Joseph Gomes

University of Iowa — United States

Materials Science · Physical Sciences

14h-index4.5kcitations43works1.62yr avg
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Research Topics

Machine Learning in Materials Science(8), Advanced MIMO Systems Optimization(7), Wireless Communication Networks Research(7), Advanced Database Systems and Queries(6), Computational Drug Discovery Methods(5)

Publications43 total

A Deep Learning Approach to Lidar Signal Denoising and Atmospheric Feature Detection
Remote Sensing·2025Open Access
Incorporation of Internal Coordinates Interpolation into the Freezing String Method
Journal of Chemical Theory and Computation·2025· 1 citedOpen Access
Using Multitask Machine Learning to Type Clouds and Aerosols from Space-Based Photon-Counting Lidar Measurements
Remote Sensing·2025Open Access
Global Aerosol Climatology from ICESat-2 Lidar Observations
Remote Sensing·2025· 1 citedOpen Access
Leveraging Deep Learning as a New Approach to Layer Detection and Cloud–Aerosol Classification Using ICESat-2 Atmospheric Data
Remote Sensing·2024· 6 citedOpen Access
Effect of silver electrode wetting state on oxygen reduction electrochemistry
Chemical Communications·2021· 10 cited
Air-Stability and Carrier Type in Conductive M<sub>3</sub>(Hexaaminobenzene)<sub>2,</sub> (M = Co, Ni, Cu)
Journal of the American Chemical Society·2020· 70 cited
Classical Quantum Optimization with Neural Network Quantum States
arXiv (Cornell University)·2019· 6 citedOpen Access
Strategies for Pre-training Graph Neural Networks
arXiv (Cornell University)·2019· 186 citedOpen Access
Pre-training Graph Neural Networks.
arXiv (Cornell University)·2019· 33 citedOpen Access
DeepStar Advanced Materials Program for Offshore HPHT Development
Offshore Technology Conference·2018
Deep Learning Phase Segregation
arXiv (Cornell University)·2018· 3 citedOpen Access
Deep Learning Physical Phenomena
Bulletin of the American Physical Society·2018
MoleculeNet: a benchmark for molecular machine learning
Chemical Science·2017· 2.8k citedOpen Access
Deep Learning the Physics of Transport Phenomena
arXiv (Cornell University)·2017· 94 citedOpen Access
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
ACS Central Science·2017· 574 citedOpen Access
Retrosynthetic reaction prediction using neural sequence-to-sequence models
arXiv (Cornell University)·2017· 1 citedOpen Access
DeepStar® Global Offshore Technology Development Program 12504: Real-time Monitoring for Critical Barriers
Offshore Technology Conference·2017· 2 cited
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
arXiv (Cornell University)·2017· 98 citedOpen Access
Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15
The Journal of Physical Chemistry B·2017· 268 citedOpen Access
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Frequent Co-authors

Vijay S. Pande(12), Hyeong‐Ah Choi(11), Martin Head‐Gordon(6), Alexis T. Bell(6), Bharath Ramsundar(5), Sankaranarayanapillai Shylesh(5), Amrinder Arora(4), Matthew J. McGill(4), Patrick Selmer(4), Evan N. Feinberg(3), Amir Barati Farimani(3), David Hanna(3), Christian G. Canlas(3), Jae-Hoon Kim(3), JungKyo Sohn(3), Hyeong In Choi(3), Luca Zappaterra(3), Zhenqin Wu(2), Caleb Geniesse(2), Aneesh Pappu(2)