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05/31/2023
Protein Design with Guided Discrete Diffusion
A popular approach to protein design is to combine a generative model wi...
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02/15/2023
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augm...
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01/27/2023
A Green(er) World for A.I
As research and practice in artificial intelligence (A.I.) grow in leaps...
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11/24/2022
Graph Contrastive Learning for Materials
Recent work has shown the potential of graph neural networks to efficien...
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10/19/2022
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Deep generative models have emerged as a popular machine learning-based ...
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03/31/2022
SELFIES and the future of molecular string representations
Artificial intelligence (AI) and machine learning (ML) are expanding in ...
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01/28/2022
Benchmarking Resource Usage for Efficient Distributed Deep Learning
Deep learning (DL) workflows demand an ever-increasing budget of compute...
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01/28/2022
FastFlows: Flow-Based Models for Molecular Graph Generation
We propose a framework using normalizing-flow based models, SELF-Referen...
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12/09/2021
Bringing Atomistic Deep Learning to Prime Time
Artificial intelligence has not yet revolutionized the design of materia...
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12/06/2021
Scalable Geometric Deep Learning on Molecular Graphs
Deep learning in molecular and materials sciences is limited by the lack...
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11/13/2021