Showing 1–20 of 23 results
/ Date/ Name
Mar 2, 2021NavTuner: Learning a Scene-Sensitive Family of Navigation PoliciesAug 16, 2017ANI-1: A data set of 20M off-equilibrium DFT calculations for organic moleculesMay 2, 2025Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you needMar 10, 2025Does Hessian Data Improve the Performance of Machine Learning Potentials?Mar 10, 2020Automated discovery of a robust interatomic potential for aluminumOct 27, 2016ANI-1: An extensible neural network potential with DFT accuracy at force field computational costFeb 7, 2025Teacher-student training improves accuracy and efficiency of machine learning interatomic potentialsAug 23, 2020Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAMAug 19, 2019Autonomous, Monocular, Vision-Based Snake Robot Navigation and Traversal of Cluttered Environments using Rectilinear Gait MotionSep 27, 2019Machine Learned Hückel Theory: Interfacing Physics and Deep Neural NetworksMar 12, 2018Transferable Molecular Charge Assignment Using Deep Neural NetworksSep 13, 2025Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning PotentialsMar 4, 2026Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic PotentialsMar 3, 2020Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking AccuracyApr 14, 2025Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACEJun 9, 2020Simple and efficient algorithms for training machine learning potentials to force dataNov 5, 2018Machine learning for molecular dynamics with strongly correlated electronsJul 6, 2021Quantum-based Molecular Dynamics Simulations Using Tensor CoresSep 29, 2017Hierarchical modeling of molecular energies using a deep neural networkJan 28, 2018Less is more: sampling chemical space with active learning