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Found 2 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.
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