292 citations · 537 across the 17 of their papers we have counts for
17 papers
Protein Discovery with Discrete Walk-Jump Sampling
Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny +10
We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Mar…
Protein Design with Guided Discrete Diffusion
Nate Gruver, Samuel Stanton, Nathan C. Frey +6
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while t…
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
Michael Maser, Ji Won Park, Joshua Yao-Yu Lin +3
We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task ai…
A Green(er) World for A.I
Dan Zhao, Nathan C. Frey, Joseph McDonald +6
As research and practice in artificial intelligence (A.I.) grow in leaps and bounds, the resources necessary to sustain and support their operations also grow at an increasing pace…
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…
Roughness of molecular property landscapes and its impact on modellability
Matteo Aldeghi, David E. Graff, Nathan Frey +4
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemi…
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models
Joseph McDonald, Baolin Li, Nathan Frey +3
The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an u…
The MIT Supercloud Workload Classification Challenge
Benny J. Tang, Qiqi Chen, Matthew L. Weiss +24
High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machi…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…
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…
Bringing Atomistic Deep Learning to Prime Time
Nathan C. Frey, Siddharth Samsi, Bharath Ramsundar +2
Artificial intelligence has not yet revolutionized the design of materials and molecules. In this perspective, we identify four barriers preventing the integration of atomistic dee…
Scalable Geometric Deep Learning on Molecular Graphs
Nathan C. Frey, Siddharth Samsi, Joseph McDonald +3
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing. Bottleneck…
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes
Matthew L. Weiss, Nathan C. Frey, Siddharth Samsi +2
Traditional frequency based projection filters, or projection operators (PO), separate signal and noise through a series of transformations which remove frequencies where noise is…
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…
Universal fluctuations in growth dynamics of economic systems
Nathan C. Frey, Sakib Matin, H. Eugene Stanley +1
The growth of business firms is an example of a system of complex interacting units that resembles complex interacting systems in nature such as earthquakes. Remarkably, work in ec…