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03/03/2022
Evolving symbolic density functionals
Systematic development of accurate density functionals has been a decade...
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09/17/2020
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Including prior knowledge is important for effective machine learning mo...
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06/29/2020
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers ...
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04/18/2019
Decoding Molecular Graph Embeddings with Reinforcement Learning
We present RL-VAE, a graph-to-graph variational autoencoder that uses re...
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01/23/2019
Neural-Guided Symbolic Regression with Semantic Prior
Symbolic regression has been shown to be quite useful in many domains fr...
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10/19/2018
Optimization of Molecules via Deep Reinforcement Learning
We present a framework, which we call Molecule Deep Q-Networks (MolDQN),...
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02/22/2018
Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds
We introduce tensor field networks, which are locally equivariant to 3D ...
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03/02/2016
Molecular Graph Convolutions: Moving Beyond Fingerprints
Molecular "fingerprints" encoding structural information are the workhor...
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02/06/2015
