quantum chemistry

Chemistry-Inspired Diffusion with Non-Differentiable Guidance ICLR 2025 Machine Learning Driven Optimization of Fe-Based TMCs for Photodynamic Therapy IJCAI 2025 Curriculum reinforcement learning for quantum architecture search under hardware errors ICLR 2024 Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models NeurIPS 2024 Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations NeurIPS 2024 Quadratic Quantum Variational Monte Carlo NeurIPS 2024 Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz NeurIPS 2024 Structured Chemistry Reasoning with Large Language Models ICML 2024 A Self-Attention Ansatz for Ab-initio Quantum Chemistry ICLR 2023 Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian ICML 2023 Generalizing Neural Wave Functions ICML 2023 Generating QM1B with PySCF_IPU NeurIPS 2023 GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising NeurIPS 2023 Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions NeurIPS 2023 Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? NeurIPS 2022 So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems NeurIPS 2022 Uncertainty-aware pseudo-labeling for quantum calculations UAI 2022 SE(3)-equivariant prediction of molecular wavefunctions and electronic densities NeurIPS 2021 On the equivalence of molecular graph convolution and molecular wave function with poor basis set NeurIPS 2020 Reinforcement Learning for Molecular Design Guided by Quantum Mechanics ICML 2020 Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective AAAI 2019 Neural Message Passing for Quantum Chemistry ICML 2017 SchNet: A continuous-filter convolutional neural network for modeling quantum interactions NeurIPS 2017