Systematic development of accurate density functionals has been a
decade...
Including prior knowledge is important for effective machine learning mo...
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers ...
We present RL-VAE, a graph-to-graph variational autoencoder that uses
re...
Symbolic regression has been shown to be quite useful in many domains fr...
We present a framework, which we call Molecule Deep Q-Networks (MolDQN),...
We introduce tensor field networks, which are locally equivariant to 3D
...
Molecular "fingerprints" encoding structural information are the workhor...
Massively multitask neural architectures provide a learning framework fo...