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Posts
Transition State Searches on Neural Network Potential-Energy Surfaces
Published:
Welcome!
Published:
Welcome to the Gomes Lab Research Blog! We are a research group in the University of Iowa Department of Chemical and Biochemical Engineering and we study problems in catalysis and energy materials using computational tools from theoretical chemistry and machine learning. We are hiring graduate students eagar to work in this area in the upcoming year, Fall 2021. Please see the Department of Chemical and Biochemical Engineering page for Prospective Graduate Students for more details on how to apply.
people
Joe Gomes
Principle Investigator
Jonah Marks
Graduate Research Assistant
Rebecca Walters
Graduate Research Assistant
Samuel Adesanoye
Graduate Research Assistant
Bolaji Oladipo
Graduate Research Assistant
portfolio
Molecular Machine Learning
Artificial intelligence for molecular property prediction and material design
Molecular Simulation
Combining physics-based and data-driven methods for molecular simulation
Computational Catalysis
Application of Density Functional Theory to understand the mechanisms of catalyzed reactions
publications
Strategies for Pre-training Graph Neural Networks
Published in International Conference on Learning Representations, 2019
We investigate the effective use of pre-training strategies on graph datasets for molecular property prediction and protein function prediction.
Recommended citation: Weihau Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, Jure Leskovec. "Strategies for Pre-training Graph Neural Networks." In International Conference on Learning Representations (2019). https://openreview.net/forum?id=HJlWWJSFDH
Classical quantum optimization with neural network quantum states
Published in 33rd Conference on Neural Information Processing Systems (NeurIPS), Workshop of Machine Learning and the Physical Sciences, 2019
We demonstrate the utility of a variational representation of quantum states based on artificial neural networks for performing quantum optimization.
Recommended citation: Joseph Gomes, Keri M. McKiernan, Peter Eastman, Vijay S. Pande. "Classical Quantum Optimization with Neural Network Quantum States". In Neural Information Processing Systems, Workshop on Machine Learning for Physical Sciences (2019). https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_144.pdf
Air-Stability and Carrier Type in Conductive M3(Hexaaminobenzene)2, (M = Co, Ni, Cu)’>
Published in Journal of the American Chemical Society, 2020
We investigate electrical transport properties of the M-HAB metal organic framework system (M = Co, Ni, Cu).
Recommended citation: Allison C. Hinckley, Jihye Park, Joseph Gomes, Evan Carlson, and Zhenan Bao. "Air-Stability and Carrier Type in Conductive M3(Hexaaminobenzene)2, (M = Co, Ni, Cu)". Journal of the American Chemical Society, 142 (25), 11123-11130 (2020). https://pubs.acs.org/doi/abs/10.1021/jacs.0c03500
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
CBE:5120 Data Science in Chemical and Engineering Systems
Theory and application of numerical methods and data-driven algorithms towards understanding chemical processes; Scientific computing in the Python programming language; Numerical solutions to differential equations; Nonlinear and constrained optimization; data preprocessing and visualization; dimensionality reduction and clustering; supervised machine learning.
CBE:3105 Chemical Engineering Thermodynamics
Applications of thermodynamic principles to chemical and physical processes; prediction of material properties; phase and chemical equilibria applied to mixtures and reacting systems.