Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session T50: Learning Materials Properties and Dynamics with Graph Neural Network ModelsInvited
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Sponsoring Units: DCOMP Chair: Boris Kozinsky, Harvard University Room: Room 320 |
Thursday, March 9, 2023 11:30AM - 12:06PM |
T50.00001: Large-scale equivariant deep learning of atomistic force fields Invited Speaker: Albert Musaelian The physics of atomic systems obeys a set of symmetries, namely permutation, rotation, translation, and inversion. The predictions of machine learning models on these systems should likewise respect those symmetries. In particular, many important properties, such as the potential energy, are invariant under these symmetries: unchanged when the system undergoes a symmetry transform. This invariance has traditionally been enforced by using generic machine learning models whose inputs are restricted to already invariant data featurizations such as interatomic distances or angles. |
Thursday, March 9, 2023 12:06PM - 12:42PM |
T50.00002: Graph Neural Networks for Molecules and Materials Invited Speaker: Johannes Gasteiger A fundamental task of computational physics and chemistry is the calculation of quantum mechanical properties of molecules and materials. Unfortunately, these calculations require substantial computational resources and the involved electronic interactions are not amenable to manually designed approximations. Machine learning and graph neural networks (GNNs) in particular have recently emerged as an effective option to sidesteps manual approximations, enabling high accuracy while being several orders of magnitude faster than traditional methods. |
Thursday, March 9, 2023 12:42PM - 1:18PM |
T50.00003: Learning materials properties and dynamics with graph neural network models Invited Speaker: Gowoon Cheon Machine learning emerged as an important tool in accelerating materials science research. Graph neural networks, in particular, are widely used for modeling molecules and materials as they are capable of learning representations from atomistic structures. I will give a brief introduction to graph neural network models and discuss applications for learning materials properties and dynamics. Future prospects and challenges for modeling materials with graph neural networks will also be discussed. |
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