{"total":16,"rows":[{"id":"2604.09130","title":"EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers","created":"2026-04-10","cited_by_count":11,"area":null},{"id":"2508.16067","title":"Training a Foundation Model for Materials on a Budget","created":"2025-08-22","cited_by_count":6,"area":null},{"id":"2509.14961","title":"Spectral/Spatial Tensor Atomic Cluster Expansion with Universal Embeddings in Cartesian Space","created":"2025-09-18","cited_by_count":6,"area":null},{"id":"2601.07742","title":"PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials","created":"2026-01-12","cited_by_count":6,"area":null},{"id":"2512.16882","title":"A Cartesian-3j Framework for Machine Learning Interatomic Potentials","created":"2025-12-18","cited_by_count":3,"area":null},{"id":"2607.02499","title":"Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials","created":"2026-07-02","cited_by_count":3,"area":null},{"id":"2601.21147","title":"Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials","created":"2026-01-29","cited_by_count":2,"area":null},{"id":"2605.05746","title":"Polarizable atomic multipoles for learning long-range electrostatics","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2605.08960","title":"CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models","created":"2026-05-09","cited_by_count":2,"area":null},{"id":"2512.16702","title":"How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?","created":"2025-12-18","cited_by_count":1,"area":null},{"id":"2508.10841","title":"Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2511.04243","title":"Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2605.08885","title":"Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2606.18691","title":"Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2609.39090","title":"A strategic roadmap for an atomistic machine-learning ecosystem","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.02013","title":"BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}