Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials
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 pr...
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2024
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| _version_ | 1852024499228114944 |
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| author | Philipp Pracht (9523414) |
| author2 | Yuthika Pillai (20420740) Venkat Kapil (5248616) Gábor Csányi (1900057) Nils Gönnheimer (20420743) Martin Vondrák (20420746) Johannes T. Margraf (1707877) David J. Wales (246398) |
| author2_role | author author author author author author author |
| author_facet | Philipp Pracht (9523414) Yuthika Pillai (20420740) Venkat Kapil (5248616) Gábor Csányi (1900057) Nils Gönnheimer (20420743) Martin Vondrák (20420746) Johannes T. Margraf (1707877) David J. Wales (246398) |
| author_role | author |
| dc.creator.none.fl_str_mv | Philipp Pracht (9523414) Yuthika Pillai (20420740) Venkat Kapil (5248616) Gábor Csányi (1900057) Nils Gönnheimer (20420743) Martin Vondrák (20420746) Johannes T. Margraf (1707877) David J. Wales (246398) |
| dc.date.none.fl_str_mv | 2024-12-12T15:12:04Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jctc.4c01157.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Efficient_Composite_Infrared_Spectroscopy_Combining_the_Double-Harmonic_Approximation_with_Machine_Learning_Potentials/28016746 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biochemistry Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified throughput analytical workflows semiempirical extended tight previous comprehensive assessments diverse data set dipole moment prediction cost quantum mechanical corresponding vibrational modes advancing automated high molecular dipole moment combination squared derivatives efficient computational prediction computational investigation computational efficiency molecular materials molecular dipoles molecular characterization efficient methods unknown compounds systematically tested suitable protocol study investigates study aims reliable identification predictive accuracy particularly focus organic molecules methodical flexibility ideal target harmonic approximation cornerstone technique conventional low composite approach assessed across approach allows accuracy limitations |
| dc.title.none.fl_str_mv | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | 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 molecular 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. |
| eu_rights_str_mv | openAccess |
| id | Manara_cbdee79aeed5dd44581ea24fdcb4975d |
| identifier_str_mv | 10.1021/acs.jctc.4c01157.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28016746 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning PotentialsPhilipp Pracht (9523414)Yuthika Pillai (20420740)Venkat Kapil (5248616)Gábor Csányi (1900057)Nils Gönnheimer (20420743)Martin Vondrák (20420746)Johannes T. Margraf (1707877)David J. Wales (246398)BiophysicsBiochemistrySpace ScienceBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedPhysical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthroughput analytical workflowssemiempirical extended tightprevious comprehensive assessmentsdiverse data setdipole moment predictioncost quantum mechanicalcorresponding vibrational modesadvancing automated highmolecular dipole momentcombination squared derivativesefficient computational predictioncomputational investigationcomputational efficiencymolecular materialsmolecular dipolesmolecular characterizationefficient methodsunknown compoundssystematically testedsuitable protocolstudy investigatesstudy aimsreliable identificationpredictive accuracyparticularly focusorganic moleculesmethodical flexibilityideal targetharmonic approximationcornerstone techniqueconventional lowcomposite approachassessed acrossapproach allowsaccuracy limitationsVibrational 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 molecular 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.2024-12-12T15:12:04ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jctc.4c01157.s001https://figshare.com/articles/dataset/Efficient_Composite_Infrared_Spectroscopy_Combining_the_Double-Harmonic_Approximation_with_Machine_Learning_Potentials/28016746CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/280167462024-12-12T15:12:04Z |
| spellingShingle | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials Philipp Pracht (9523414) Biophysics Biochemistry Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified throughput analytical workflows semiempirical extended tight previous comprehensive assessments diverse data set dipole moment prediction cost quantum mechanical corresponding vibrational modes advancing automated high molecular dipole moment combination squared derivatives efficient computational prediction computational investigation computational efficiency molecular materials molecular dipoles molecular characterization efficient methods unknown compounds systematically tested suitable protocol study investigates study aims reliable identification predictive accuracy particularly focus organic molecules methodical flexibility ideal target harmonic approximation cornerstone technique conventional low composite approach assessed across approach allows accuracy limitations |
| status_str | publishedVersion |
| title | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials |
| title_full | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials |
| title_fullStr | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials |
| title_full_unstemmed | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials |
| title_short | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials |
| title_sort | Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials |
| topic | Biophysics Biochemistry Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified throughput analytical workflows semiempirical extended tight previous comprehensive assessments diverse data set dipole moment prediction cost quantum mechanical corresponding vibrational modes advancing automated high molecular dipole moment combination squared derivatives efficient computational prediction computational investigation computational efficiency molecular materials molecular dipoles molecular characterization efficient methods unknown compounds systematically tested suitable protocol study investigates study aims reliable identification predictive accuracy particularly focus organic molecules methodical flexibility ideal target harmonic approximation cornerstone technique conventional low composite approach assessed across approach allows accuracy limitations |