Today we’re unveiling Odyssey-3, a big step forward for foundation world models.
It can control robots, power humanoids, drive cars (on the roads of India!), train AIs, pilot drones, and even play video games.
We can’t wait to see what intelligent systems it enables.
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Agents can now build accurate geometry, but struggle to create feature trees engineers can maintain and edit
polyGen - A Learning Framework for Atomic-level Polymer Structure Generation
1. polyGen introduces a groundbreaking latent diffusion model tailored for the generation of realistic polymer structures, addressing the challenge of designing synthetic polymers with diverse conformations based on minimal input like repeat unit chemistry.
2. This framework stands out by integrating a molecular encoding that captures polymer connectivity, enabling it to predict a wide array of conformations, including both linear chains and complex branched structures.
3. polyGen overcomes the limitation of existing polymer generation models by utilizing a diffusion-based architecture to learn polymer conformations, extending beyond traditional DFT-optimized molecular structures to generate a variety of realistic 3D atomic configurations.
4. The model's innovative approach involves using a latent space that encodes chemical connectivity, which allows polyGen to generate diverse conformations of polymers even from small datasets, with a strong focus on capturing the low-energy conformational diversity inherent to polymer structures.
5. Results demonstrate that polyGen can successfully replicate the bond lengths, angles, and dihedral distributions of polymer structures, providing a robust tool for polymer design in material science, energy storage, and electronics applications.
6. One of the key strengths of polyGen is its ability to generate polymer ensembles with multiple diverse structures, offering a more accurate representation of polymer behavior compared to single-conformation approaches.
7. The model also benchmarks its performance using a unique evaluation method grounded in first principles, comparing the predicted polymer structures to ground truth data derived from Density Functional Theory (DFT) optimized structures.
8. Despite its success in generating accurate structures for smaller polymer systems, the model's performance shows challenges with larger repeat units and highly flexible chains, highlighting the need for further refinement to scale up its capabilities.
9. polyGen represents a significant step forward in computational materials science by enabling the rapid, on-demand generation of synthetic polymer structures, potentially accelerating the discovery of novel materials.
💻Code: https://t.co/OanqfezkBS
📜Paper: https://t.co/L1zFzI5R9h
#PolymerScience #DeepLearning #MaterialsScience #GenerativeModels #AIinMaterials #DiffusionModels #SyntheticPolymers #ComputationalDesign