{"total":46,"rows":[{"id":"2302.04168","title":"Generalizing Neural Wave Functions","created":"2023-02-08","cited_by_count":28,"area":null},{"id":"2401.10190","title":"A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions","created":"2024-01-18","cited_by_count":26,"area":null},{"id":"2308.16848","title":"Accurate Computation of Quantum Excited States with Neural Networks","created":"2023-08-31","cited_by_count":17,"area":null},{"id":"2506.19960","title":"An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking","created":"2025-06-24","cited_by_count":14,"area":null},{"id":"2307.07050","title":"Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schr\\\"odinger Equation","created":"2023-07-06","cited_by_count":12,"area":null},{"id":"2412.08541","title":"Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost","created":"2024-12-11","cited_by_count":12,"area":null},{"id":"2307.09337","title":"Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions","created":"2023-07-15","cited_by_count":9,"area":null},{"id":"2405.14762","title":"Neural Pfaffians: Solving Many Many-Electron Schr\\\"odinger Equations","created":"2024-05-23","cited_by_count":9,"area":null},{"id":"2504.06087","title":"Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems","created":"2025-04-08","cited_by_count":8,"area":null},{"id":"2303.09949","title":"Towards a Foundation Model for Neural Network Wavefunctions","created":"2023-03-17","cited_by_count":7,"area":null},{"id":"2403.07447","title":"Ab-initio variational wave functions for the time-dependent many-electron Schr\\\"odinger equation","created":"2024-03-12","cited_by_count":7,"area":null},{"id":"2405.07599","title":"Transferable Neural Wavefunctions for Solids","created":"2024-05-13","cited_by_count":7,"area":null},{"id":"2307.08214","title":"Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo","created":"2023-07-17","cited_by_count":6,"area":null},{"id":"2405.18874","title":"Are queries and keys always relevant? A case study on Transformer wave functions","created":"2024-05-29","cited_by_count":6,"area":null},{"id":"2409.01306","title":"Highly Accurate Real-space Electron Densities with Neural Networks","created":"2024-09-02","cited_by_count":4,"area":null},{"id":"2603.02346","title":"Large Electron Model: A Universal Ground State Predictor","created":"2026-03-02","cited_by_count":4,"area":null},{"id":"2507.10835","title":"Functional Neural Wavefunction Optimization","created":"2025-07-14","cited_by_count":3,"area":null},{"id":"2602.03927","title":"First-Principles AI finds crystallization of fractional quantum Hall liquids","created":"2026-02-03","cited_by_count":3,"area":null},{"id":"2604.26018","title":"QERNEL: a Scalable Large Electron Model","created":"2026-04-28","cited_by_count":3,"area":null},{"id":"2303.11602","title":"Convergence of variational Monte Carlo simulation and scale-invariant pre-training","created":"2023-03-21","cited_by_count":2,"area":null},{"id":"2403.05249","title":"On Representing Electronic Wave Functions with Sign Equivariant Neural Networks","created":"2024-03-08","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":"2502.05383","title":"Is attention all you need to solve the correlated electron problem?","created":"2025-02-07","cited_by_count":2,"area":null},{"id":"2503.19847","title":"Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo","created":"2025-03-25","cited_by_count":2,"area":null},{"id":"2511.01671","title":"Spin-Adapted Neural Network Wavefunctions in Real Space","created":"2025-11-03","cited_by_count":2,"area":null},{"id":"2311.05598","title":"Sorting Out Quantum Monte Carlo","created":"2023-11-09","cited_by_count":1,"area":null},{"id":"2502.05318","title":"Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schr\\\"odinger Equation","created":"2025-02-07","cited_by_count":1,"area":null},{"id":"2505.12137","title":"Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2511.07367","title":"Reduced Density Matrices Through Machine Learning","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2511.07433","title":"Benchmarking Simulacra AI's Quantum Accurate Synthetic Data Generation for Chemical Sciences","created":"2025-10-30","cited_by_count":1,"area":null},{"id":"2603.14515","title":"Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State","created":"2026-03-15","cited_by_count":1,"area":null},{"id":"2609.23334","title":"Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States","created":"2026-09-20","cited_by_count":1,"area":null},{"id":"2305.16540","title":"A Score-Based Model for Learning Neural Wavefunctions","created":"2023-05-25","cited_by_count":0,"area":null},{"id":"2410.14426","title":"Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2509.06871","title":"Learning spatially structured open quantum dynamics with regional-attention transformers","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2602.09093","title":"Predicting magnetism with first-principles AI","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2603.25381","title":"Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2604.24883","title":"Uncovering Exotic Paired States in the 2D Spin-Imbalanced Fermi Gas with Neural Wave Functions","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2605.05540","title":"Autoregressive One-Step Generative Modeling for Dynamical System Forecasting","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.26009","title":"Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization","created":"2026-06-24","cited_by_count":0,"area":null},{"id":"2609.22342","title":"Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.37733","title":"Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.03463","title":"Generalization of Transformer-Based Neural Quantum States via In-Context Learning","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.12985","title":"NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.17030","title":"Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.18967","title":"Non-Abelian quantum Hall states at strong Landau level mixing: finding anyons with first-principles AI","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}