6 papers
LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
Siddharth Betala, Samuel P. Gleason, Ali Ramlaoui +12
Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented explorat…
Energy Loss Functions for Physical Systems
Sékou-Oumar Kaba, Kusha Sareen, Daniel Levy +1
Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous approaches mostly focused on incorporat…
Quantitative Bounds for Sorting-Based Permutation-Invariant Embeddings
Nadav Dym, Matthias Wellershoff, Efstratios Tsoukanis +2
We study permutation-invariant embeddings of -dimensional point sets, which are defined by sorting independent one-dimensional projections of the input. Such embeddings aris…
Symmetry-Aware Generative Modeling through Learned Canonicalization
Kusha Sareen, Daniel Levy, Arnab Kumar Mondal +3
Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations. The existing generative modeling paradigm for…
E(3)-Equivariant Mesh Neural Networks
Thuan Trang, Nhat Khang Ngo, Daniel Levy +3
Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have address the need for geometric deep learning on 3D mesh. However, we o…
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy, Sékou-Oumar Kaba, Carmelo Gonzales +2
We present a natural extension to E(n)-equivariant graph neural networks that uses multiple equivariant vectors per node. We formulate the extension and show that it improves perfo…