16 citations · 25 across the 4 of their papers we have counts for
5 papers
Graph Contrastive Learning for Materials
Teddy Koker, Keegan Quigley, Will Spaeth +2
Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, how…
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Nataša Tagasovska, Nathan C. Frey, Andreas Loukas +9
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences. However, these problems often require sampling ne…
Benchmarking Resource Usage for Efficient Distributed Deep Learning
Nathan C. Frey, Baolin Li, Joseph McDonald +6
Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains. Neural architecture searches, hyperparameter sweeps, and rap…
FastFlows: Flow-Based Models for Molecular Graph Generation
Nathan C. Frey, Vijay Gadepally, Bharath Ramsundar
We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules. With an…
High-throughput search for magnetic and topological order in transition metal oxides
Nathan C. Frey, Matthew K. Horton, Jason M. Munro +3
The discovery of intrinsic magnetic topological order in has invigorated the search for materials with coexisting magnetic and topological phases. These multi-orde…