{"total":18,"rows":[{"id":"2102.03150","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","created":"2021-02-05","cited_by_count":441,"area":null},{"id":"2305.19302","title":"Smooth, exact rotational symmetrization for deep learning on point clouds","created":"2023-05-30","cited_by_count":39,"area":null},{"id":"2105.14655","title":"Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry","created":"2021-05-31","cited_by_count":30,"area":null},{"id":"2212.05517","title":"SchNetPack 2.0: A neural network toolbox for atomistic machine learning","created":"2022-12-11","cited_by_count":29,"area":null},{"id":"2311.11228","title":"A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems","created":"2023-11-19","cited_by_count":10,"area":null},{"id":"2303.04124","title":"Wigner kernels: body-ordered equivariant machine learning without a basis","created":"2023-03-07","cited_by_count":7,"area":null},{"id":"2206.02789","title":"Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes","created":"2022-06-06","cited_by_count":6,"area":null},{"id":"2206.14087","title":"Electronic-structure properties from atom-centered predictions of the electron density","created":"2022-06-28","cited_by_count":4,"area":null},{"id":"2204.09831","title":"Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space","created":"2022-04-21","cited_by_count":3,"area":null},{"id":"2211.16443","title":"Synthetic data enable experiments in atomistic machine learning","created":"2022-11-29","cited_by_count":3,"area":null},{"id":"2205.15510","title":"Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning","created":"2022-05-31","cited_by_count":1,"area":null},{"id":"2207.08317","title":"Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression","created":"2022-07-17","cited_by_count":1,"area":null},{"id":"2205.05591","title":"Predicting hot-electron free energies from ground-state data","created":"2022-05-11","cited_by_count":0,"area":null},{"id":"2206.12867","title":"Edge Direction-invariant Graph Neural Networks for Molecular Dipole Moments Prediction","created":"2022-06-26","cited_by_count":0,"area":null},{"id":"2410.11391","title":"Benchmarking Data Efficiency in $\\Delta$-ML and Multifidelity Models for Quantum Chemistry","created":"2024-10-15","cited_by_count":0,"area":null},{"id":"2509.16266","title":"Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2605.10458","title":"QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2608.18346","title":"Coupled-cluster molecular properties across the main group that extrapolate beyond training size","created":"2026-08-18","cited_by_count":0,"area":null}],"next_cursor":null}