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Found 10 papers, 3 papers with code
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers.
A Green(er) World for A.I
applications, we are sure to face an ever-mounting energy footprint to sustain these computational budgets, data storage needs, and more.
Graph Contrastive Learning for Materials
By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks.
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 approach for inverse design problems in the life sciences.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
FastFlows: Flow-Based Models for Molecular Graph Generation
We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.
Benchmarking Resource Usage for Efficient Distributed Deep Learning
Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains.
Bringing Atomistic Deep Learning to Prime Time
Artificial intelligence has not yet revolutionized the design of materials and molecules.
Scalable Geometric Deep Learning on Molecular Graphs
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing.
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes
Traditional frequency based projection filters, or projection operators (PO), separate signal and noise through a series of transformations which remove frequencies where noise is present.
