The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
Fuente:
arXiv
Saved in:
| Main Authors: | Levine, Daniel S., Shuaibi, Muhammed, Spotte-Smith, Evan Walter Clark, Taylor, Michael G., Hasyim, Muhammad R., Michel, Kyle, Batatia, Ilyes, Csányi, Gábor, Dzamba, Misko, Eastman, Peter, Frey, Nathan C., Fu, Xiang, Gharakhanyan, Vahe, Krishnapriyan, Aditi S., Rackers, Joshua A., Raja, Sanjeev, Rizvi, Ammar, Rosen, Andrew S., Ulissi, Zachary, Vargas, Santiago, Zitnick, C. Lawrence, Blau, Samuel M., Wood, Brandon M. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
by: Barroso-Luque, Luis, et al.
Published: (2024)
by: Barroso-Luque, Luis, et al.
Published: (2024)
UMA: A Family of Universal Models for Atoms
by: Wood, Brandon M., et al.
Published: (2025)
by: Wood, Brandon M., et al.
Published: (2025)
Open Molecular Crystals 2025 (OMC25) Dataset and Models
by: Gharakhanyan, Vahe, et al.
Published: (2025)
by: Gharakhanyan, Vahe, et al.
Published: (2025)
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
by: Wander, Brook, et al.
Published: (2024)
by: Wander, Brook, et al.
Published: (2024)
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
by: Gharakhanyan, Vahe, et al.
Published: (2025)
by: Gharakhanyan, Vahe, et al.
Published: (2025)
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
by: Fu, Xiang, et al.
Published: (2025)
by: Fu, Xiang, et al.
Published: (2025)
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
by: Grega, Ivan, et al.
Published: (2024)
by: Grega, Ivan, et al.
Published: (2024)
The Open Catalyst 2025 (OC25) Dataset and Models for Solid-Liquid Interfaces
by: Sahoo, Sushree Jagriti, et al.
Published: (2025)
by: Sahoo, Sushree Jagriti, et al.
Published: (2025)
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
by: Baldwin, William J., et al.
Published: (2026)
by: Baldwin, William J., et al.
Published: (2026)
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
by: Amin, Ishan, et al.
Published: (2025)
by: Amin, Ishan, et al.
Published: (2025)
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
by: Qu, Eric, et al.
Published: (2026)
by: Qu, Eric, et al.
Published: (2026)
Equivariant Matrix Function Neural Networks
by: Batatia, Ilyes, et al.
Published: (2023)
by: Batatia, Ilyes, et al.
Published: (2023)
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
by: Raja, Sanjeev, et al.
Published: (2024)
by: Raja, Sanjeev, et al.
Published: (2024)
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
by: Goel, Divyam, et al.
Published: (2026)
by: Goel, Divyam, et al.
Published: (2026)
Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
by: Nelson, Kai, et al.
Published: (2026)
by: Nelson, Kai, et al.
Published: (2026)
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026)
by: Liu, Ryan, et al.
Published: (2026)
All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
by: Joshi, Chaitanya K., et al.
Published: (2025)
by: Joshi, Chaitanya K., et al.
Published: (2025)
Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
by: Kreiman, Tobias, et al.
Published: (2025)
by: Kreiman, Tobias, et al.
Published: (2025)
EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
by: Du, Yiheng, et al.
Published: (2025)
by: Du, Yiheng, et al.
Published: (2025)
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
by: Qu, Eric, et al.
Published: (2024)
by: Qu, Eric, et al.
Published: (2024)
Captive seawater fishes: science and technology / Stephen Spotte
by: Spotte, Stephen
by: Spotte, Stephen
Marine aquarium keeping / Stephen Spotte
by: Spotte, Stephen
Published: (1993)
by: Spotte, Stephen
Published: (1993)
Zero Shot Molecular Generation via Similarity Kernels
by: Elijošius, Rokas, et al.
Published: (2024)
by: Elijošius, Rokas, et al.
Published: (2024)
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
by: Abed, Jehad, et al.
Published: (2024)
by: Abed, Jehad, et al.
Published: (2024)
Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential
by: Kumar, Nitesh, et al.
Published: (2026)
by: Kumar, Nitesh, et al.
Published: (2026)
Genetic Algorithm-Accelerated Computational Discovery of Liquid Crystal Polymers with Enhanced Optical Properties
by: Zhou, Jianing, et al.
Published: (2025)
by: Zhou, Jianing, et al.
Published: (2025)
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)
by: Stark, Wojciech G., et al.
Published: (2024)
Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields
by: Batatia, Ilyes, et al.
Published: (2025)
by: Batatia, Ilyes, et al.
Published: (2025)
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
by: Luo, Shengjie, et al.
Published: (2024)
by: Luo, Shengjie, et al.
Published: (2024)
Neural Spectral Methods: Self-supervised learning in the spectral domain
by: Du, Yiheng, et al.
Published: (2023)
by: Du, Yiheng, et al.
Published: (2023)
Scaling physics-informed hard constraints with mixture-of-experts
by: Chalapathi, Nithin, et al.
Published: (2024)
by: Chalapathi, Nithin, et al.
Published: (2024)
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
by: Raja, Sanjeev, et al.
Published: (2025)
by: Raja, Sanjeev, et al.
Published: (2025)
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
by: Kaur, Harveen, et al.
Published: (2024)
by: Kaur, Harveen, et al.
Published: (2024)
The Open Polymers 2026 (OPoly26) Dataset and Evaluations
by: Levine, Daniel S., et al.
Published: (2025)
by: Levine, Daniel S., et al.
Published: (2025)
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
by: Gruver, Nate, et al.
Published: (2024)
by: Gruver, Nate, et al.
Published: (2024)
BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
by: Schaaf, Lars L., et al.
Published: (2024)
by: Schaaf, Lars L., et al.
Published: (2024)
Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations
by: Wallace, Ewan R. S., et al.
Published: (2025)
by: Wallace, Ewan R. S., et al.
Published: (2025)
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
by: Liao, Yi-Lun, et al.
Published: (2024)
by: Liao, Yi-Lun, et al.
Published: (2024)
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
by: Jian, Yue, et al.
Published: (2024)
by: Jian, Yue, et al.
Published: (2024)
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
by: Shoghi, Nima, et al.
Published: (2023)
by: Shoghi, Nima, et al.
Published: (2023)
Similar Items
-
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
by: Barroso-Luque, Luis, et al.
Published: (2024) -
UMA: A Family of Universal Models for Atoms
by: Wood, Brandon M., et al.
Published: (2025) -
Open Molecular Crystals 2025 (OMC25) Dataset and Models
by: Gharakhanyan, Vahe, et al.
Published: (2025) -
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
by: Wander, Brook, et al.
Published: (2024) -
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
by: Gharakhanyan, Vahe, et al.
Published: (2025)