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Rhys Goodall .
Monday 26 July 2021, 16:30-17:00
virtual ZOOM meeting ID: 263 591 6003, Passcode: 000042, https://us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09
Contact: No managers listed
0 upcoming talks View 28 archived talks
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Rapid Discovery of Novel Materials by Coordinate-free Coarse Graining using Wyckoff Representations
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
A transferable active-learning strategy for reactive molecular force fields
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Ranking the information content of distance measures through the information imbalance
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Machine Learning for Molecular Spectra and Solvent Effects
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Through the eyes of a descriptor: Constructing complete, invertible, descriptions of atomic environments
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Learning the molecular grammar of protein condensate formation
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Integrating machine learning and quantum chemistry with fully differentiable quantum chemistry
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Selected applications of machine learning for materials modeling: structural characterization and visualization, van der Waals interactions and X-ray spectroscopy
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Data-Efficient Machine Learning with Chemical and Physical Priors
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Reinforcement Learning for 3D Molecular Design
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
What can we learn from toy models?
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
dNNsolve: an efficient NN-based PDE solver
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Neural Equivariant Interatomic Potentials
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Neural Network Approximations for Calabi-Yau Metrics
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Machine-learning-driven advances in modelling amorphous solids
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
π-Machine Learning for Molecular Crystal Structure Prediction
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Crystal Structure Search with Random Relaxations Using Graph Networks
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Global optimisation of atomistic structure with evolutionary algorithms and reinforcement learning
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Time Symmetries and Neurosymbolic Learning for Dynamical Systems
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Graph Convolutional Networks for Atomic Structures
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Journal club: Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Machine learning assisted accurate potential energy surfaces generation
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
e3nn: A modular PyTorch framework for Euclidean Neural Networks
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Neural Networks with Euclidean Symmetry for Physical Sciences
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Interpretable machine learning for critical evaluation of scientific ML models - the case of reaction prediction
Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
Towards explainable computational biology: small steps and many questions
Please see above for contact details for this list.