تقرير
Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks
| العنوان: | Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks |
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| المؤلفون: | Ashby, Michael Hunter, Bilbrey, Jenna A. |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Physics - Chemical Physics |
| الوصف: | We apply a temporal edge prediction model for weighted dynamic graphs to predict time-dependent changes in molecular structure. Each molecule is represented as a complete graph in which each atom is a vertex and all vertex pairs are connected by an edge weighted by the Euclidean distance between atom pairs. We ingest a sequence of complete molecular graphs into a dynamic graph neural network (GNN) to predict the graph at the next time step. Our dynamic GNN predicts atom-to-atom distances with a mean absolute error of 0.017 \r{A}, which is considered ``chemically accurate'' for molecular simulations. We also explored the transferability of a trained network to new molecular systems and found that finetuning with less than 10% of the total trajectory provides a mean absolute error of the same order of magnitude as that when training from scratch on the full molecular trajectory. Comment: 11 pages, 4 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2106.13277 |
| رقم الأكسشن: | edsarx.2106.13277 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |