Report
Machine Learning of coarse-grained Molecular Dynamics Force Fields
| العنوان: | Machine Learning of coarse-grained Molecular Dynamics Force Fields |
|---|---|
| المؤلفون: | Wang, Jiang, Olsson, Simon, Wehmeyer, Christoph, Perez, Adria, Charron, Nicholas E., de Fabritiis, Gianni, Noe, Frank, Clementi, Cecilia |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Physics (Other) Statistics |
| مصطلحات موضوعية: | Physics - Computational Physics, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multi-body terms that emerge from the dimensionality reduction. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1812.01736 |
| رقم الانضمام: | edsarx.1812.01736 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |