Force fields and folding models are merging: physics-informed latent spaces
TimothyDuignan · x · 2026-10-22
Timothy Duignan highlights NVIDIA's work merging force fields with folding models. The latent space is physics-informed: training uses physics-based synthetic PDB with glycan conformers plus DFT small molecules from OMol, and the autoencoder is supervised via force-field backpropagation through TMol. New tasks then only need new input conditions.
More from Research
- Domingos Cites New Result Claiming RL Is 'Basically Useless' vs Supervised Learning — pmddomingos · 2026-10-23
- Wolfram Opens Rule 30 Prizes to AIs: $30,000 for Three Open Problems — cephaloform · 2026-10-23
- Stanford and Databricks use AI agents to reproduce medical findings and test analytical robustness — ChengleiSi · 2026-10-23
- EMNLP paper: diffusion LMs internally encode latent denoising timestep representations — gsarti_ · 2026-10-23
- KU Leuven and VIB open two Group Leader professorships in AI for Biology, start 2027 — jpillowtime · 2026-10-23
- Subspace Tuning curbs LLM catastrophic forgetting by freezing principal singular directions — scottniekum · 2026-10-23