TL;DR
DFDM links noise-dependent denoising with molecular graph quality by integrating complementary local and global representations throughout generation by using signal-to-noise-ratio-guided, layer-specific weights to adapt their contributions during reverse generation.
The convergence mechanism is established as a practical design principle for molecular generative sampling, clarifying when stochastic diffusion provides robustness and when deterministic transport requires higher representational capacity.
Machine-learning-based inverse design can accelerate the discovery of materials with targeted properties, but conventional structure--property models often require large training datasets and generalize poorly beyond their training distribution. Here, we present a differentiable inverse design framework based on a grap...
Molecular dynamics simulations of silica ( SiO2) crystallization remain challenging due to high energy barriers that frequently trap systems in amorphous states. Although experimental synthesis commonly relies on pH-controlled sol–gel routes, computational studies predominantly employ extreme melt-quench protocols. Her...
Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that distant nodes effectively influence each other. Sheaf neural networks enrich graph propagation through matrix-valued transport between stalks; still, this expressivity...
Normalizing Flow Flow Maps (NF$^2$M), which combines the strengths of recent stochastic flow maps with the tractability of classic normalizing flows to build a Boltzmann Generator, and is evaluated on peptide systems, demonstrating improved sampling efficiency and sample quality.
Heterogeneous operators often organize response through hidden barriers, wells, and weakly communicating compartments that are not faithfully described by Euclidean distance or raw graph connectivity alone. We study this structure through the localization landscape, obtained from a single source solve, and the associat...
A Blog post by IBM Research on Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A model developed by researchers at MIT and Boston Children's Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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