180 citations · 463 across the 9 of their papers we have counts for
9 papers
Evaluating and Validating Cluster Results
Anupriya Vysala, Joseph Gomes
Clustering is the technique to partition data according to their characteristics. Data that are similar in nature belong to the same cluster [1]. There are two types of evaluation…
Classical Quantum Optimization with Neural Network Quantum States
Joseph Gomes, Keri A. McKiernan, Peter Eastman +1
The classical simulation of quantum systems typically requires exponential resources. Recently, the introduction of a machine learning-based wavefunction ansatz has led to the abil…
Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes +4
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 lab…
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss
Rishi Sharma, Amir Barati Farimani, Joe Gomes +2
In typical machine learning tasks and applications, it is necessary to obtain or create large labeled datasets in order to to achieve high performance. Unfortunately, large labeled…
Deep Learning Phase Segregation
Amir Barati Farimani, Joseph Gomes, Rishi Sharma +2
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 bi…
Deep Learning the Physics of Transport Phenomena
Amir Barati Farimani, Joseph Gomes, Vijay S. Pande
We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning. Using conditional generative a…
Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar +7
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. The end-to-end t…
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg +1
Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of…
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg +5
Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make…