Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910151636680704 |
|---|---|
| author | Eastman, Peter Pretti, Evan Markland, Thomas E. |
| author_facet | Eastman, Peter Pretti, Evan Markland, Thomas E. |
| contents | The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations, and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, but observe no benefit from including explicit Coulomb energy terms. Speed and memory use are determined as much by the model architecture as by the size of the model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16331 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations Eastman, Peter Pretti, Evan Markland, Thomas E. Chemical Physics The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations, and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, but observe no benefit from including explicit Coulomb energy terms. Speed and memory use are determined as much by the model architecture as by the size of the model. |
| title | Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2601.16331 |