[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120164-en":3,"doc-seo-120164-105":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},120164,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Efficient Composite Infrared Spectroscopy - Combining the Double-Harmonic Approximation with Machine Learning Potentials","Vibrational spectroscopy enables accurate molecular characterization and is central to computational studies of molecular materials. This work evaluates the predictive accuracy and computational efficiency of gas-phase IR spectrum calculations through a composite framework combining the double-harmonic approximation with transferable machine learning potentials. IR intensities are computed using harmonic vibrational frequencies together with squared derivatives of the molecular dipole moment, allowing flexible treatment from dipoles to vibrational modes. Semiempirical xTB, charge equilibrium models, and MLPs for dipole prediction are benchmarked on diverse organic molecules, with emphasis on the MACE-OFF23 potential to mitigate low-cost accuracy limits.","This article is licensed under CC-BY 4.0   \n[pubs.acs.org/JCTC](pubs.acs.org/JCTC)  Article   \nEfficient Composite Infrared Spectroscopy: Combining the DoubleHarmonic Approximation with Machine Learning Potentials  \nPhilipp Pracht, * Yuthika Pillai, Venkat Kapil, Gábor Csányi, Nils Gönnheimer, Martin Vondrák, Johannes T. Margraf, and David J. Wales  \n Cite This: J. Chem. Theory Comput. 2024, 20, 10986−11004  \nRead Online  \n\n|  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n\nABSTRACT: Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target for the computational investigation of molecular materials. Building on previous comprehensive assessments of efficient methods for infrared (IR) spectroscopy, this study investigates the predictive accuracy and computational efficiency of gas-phase IR spectra calculations, accessible through a combination of modern semiempirical quantum mechanical and transferable machine learning potentials. A composite approach for IR spectra prediction based on the double-harmonic approximation, utilizing harmonic vibrational frequencies in combination squared derivatives of the molecular dipole moment, is employed. This approach allows for methodical flexibility in the calculation of IR intensities from  \nmolecular dipoles and the corresponding vibrational modes. Various methods are systematically tested to suggest a suitable protocol with an emphasis on computational efficiency. Among these methods, semiempirical extended tight-binding (xTB) models, classical charge equilibrium models, and machine learning potentials trained for dipole moment prediction are assessed across a diverse data set of organic molecules. We particularly focus on the recently reported foundational machine learning potential MACE-OFF23 to address the accuracy limitations of conventional low-cost quantum mechanical and force-field methods. This study aims to establish a standard for the efficient computational prediction of IR spectra, facilitating the rapid and reliable identification of unknown compounds and advancing automated high-throughput analytical workflows in chemistry.  \n1. INTRODUCTION  \nInfrared (IR) spectroscopy remains a key analytical tool for characterizing molecular structures and dynamics, widely applied from fundamental research to industrial quality control. 1−3 Among the various types of vibrational spectroscopy, IR spectroscopy is arguably most widely adapted and has hence been a longstanding target for computational simulations, where the two most common approaches are Fourier transform-based spectra prediction from both classical and quantum dynamical simulations,4 or static approaches based on the harmonic molecular Hessian and the so-called double-harmonic approximation (DHA).5 While the former approach includes anharmonic effects and an averaging over conformational states via time evolution of the system, the static approach generally produces less computational overhead. This computational efficiency can be especially important, since predicting vibrational spectra often involves time-consuming first-principles calculations that account specifically for electronic structure. To partially circumvent these problems, several approaches have been proposed to predict vibrational spectra from dynamical simulations via machine learning.6−11 Within static approaches, the current  \nstate-of-the-art corresponds to extensive methods like VPT2, 12, 13 that go beyond the harmonic approximation, include electronic effects, and correctly treat vibrationalanharmonicity. While these approaches are routinely applicable with via a variety of theories implemented in software program packages, they are often associated with increased computational effort, in particular for larger system size,14 and thus are less suitable for high-throughput applications than more approxi","cbCaivz2Z9W0hByt","https://ap.wps.com/l/cbCaivz2Z9W0hByt","pdf",7593848,1,19,"English","en",105,"# Abstract\n# Introduction\n## Computational approaches for IR spectra\n## Double-harmonic approximation and efficiency\n## Prior developments in SQM and machine learning potentials","[{\"question\":\"What computational strategy is used to predict gas-phase IR spectra in this study?\",\"answer\":\"A composite approach based on the double-harmonic approximation combines harmonic vibrational frequencies with squared derivatives of the molecular dipole moment to compute IR intensities.\"},{\"question\":\"Which methods are systematically benchmarked for IR intensity prediction?\",\"answer\":\"The study tests semiempirical extended tight-binding (xTB) models, classical charge equilibrium models, and machine learning potentials trained for dipole moment prediction across a diverse set of organic molecules.\"},{\"question\":\"Why is the MACE-OFF23 machine learning potential emphasized?\",\"answer\":\"MACE-OFF23 is highlighted to address accuracy limitations of conventional low-cost quantum mechanical and force-field methods, improving the reliability of the composite IR predictions.\"}]","Efficient Composite Infrared Spectroscopy - Combining the Double-Harmonic Approximation with Machine Learning Potentials | PDF",1785728515,48,{"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},"efficient-composite-infrared-spectroscopy-combining-the-double-harmonic-approximation-with-machine-learning-potentials","",{"@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/efficient-composite-infrared-spectroscopy-combining-the-double-harmonic-approximation-with-machine-learning-potentials/120164/",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","2026-08-03",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},"What computational strategy is used to predict gas-phase IR spectra in this study?","Question",{"text":75,"@type":76},"A composite approach based on the double-harmonic approximation combines harmonic vibrational frequencies with squared derivatives of the molecular dipole moment to compute IR intensities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which methods are systematically benchmarked for IR intensity prediction?",{"text":80,"@type":76},"The study tests semiempirical extended tight-binding (xTB) models, classical charge equilibrium models, and machine learning potentials trained for dipole moment prediction across a diverse set of organic molecules.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the MACE-OFF23 machine learning potential emphasized?",{"text":84,"@type":76},"MACE-OFF23 is highlighted to address accuracy limitations of conventional low-cost quantum mechanical and force-field methods, improving the reliability of the composite IR predictions.","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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]