1.9k citations · 4k across the 54 of their papers we have counts for
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FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
Kyucheol Min, Elyssa Hofgard, Tess Smidt
Predicting the thermodynamic properties of an alloy requires sampling its configurations across many chemical conditions and recovering free energies on a common absolute scale. Ma…
Reconstructing local environments from concise atomistic representations
Jigyasa Nigam, Tuong Phung, Ameya Daigavane +2
Symmetry-based representations of local atomic structure, such as the power spectrum or bispectrum, are routinely used to characterize the structural diversity of datasets and as i…
Rotational equivariance and locality in data-driven subgrid-scale closures
Ryley McConkey, Julia Balla, Elyssa Hofgard +2
Data-driven subgrid-scale closures for large eddy simulation are of significant interest in many engineering and geoscience applications. In this context, several important questio…
Learning Lattice Parameters from Powder X-Ray Diffraction Data Using Invariants
Elyssa Hofgard, Kyucheol Min, Nofit Segal +8
We present a machine learning (ML) method to determine unit cell parameters from powder X-Ray diffraction (XRD) data using a novel invariant lattice representation. In ML, the data…
EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
Yi-Lun Liao, Alexander J. Hoffman, Sabrina C. Shen +3
As -equivariant graph neural networks mature as a core tool for 3D atomistic modeling, improving their efficiency, expressivity, and physical consistency has become a centra…
The Closure Challenge: a benchmark task for machine learning in turbulence modelling
Ryley McConkey, Tyler Buchanan, Tess Smidt +3
We introduce a field-wide benchmark challenge for machine learning in Reynolds-averaged Navier-Stokes (RANS) turbulence modelling. Though open-source datasets exist for training da…
Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
YuQing Xie, Ameya Daigavane, Mit Kotak +1
-equivariant neural networks have proven to be effective in a wide range of 3D modeling tasks. A fundamental operation of such networks is the tensor product, which allows in…
Machine learning of electronic structure and atomistic properties from the external potential
Jigyasa Nigam, Tess Smidt, Geneviève Dusson
Electronic structure calculations remain a major bottleneck in atomistic simulations and, not surprisingly, have attracted significant attention in machine learning (ML). Most exis…
Turbulence teaches equivariance to neural networks
Ryley McConkey, Julia Balla, Jeremiah Bailey +5
We show that the rotational nature of turbulence affects how neural networks learn mappings between quantities governed by the Navier-Stokes equations. We train super-resolution mo…
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
Teddy Koker, Abhijeet Gangan, Mit Kotak +2
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on ene…