تقرير
Analyzing multimodal probability measures with autoencoders
| العنوان: | Analyzing multimodal probability measures with autoencoders |
|---|---|
| المؤلفون: | Lelièvre, Tony, Pigeon, Thomas, Stoltz, Gabriel, Zhang, Wei |
| سنة النشر: | 2023 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Physics - Chemical Physics, Condensed Matter - Statistical Mechanics |
| الوصف: | Finding collective variables to describe some important coarse-grained information on physical systems, in particular metastable states, remains a key issue in molecular dynamics. Recently, machine learning techniques have been intensively used to complement and possibly bypass expert knowledge in order to construct collective variables. Our focus here is on neural network approaches based on autoencoders. We study some relevant mathematical properties of the loss function considered for training autoencoders, and provide physical interpretations based on conditional variances and minimum energy paths. We also consider various extensions in order to better describe physical systems, by incorporating more information on transition states at saddle points, and/or allowing for multiple decoders in order to describe several transition paths. Our results are illustrated on toy two dimensional systems and on alanine dipeptide. Comment: 37 pages including 15 figures and 2 appendices |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.03492 |
| رقم الأكسشن: | edsarx.2310.03492 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |