Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data
| Title: | Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data |
|---|---|
| Authors: | Shanks, Brennon L., Sullivan, Harry W., Hoepfner, Michael P. |
| Publication Year: | 2024 |
| Collection: | Condensed Matter |
| Subject Terms: | Condensed Matter - Statistical Mechanics, Condensed Matter - Soft Condensed Matter |
| More Details: | The inverse problem of statistical mechanics is an unsolved, century-old challenge to learn classical pair potentials directly from experimental scattering data. This problem was extensively investigated in the 20th century but was eventually eclipsed by standard methods of benchmarking pair potentials to macroscopic thermodynamic data. However, it is becoming increasingly clear that existing force field models fail to reliably reproduce fluid structures even in simple liquids, which can result in reduced transferability and substantial misrepresentations of thermophysical behavior and self-assembly. In this study, we revisited the structure inverse problem for a classical Mie fluid to determine to what extent experimental uncertainty in neutron scattering data influences the ability to recover classical pair potentials. Bayesian uncertainty quantification was used to show that structure factors with random noise smaller than 0.005 to $\sim30$ A$^{-1}$ are required to accurately recover pair potentials from neutron scattering. Notably, modern neutron instruments can achieve this precision to extract classical force models to within approximately $\pm$ 1.3 for the repulsive exponent, $\pm$ 0.068 A$^{-1}$ for atomic size, and 0.024 kcal/mol in the potential well-depth with 95\% confidence. Our results suggest the exciting possibility of improving molecular simulation accuracy through the incorporation of neutron scattering data, advancement in structural modeling, and extraction of model-independent measurements of local atomic forces in real fluids. |
| Document Type: | Working Paper |
| DOI: | 10.1021/acs.jpclett.4c02941 |
| Access URL: | http://arxiv.org/abs/2407.04839 |
| Accession Number: | edsarx.2407.04839 |
| Database: | arXiv |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shanks%2C+Brennon+L%2E%22">Shanks, Brennon L.</searchLink><br /><searchLink fieldCode="AR" term="%22Sullivan%2C+Harry+W%2E%22">Sullivan, Harry W.</searchLink><br /><searchLink fieldCode="AR" term="%22Hoepfner%2C+Michael+P%2E%22">Hoepfner, Michael P.</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: Condensed Matter – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Condensed+Matter+-+Statistical+Mechanics%22">Condensed Matter - Statistical Mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Condensed+Matter+-+Soft+Condensed+Matter%22">Condensed Matter - Soft Condensed Matter</searchLink> – Name: Abstract Label: Description Group: Ab Data: The inverse problem of statistical mechanics is an unsolved, century-old challenge to learn classical pair potentials directly from experimental scattering data. This problem was extensively investigated in the 20th century but was eventually eclipsed by standard methods of benchmarking pair potentials to macroscopic thermodynamic data. However, it is becoming increasingly clear that existing force field models fail to reliably reproduce fluid structures even in simple liquids, which can result in reduced transferability and substantial misrepresentations of thermophysical behavior and self-assembly. In this study, we revisited the structure inverse problem for a classical Mie fluid to determine to what extent experimental uncertainty in neutron scattering data influences the ability to recover classical pair potentials. Bayesian uncertainty quantification was used to show that structure factors with random noise smaller than 0.005 to $\sim30$ A$^{-1}$ are required to accurately recover pair potentials from neutron scattering. Notably, modern neutron instruments can achieve this precision to extract classical force models to within approximately $\pm$ 1.3 for the repulsive exponent, $\pm$ 0.068 A$^{-1}$ for atomic size, and 0.024 kcal/mol in the potential well-depth with 95\% confidence. Our results suggest the exciting possibility of improving molecular simulation accuracy through the incorporation of neutron scattering data, advancement in structural modeling, and extraction of model-independent measurements of local atomic forces in real fluids. – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: DOI Label: DOI Group: ID Data: 10.1021/acs.jpclett.4c02941 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2407.04839" linkWindow="_blank">http://arxiv.org/abs/2407.04839</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2407.04839 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1021/acs.jpclett.4c02941 Subjects: – SubjectFull: Condensed Matter - Statistical Mechanics Type: general – SubjectFull: Condensed Matter - Soft Condensed Matter Type: general Titles: – TitleFull: Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shanks, Brennon L. – PersonEntity: Name: NameFull: Sullivan, Harry W. – PersonEntity: Name: NameFull: Hoepfner, Michael P. IsPartOfRelationships: – BibEntity: Dates: – D: 05 M: 07 Type: published Y: 2024 |
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