Transferability of atom-based neural networks
Machine-learning models in chemistry—when based on descriptors of atoms embedded within molecules—face essential challenges in transferring the quality of predictions of local electronic structures and their associated properties across chemical compound space. In the present work, we make use of ad...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ad9709 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846139527258701824 |
|---|---|
| author | Frederik Ø Kjeldal Janus J Eriksen |
| author_facet | Frederik Ø Kjeldal Janus J Eriksen |
| author_sort | Frederik Ø Kjeldal |
| collection | DOAJ |
| description | Machine-learning models in chemistry—when based on descriptors of atoms embedded within molecules—face essential challenges in transferring the quality of predictions of local electronic structures and their associated properties across chemical compound space. In the present work, we make use of adversarial validation to elucidate certain intrinsic complications related to machine inferences of unseen chemistry. On this basis, we employ invariant and equivariant neural networks—both trained either exclusively on total molecular energies or a combination of these and data from atomic partitioning schemes—to evaluate how such models scale performance-wise between datasets of fundamentally different functionality and composition. We find the inference of local electronic properties to improve significantly when training models on augmented data that appropriately expose local functional features. However, molecular datasets for training purposes must themselves be sufficiently comprehensive and rich in composition to warrant any generalizations to larger systems, and even then, transferability can still only genuinely manifest if the body of atomic energies available for training purposes exposes the uniqueness of different functional moieties within molecules. We demonstrate this point by comparing machine models trained on atomic partitioning schemes based on the spatial locality of either native atomic or molecular orbitals. |
| format | Article |
| id | doaj-art-fe88dd4ce639402a835b4b38cdbe6836 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-fe88dd4ce639402a835b4b38cdbe68362024-12-06T10:04:29ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015404505910.1088/2632-2153/ad9709Transferability of atom-based neural networksFrederik Ø Kjeldal0Janus J Eriksen1https://orcid.org/0000-0001-8583-3842DTU Chemistry, Technical University of Denmark , Kemitorvet Bldg. 206, 2800 Kgs. Lyngby, DenmarkDTU Chemistry, Technical University of Denmark , Kemitorvet Bldg. 206, 2800 Kgs. Lyngby, DenmarkMachine-learning models in chemistry—when based on descriptors of atoms embedded within molecules—face essential challenges in transferring the quality of predictions of local electronic structures and their associated properties across chemical compound space. In the present work, we make use of adversarial validation to elucidate certain intrinsic complications related to machine inferences of unseen chemistry. On this basis, we employ invariant and equivariant neural networks—both trained either exclusively on total molecular energies or a combination of these and data from atomic partitioning schemes—to evaluate how such models scale performance-wise between datasets of fundamentally different functionality and composition. We find the inference of local electronic properties to improve significantly when training models on augmented data that appropriately expose local functional features. However, molecular datasets for training purposes must themselves be sufficiently comprehensive and rich in composition to warrant any generalizations to larger systems, and even then, transferability can still only genuinely manifest if the body of atomic energies available for training purposes exposes the uniqueness of different functional moieties within molecules. We demonstrate this point by comparing machine models trained on atomic partitioning schemes based on the spatial locality of either native atomic or molecular orbitals.https://doi.org/10.1088/2632-2153/ad9709neural networkselectronic-structure theoryatomic decomposition schemesatomic energies |
| spellingShingle | Frederik Ø Kjeldal Janus J Eriksen Transferability of atom-based neural networks Machine Learning: Science and Technology neural networks electronic-structure theory atomic decomposition schemes atomic energies |
| title | Transferability of atom-based neural networks |
| title_full | Transferability of atom-based neural networks |
| title_fullStr | Transferability of atom-based neural networks |
| title_full_unstemmed | Transferability of atom-based neural networks |
| title_short | Transferability of atom-based neural networks |
| title_sort | transferability of atom based neural networks |
| topic | neural networks electronic-structure theory atomic decomposition schemes atomic energies |
| url | https://doi.org/10.1088/2632-2153/ad9709 |
| work_keys_str_mv | AT frederikøkjeldal transferabilityofatombasedneuralnetworks AT janusjeriksen transferabilityofatombasedneuralnetworks |