Machine learning intermolecular transfer integrals with compact atomic cluster representations
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912697612763136 |
|---|---|
| author | Keeratikarn, Keerati Ortner, Christoph Frost, Jarvist Moore |
| author_facet | Keeratikarn, Keerati Ortner, Christoph Frost, Jarvist Moore |
| contents | Calculating intermolecular charge transfer integrals in organic semiconductors requires substantial computer resource for each individual calculation. We might alternatively construct a machine learning model for transfer integrals, which model the full six-degrees of freedom for the relative position of dimer pairs, trained on representative calculations for the molecules of interest. Recent developments have produced effective machine learning force fields, which model the total energy of atomic assemblies. We extend the Atomic Cluster Expansion (ACE) with the correct symmetries for transfer (kinetic-energy) integrals. Combined with a spherical harmonic basis makes, this forms a strong inductive bias and makes for a data efficient model. We introduce coarse-grained and heavy-atom representations, and assess the methodology on representative conjugated semiconductors: ethylene, thiophene, and naphthalene. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06551 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning intermolecular transfer integrals with compact atomic cluster representations Keeratikarn, Keerati Ortner, Christoph Frost, Jarvist Moore Disordered Systems and Neural Networks Materials Science Calculating intermolecular charge transfer integrals in organic semiconductors requires substantial computer resource for each individual calculation. We might alternatively construct a machine learning model for transfer integrals, which model the full six-degrees of freedom for the relative position of dimer pairs, trained on representative calculations for the molecules of interest. Recent developments have produced effective machine learning force fields, which model the total energy of atomic assemblies. We extend the Atomic Cluster Expansion (ACE) with the correct symmetries for transfer (kinetic-energy) integrals. Combined with a spherical harmonic basis makes, this forms a strong inductive bias and makes for a data efficient model. We introduce coarse-grained and heavy-atom representations, and assess the methodology on representative conjugated semiconductors: ethylene, thiophene, and naphthalene. |
| title | Machine learning intermolecular transfer integrals with compact atomic cluster representations |
| topic | Disordered Systems and Neural Networks Materials Science |
| url | https://arxiv.org/abs/2511.06551 |