Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data
In this work, we utilize computationally efficient molecular dynamics simulations to create a machine learning pipeline for discovery of crystalline multi-component alloys. We employ high-quality interatomic potentials to create a dataset of NiFeCr structures and apply crystal diffusion variational...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/addf0f |
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| author | Amirhossein D Naghdi Grzegorz Kaszuba Stefanos Papanikolaou Andrzej Jaszkiewicz Piotr Sankowski |
| author_facet | Amirhossein D Naghdi Grzegorz Kaszuba Stefanos Papanikolaou Andrzej Jaszkiewicz Piotr Sankowski |
| author_sort | Amirhossein D Naghdi |
| collection | DOAJ |
| description | In this work, we utilize computationally efficient molecular dynamics simulations to create a machine learning pipeline for discovery of crystalline multi-component alloys. We employ high-quality interatomic potentials to create a dataset of NiFeCr structures and apply crystal diffusion variational autoencoder to maximize their mechanical properties, i.e. bulk modulus. As part of the experiment, we utilize local search coupled with classical interatomic potentials to explore the local structure space and show that utilization of this procedure greatly improves optimization capability of the neural model. We also expand the model with an extra submodule, which attains 42% improvement on modeling the crystalline phase of the structures. Ultimately, we verify the global stability of the created structures with quantum mechanical calculation methods. |
| format | Article |
| id | doaj-art-3b0e49ec01de4718a5a4ebe76300cd8b |
| institution | DOAJ |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-3b0e49ec01de4718a5a4ebe76300cd8b2025-08-20T03:11:03ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202505510.1088/2632-2153/addf0fEfficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics dataAmirhossein D Naghdi0https://orcid.org/0000-0002-2709-8111Grzegorz Kaszuba1https://orcid.org/0000-0002-8993-1703Stefanos Papanikolaou2https://orcid.org/0000-0001-5239-1275Andrzej Jaszkiewicz3Piotr Sankowski4NOMATEN Centre of Excellence , Warsaw, PolandIDEAS NCBR , Warsaw, Poland; Poznań University of Technology , Poznań, PolandNOMATEN Centre of Excellence , Warsaw, PolandPoznań University of Technology , Poznań, PolandUniversity of Warsaw , Warsaw, Poland; MIM Solutions , Warsaw, PolandIn this work, we utilize computationally efficient molecular dynamics simulations to create a machine learning pipeline for discovery of crystalline multi-component alloys. We employ high-quality interatomic potentials to create a dataset of NiFeCr structures and apply crystal diffusion variational autoencoder to maximize their mechanical properties, i.e. bulk modulus. As part of the experiment, we utilize local search coupled with classical interatomic potentials to explore the local structure space and show that utilization of this procedure greatly improves optimization capability of the neural model. We also expand the model with an extra submodule, which attains 42% improvement on modeling the crystalline phase of the structures. Ultimately, we verify the global stability of the created structures with quantum mechanical calculation methods.https://doi.org/10.1088/2632-2153/addf0fgenerative AIhigh entropy alloyscomposition optimizationmolecular dynamics |
| spellingShingle | Amirhossein D Naghdi Grzegorz Kaszuba Stefanos Papanikolaou Andrzej Jaszkiewicz Piotr Sankowski Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data Machine Learning: Science and Technology generative AI high entropy alloys composition optimization molecular dynamics |
| title | Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data |
| title_full | Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data |
| title_fullStr | Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data |
| title_full_unstemmed | Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data |
| title_short | Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data |
| title_sort | efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data |
| topic | generative AI high entropy alloys composition optimization molecular dynamics |
| url | https://doi.org/10.1088/2632-2153/addf0f |
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