Hyperactive learning for data-driven interatomic potentials Article Swipe
Cas van der Oord
,
Matthias Sachs
,
David Kovacs
,
Christoph Ortner
,
Gábor Cśanyi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2248548/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2248548/v1
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for ab initio potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accurate and transferable potentials the most time-consuming and arguably most important task is generating the training set, which still requires significant expert user input. To accelerate this process, this work presents hyperactive learning (HAL), a framework for formulating an accelerated sampling algorithm specifically for the task of training database generation. The key idea is to start from a physically motivated sampler (e.g., molecular dynamics) and add a biasing term that drives the system towards high uncertainty and thus to unseen training configurations. Building on this framework, general protocols for building training databases for alloys and polymers leveraging the HAL framework will be presented. For alloys, ACE potentials for AlSi10 are created by fitting to a minimal HAL-generated database containing 88 configurations (32 atoms each) with fast evaluation times of <100 μs/atom/cpu-core. These potentials are demonstrated to predict the melting temperature with excellent accuracy. For polymers, a HAL database is built using ACE, able to determine the density of a long polyethylene glycol (PEG) polymer formed of 200 monomer units with experimental accuracy by only fitting to small isolated PEG polymers with sizes ranging from 2 to 32.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2248548/v1
- https://www.researchsquare.com/article/rs-2248548/latest.pdf
- OA Status
- green
- Cited By
- 11
- References
- 85
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309390910
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4309390910Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2248548/v1Digital Object Identifier
- Title
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Hyperactive learning for data-driven interatomic potentialsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-18Full publication date if available
- Authors
-
Cas van der Oord, Matthias Sachs, David Kovacs, Christoph Ortner, Gábor CśanyiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2248548/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-2248548/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-2248548/latest.pdfDirect OA link when available
- Concepts
-
Interatomic potential, Computer science, Psychology, Physics, Molecular dynamics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2023: 8Per-year citation counts (last 5 years)
- References (count)
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85Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2022-11-18 |
| publication_year | 2022 |
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