🚀 Excited to share our ICLR2026 @iclr_conf work on AI for molecular dynamics generation — a collaboration between Stanford University @Stanford and Lambda @LambdaAPI ! 📄 "Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics" 🔗 Paper: arxiv.org/pdf/2604.03911v1 🧬 Learning molecular dynamics (MD) trajectories is challenging due to the scarcity and high dimensionality of trajectory data. 💡This work proposes EGInterpolator, a framework that leverages structure pretraining to overcome this bottleneck: •🧬Pretrains on large-scale molecular structure datasets •⏳Introduces an equivariant temporal interpolator to model trajectory evolution •🔄Blends structure-based predictions with temporal modeling via learnable mixing •📈Enables more stable and data-efficient trajectory generation Key results: 🚀 Improved trajectory generation quality across QM9 and GEOM-Drugs 🧠 Better modeling of geometric, dynamical, and energetic distributions ⚡ Strong performance gains in forward simulation and interpolation tasks 🔁 Generalizes to peptides and preliminary protein settings Takeaway: ✨Leveraging abundant molecular structures can significantly reduce the difficulty of learning molecular dynamics trajectories. 🙌 Huge shoutout to my Stanford collaborators: @aniketh_iyengar, @jiaqihan99, Pengwei Sun, @MingjianJ , and @StefanoErmon #AI4Science #MolecularDynamics #MD #GenerativeModels #ICLR2026 #neurips #icml #DrugDesign #GeometricDiffusion #StructurePretraining