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Found 11 papers, 5 papers with code
A survey of probabilistic generative frameworks for molecular simulations
In a nutshell, (i) Neural Spline Flows do best at capturing mode asymmetry present in low-dimensional data, (ii) Conditional Flow Matching outperforms other models for high-dimensional data with low complexity, and (iii) Denoising Diffusion Probabilistic Models appears the best for low-dimensional data with high complexity.
Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics
Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations.
Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena
We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry.
Enhanced sampling of Crystal Nucleation with Graph Representation Learnt Variables
This underscores the strength and promise of our graph neural net variables for improved sampling.
Enhanced Sampling with Machine Learning: A Review
Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe time-scale limitations.
From latent dynamics to meaningful representations
We show this is a more natural constraint for representation learning in stochastic dynamical systems, with the crucial ability to uniquely identify the ground truth representation.
Thermodynamics-inspired Explanations of Artificial Intelligence
To demonstrate the wide-ranging applicability of TERP, we successfully employ it to explain various black-box model architectures, including deep learning Autoencoders, Recurrent Neural Networks, and Convolutional Neural Networks, across diverse domains such as molecular simulations, text, and image classification.
Path sampling of recurrent neural networks by incorporating known physics
Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system.
Learning Molecular Dynamics with Simple Language Model built upon Long Short-Term Memory Neural Network
We anticipate that our work represents a stepping stone in the understanding and use of RNNs for modeling and predicting dynamics of complex stochastic molecular systems.
Disordered Systems and Neural Networks Statistical Mechanics Chemical Physics Data Analysis, Statistics and Probability
Understanding the role of predictive time delay and biased propagator in RAVE
We demonstrate through a master equation framework as to why the exact choice of time-delay is irrelevant as long as a small non-zero value is adopted.
Statistical Mechanics Chemical Physics Computational Physics
Machine learning approaches for analyzing and enhancing molecular dynamics simulations
Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software.
Computational Physics Biological Physics Chemical Physics
