Sasank Edara
Hamiltonian Flow Maps Learn Larger Molecular Dynamics Steps Without Trajectories
Michael Plainer, Winfried Ripken and Gregor Lied argue that generative models can attack molecular dynamics’ central bottleneck: the gap between femtosecond integration steps and biological processes that unfold many orders of magnitude later. In the Microsoft Research seminar, they separate the problem by timescale, using diffusion models to sample equilibrium Boltzmann states and extract force information, while proposing Hamiltonian flow maps for the intermediate regime where simulations need large, stable steps without training on expensive future-state trajectories.
Split-Flows Make Mapping Entropy Computable for Molecular Coarse-Graining
Tristan Bereau presents Split-Flows, a flow-based method for connecting atomistic and coarse-grained molecular representations by adding explicit noise variables for the degrees of freedom lost under coarse-graining. The argument is that this augmentation turns a many-to-one mapping into a tractable coordinate transform, enabling both generative backmapping and computation of configuration-dependent mapping entropy. Bereau says the approach makes information loss measurable for complex molecular systems, though it depends on a differentiable bijective construction and still faces scaling costs.
Diffusion Models Generate Images Through Critical Instability Windows
Luca Ambrogioni argues that trained diffusion models generate images through brief instability windows rather than uniform step-by-step denoising. In a Microsoft Research generative modeling seminar, he links score dynamics, conditional entropy and statistical-physics phase transitions to show how low-frequency spatial modes soften at critical times, allowing noise to organize into coherent structure. Experiments on patch models, Fashion-MNIST and ImageNet models are presented as evidence that these critical windows govern both pattern formation and the timing of effective guidance.