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Found 7 papers, 4 papers with code
Classical Quantum Optimization with Neural Network Quantum States
The classical simulation of quantum systems typically requires exponential resources.
Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics
Strategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Ranked #3 on
Molecular Property Prediction
on ToxCast
Deep Learning Phase Segregation
Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems.
Deep Learning the Physics of Transport Phenomena
We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning.
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose.
MoleculeNet: A Benchmark for Molecular Machine Learning
However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods.
