online  \n\n|  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n\nABSTRACT: Our current understanding of the structure and dynamics of aqueous interfacesat the molecular level has grown substantially due to the continuous development of surfacespecific spectroscopies, such as vibrational sum-frequency generation (VSFG). As in other vibrational spectroscopies, we must turn to atomistic simulations to extract all of the information encoded in the VSFG spectra. The high computational cost associated with existing methods means that they have limitations in representing systems with complex electronic structure or in achieving statistical convergence. In this work, we combine highdimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to overcome these constraints. We show that it is possible to model VSFG signals with fully ab initio accuracy using machine learning and illustrate the versatility of our approach on the water/air interface. Our strategy allows us to identify the main sources of theoretical inaccuracy and establish a clear pathway toward the modeling of surface-sensitive spectroscopy of complex interfaces.  \nS oft matter interfaces, including aqueous interfaces, solid/  \nliquid interfaces, and liquid/liquid interfaces, are ubiquitous in nature and play a crucial role in many important processes, such as (electro)catalytic/electrochemical applications, 1 atmospheric aerosol−gas exchanges,2 and mineral dissolution.3 These processes are governed by the molecular level interaction between the molecules at the interface and the other (electrified) molecules/materials.  \nTo probe the interfacial response, a technique must isolate the signal of the relatively few surface molecules at the surfaces from the enormous contribution due to the bulk.4 Vibrational sum frequency generation (VSFG) is a technique where IR and visible beams are spatially and temporally overlapped and the signal generated at the sum of the input beam frequencies is measured. VSFG is a second-order nonlinear optical process, and as in any other even-order nonlinear optical techniques, the centro-symmetric bulk contributions vanish due to the symmetry of the second-order susceptibility, χ(2), making their signal surface-specific.5 The VSFG signal further possesses molecular specificity: a VSFG signal is enhanced when the IR frequency is resonant with an interfacial molecular vibration. When combining probes with different polarizations, VSFG can provide information on the orientation of interfacial molecules,6−8 the depth profile of the interfacial molecules,9 and molecular chirality.10 This makes VSFG a powerful method to characterize the identity, structure, and interaction of the molecules at interfaces.  \nExperimental VSFG data alone are normally insufficient to connect spectroscopic observables with molecular structure, and atomistic simulations are required to achieve a microscopic understanding. The theoretical calculation of VSFG spectra is more challenging than that of more traditional spectroscopies  \nsuch as linear IR and Raman, since relatively long simulation times (on the order of nanoseconds) are required to converge the statistics and guarantee that the signal in the bulk-like (centrosymmetric) regions vanishes.11, 12  \nThe vibrational resonant component of the second-order susceptibility, χp(2)qr, in an electronically nonresonant condition, can be computed as 11  \np(2qr) (IR) = i  \n0  \ndtei IRt pq(t)Pr (0)  (1)  \nwhere αpq is the pq component of the polarizability tensor, ωIRis the frequency of the IR pulse, and Pr is t","cbCaihpLRrhIfAym","https://ap.wps.com/l/cbCaihpLRrhIfAym","pdf",2113843,1,"English","en",105,"# Abstract\n## VSFG and molecular specificity\n## Need for atomistic simulations\n## Computational challenges and limitations\n## Proposed ML + ab initio modeling strategy\n## Water/air interface case study\n## Accuracy analysis and future pathway","[{\"question\":\"Why is VSFG considered surface-specific in even-order nonlinear optics?\",\"answer\":\"Because centro-symmetric bulk contributions vanish due to the symmetry of the second-order susceptibility, χ(2), making the measured signal surface-specific.\"},{\"question\":\"What limitations do existing VSFG simulation methods have?\",\"answer\":\"They have high computational cost, which restricts representing complex electronic structures and prevents efficient statistical convergence.\"},{\"question\":\"How does the proposed approach improve VSFG modeling accuracy?\",\"answer\":\"It combines high-dimensional neural network interatomic potentials with symmetry-adapted Gaussian process regression, enabling fully ab initio accurate modeling of VSFG signals.\"}]","Fully First-Principles Surface Spectroscopy with Machine Learning | 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is VSFG considered surface-specific in even-order nonlinear optics?","Question",{"text":74,"@type":75},"Because centro-symmetric bulk contributions vanish due to the symmetry of the second-order susceptibility, χ(2), making the measured signal surface-specific.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What limitations do existing VSFG simulation methods have?",{"text":79,"@type":75},"They have high computational cost, which restricts representing complex electronic structures and prevents efficient statistical convergence.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the proposed approach improve VSFG modeling accuracy?",{"text":83,"@type":75},"It combines high-dimensional neural network interatomic potentials with symmetry-adapted Gaussian process regression, enabling fully ab initio accurate modeling of VSFG 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