{"total":24,"rows":[{"id":"2401.10216","title":"Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products","created":"2024-01-18","cited_by_count":36,"area":null},{"id":"2502.19227","title":"Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems","created":"2025-02-26","cited_by_count":14,"area":null},{"id":"2403.09560","title":"Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction","created":"2024-03-14","cited_by_count":12,"area":null},{"id":"2502.01171","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","created":"2025-02-03","cited_by_count":11,"area":null},{"id":"2505.18817","title":"High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction","created":"2025-05-24","cited_by_count":11,"area":null},{"id":"2510.00224","title":"Learning from the electronic structure of molecules across the periodic table","created":"2025-09-30","cited_by_count":7,"area":null},{"id":"2406.03794","title":"Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models","created":"2024-06-06","cited_by_count":6,"area":null},{"id":"2411.04219","title":"Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction","created":"2024-11-06","cited_by_count":6,"area":null},{"id":"2506.09398","title":"Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames","created":"2025-06-11","cited_by_count":6,"area":null},{"id":"2402.04030","title":"Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory","created":"2024-02-06","cited_by_count":4,"area":null},{"id":"2405.05722","title":"TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction","created":"2024-05-09","cited_by_count":4,"area":null},{"id":"2509.25724","title":"Towards A Transferable Acceleration Method for Density Functional Theory","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2604.21657","title":"Transferable SCF-Acceleration through Solver-Aligned Initialization Learning","created":"2026-04-23","cited_by_count":3,"area":null},{"id":"2402.10186","title":"Self-consistent Validation for Machine Learning Electronic Structure","created":"2024-02-15","cited_by_count":2,"area":null},{"id":"2406.14347","title":"$\\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials","created":"2024-06-20","cited_by_count":2,"area":null},{"id":"2505.09262","title":"EDBench: Large-Scale Electron Density Data for Molecular Modeling","created":"2025-05-14","cited_by_count":2,"area":null},{"id":"2509.15193","title":"TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE","created":"2025-09-18","cited_by_count":2,"area":null},{"id":"2509.19877","title":"Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2503.24111","title":"Inductive Graph Representation Learning with Quantum Graph Neural Networks","created":"2025-03-31","cited_by_count":1,"area":null},{"id":"2602.09309","title":"How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science","created":"2026-02-10","cited_by_count":1,"area":null},{"id":"2406.00441","title":"Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks","created":"2024-06-01","cited_by_count":0,"area":null},{"id":"2605.05476","title":"A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2606.14498","title":"A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2606.28911","title":"MALOQ: Massively Accelerated Learning of Operators for Quantum Transport","created":"2026-06-27","cited_by_count":0,"area":null}],"next_cursor":null}