Predictive Collective Variable Discovery with Deep Bayesian Models
By M. Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis. 2018
36 citations recorded by the source corpus.
Extending spatio-temporal scale limitations of models for complex atomistic systems considered in biochemistry and materials science necessitates the development of enhanced sampling methods. The potential acceleration in exploring the configurational space by enhanced sampling methods depends on the choice of collective variables (CVs). In this work, we formulate the discovery of CVs as a Bayesian inference problem and consider the CVs as hidden generators of the full-atomistic trajectory. The ability to generate samples of the fine-scale atomistic configurations using limited training data allows us to compute estimates of observables as well as our probabilistic confidence on them. The methodology is based on emerging methodological advances in machine learning and variational inference. The discovered CVs are related to physicochemical properties which are essential for understanding mechanisms especially in unexplored complex systems. We provide a quantitative assessment of the CVs in terms of their predictive ability for alanine dipeptide (ALA-2) and ALA-15 peptide.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Nicholas Geneva
- Nicholas Zabaras
- Sondipon Adhikari
- Anthony Nouy
- Phaedon-Stelios Koutsourelakis
- Constantin Grigo
- Bruno Sudret
- Géraud Blatman
In Materials Chemistry
In Molecular Biology
Citations in the corpus
- Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks (2019, methodology): “Among the most popular deep learning models is deep neural networks (DNNs), which have proven to be an extremely effective method for modeling a wide spectrum of physical systems such as flow through porous media [2, 3, 4], Navier-Stokes equations [5], turbulence modeling [6], molecular dynamics [7]”