Elemental frequency-based supervised classification approach for the search of novel topological materials.
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| Title: | Elemental frequency-based supervised classification approach for the search of novel topological materials. |
|---|---|
| Authors: | Ralte, Zodinpuia1 (AUTHOR), Kumar, Ramesh1 (AUTHOR), Singh, Mukhtiyar1 (AUTHOR) msphysik09@gmail.com |
| Source: | Modelling & Simulation in Materials Science & Engineering. 2026, Vol. 34 Issue 3, p1-16. 16p. |
| Subjects: | Supervised learning, Materials science, Support vector machines, Topological insulators, Machine learning, Random forest algorithms |
| Abstract: | The machine learning (ML) based approaches efficiently solve the goal of searching the best materials candidate for the targeted properties. The search for topological materials (TMs) using traditional first-principles and symmetry-based methods often require lots of computing power or is limited by the crystalline symmetries. In this study, we present frequency-based statistical descriptors for ML-driven TM's classification that is independent of crystallographic symmetry of wave functions. This approach predicts the topological nature of a material based on its chemical formula. With a balanced dataset of 3880 materials, we have achieved classification accuracies of 82% with the support vector machine model and 83% with the random forest model, where both models have trained on common frequency based features. We have verified the performances of the models using 5-fold cross-validation approach. Moreover, we have validated the models on a dataset of unseen binary compounds and have efficiently identified 22 common materials using both the models. Next, we used the first-principles approach to provide preliminary evidence for the topological candidates via band inversions at time-reversal invariant momenta points. Therefore, we have demonstrated that the implications of frequency-based descriptors is a practical and less complex way to find novel TMs with certain physical post-processing filters. This approach lays the groundwork for scalable, data-driven topological property screening of complex materials. [ABSTRACT FROM AUTHOR] |
| Copyright of Modelling & Simulation in Materials Science & Engineering is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192349061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Elemental frequency-based supervised classification approach for the search of novel topological materials. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ralte%2C+Zodinpuia%22">Ralte, Zodinpuia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar%2C+Ramesh%22">Kumar, Ramesh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+Mukhtiyar%22">Singh, Mukhtiyar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> msphysik09@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Modelling+%26+Simulation+in+Materials+Science+%26+Engineering%22">Modelling & Simulation in Materials Science & Engineering</searchLink>. 2026, Vol. 34 Issue 3, p1-16. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+science%22">Materials science</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Topological+insulators%22">Topological insulators</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The machine learning (ML) based approaches efficiently solve the goal of searching the best materials candidate for the targeted properties. The search for topological materials (TMs) using traditional first-principles and symmetry-based methods often require lots of computing power or is limited by the crystalline symmetries. In this study, we present frequency-based statistical descriptors for ML-driven TM's classification that is independent of crystallographic symmetry of wave functions. This approach predicts the topological nature of a material based on its chemical formula. With a balanced dataset of 3880 materials, we have achieved classification accuracies of 82% with the support vector machine model and 83% with the random forest model, where both models have trained on common frequency based features. We have verified the performances of the models using 5-fold cross-validation approach. Moreover, we have validated the models on a dataset of unseen binary compounds and have efficiently identified 22 common materials using both the models. Next, we used the first-principles approach to provide preliminary evidence for the topological candidates via band inversions at time-reversal invariant momenta points. Therefore, we have demonstrated that the implications of frequency-based descriptors is a practical and less complex way to find novel TMs with certain physical post-processing filters. This approach lays the groundwork for scalable, data-driven topological property screening of complex materials. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Modelling & Simulation in Materials Science & Engineering is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1088/1361-651X/ae4b5d Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Supervised learning Type: general – SubjectFull: Materials science Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Topological insulators Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: Elemental frequency-based supervised classification approach for the search of novel topological materials. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ralte, Zodinpuia – PersonEntity: Name: NameFull: Kumar, Ramesh – PersonEntity: Name: NameFull: Singh, Mukhtiyar IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 04 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09650393 Numbering: – Type: volume Value: 34 – Type: issue Value: 3 Titles: – TitleFull: Modelling & Simulation in Materials Science & Engineering Type: main |
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