woshicc/asp: Resolving Chemical-Motif Similarity with Enhanced Atomic Structure Representations for Accurately Predicting Descriptors at Metallic Interfaces
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Recurso digital |
| Publié: |
Zenodo
2025
|
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866902143046254592 |
|---|---|
| author | Cai, Cheng Wang, Tao |
| author_facet | Cai, Cheng Wang, Tao |
| contents | <p>Accurately predicting catalytic descriptors with machine learning (ML) methods is significant to achieving accelerated catalyst design, where a unique representation of the atomic structure of each system is the key to developing a universal, efficient, and accurate ML model that is capable of tackling diverse degrees of complexity in heterogeneous catalysis scenarios. Herein, we integrate equivariant message-passing-enhanced atomic structure representation to resolve chemical-motif similarity in highly complex catalytic systems. Our developed equivariant graph neural network (equivGNN) model achieves mean absolute errors < 0.09 eV for different descriptors at metallic interfaces, including complex adsorbates with more diverse adsorption motifs on ordered catalyst surfaces, adsorption motifs on highly disordered surfaces of high-entropy alloys, and the complex structures of supported nanoparticles.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16713880 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | woshicc/asp: Resolving Chemical-Motif Similarity with Enhanced Atomic Structure Representations for Accurately Predicting Descriptors at Metallic Interfaces Cai, Cheng Wang, Tao <p>Accurately predicting catalytic descriptors with machine learning (ML) methods is significant to achieving accelerated catalyst design, where a unique representation of the atomic structure of each system is the key to developing a universal, efficient, and accurate ML model that is capable of tackling diverse degrees of complexity in heterogeneous catalysis scenarios. Herein, we integrate equivariant message-passing-enhanced atomic structure representation to resolve chemical-motif similarity in highly complex catalytic systems. Our developed equivariant graph neural network (equivGNN) model achieves mean absolute errors < 0.09 eV for different descriptors at metallic interfaces, including complex adsorbates with more diverse adsorption motifs on ordered catalyst surfaces, adsorption motifs on highly disordered surfaces of high-entropy alloys, and the complex structures of supported nanoparticles.</p> |
| title | woshicc/asp: Resolving Chemical-Motif Similarity with Enhanced Atomic Structure Representations for Accurately Predicting Descriptors at Metallic Interfaces |
| url | https://doi.org/10.5281/zenodo.16713880 |