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Found 3 papers, 1 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.
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 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.
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