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09/12/2023
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence u...
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09/06/2023
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
We present a natural extension to E(n)-equivariant graph neural networks...
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05/14/2023
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling
We present MatSci-NLP, a natural language benchmark for evaluating the p...
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01/28/2023
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Current protein language models (PLMs) learn protein representations mai...
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12/18/2022
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
The emergence of large pretrained models has enabled language models to ...
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11/23/2022
Group SELFIES: A Robust Fragment-Based Molecular String Representation
We introduce Group SELFIES, a molecular string representation that lever...
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11/22/2022
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Mitigating the climate crisis requires a rapid transition towards lower ...
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10/31/2022
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
We present the Open MatSci ML Toolkit: a flexible, self-contained, and s...
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10/23/2022
Multi-Objective GFlowNets
In many applications of machine learning, like drug discovery and materi...
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06/14/2021
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
Mixed-precision quantization is a powerful tool to enable memory and com...
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10/08/2020
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
Learning effective policies for sparse objectives is a key challenge in ...
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10/06/2020
Safety Aware Reinforcement Learning (SARL)
As reinforcement learning agents become increasingly integrated into com...
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07/14/2020
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
As modern neural networks have grown to billions of parameters, meeting ...
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05/02/2019