Journal Publications


[1] Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy. ACS Sustainable Chemistry & Engineering 10.48 (2022): 16064-16069.

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[2] Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium. Digital Discovery (2022).

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[3] Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals Using 3D Convolutional Neural Networks. ACS sensors 7.9 (2022): 2545-2555.

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[4] Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste. Computers & Chemical Engineering 155 (2021): 107547.

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[5] Using Accurate Characterization of Mixed Plastic Waste Using Machine Learning and Fast Infrared Spectroscopy. ACS Sustainable Chemistry & Engineering 9.42 (2021): 14143-14151.

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[6] Convolutional Neural Nets In Chemical Engineering: Foundations, Computations, And Applications. AIChE Journal 67.9 (2021): e17282.

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[7] Using Machine Learning and Liquid Crystal Droplets to Identify and Quantify Endotoxins From Different Bacterial Species. Analyst 146.4 (2021): 1224-1233.

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[8] Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks. Chemical science 11.46 (2020): 12464-12476.

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[9] Highly Compact, Free-Standing Porous Electrodes From Polymer-Derived Nanoporous Carbons for Efficient Electrochemical Capacitive Deionization.Journal of Materials Chemistry A 7.4 (2019): 1768-1778.

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[10] Scalable Synthesis of Uniform Nanosized Microporous Carbon Particles From Rigid Polymers for Rapid Ion and Molecule Adsorption. ACS applied materials & interfaces 10.30 (2018): 25429-25437.

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[11] Understanding the Electrochemical Properties of Naphthalene Diimide: Implication for Stable and High-Rate Lithium-Ion Battery Electrodes. Chemistry of Materials 30.10 (2018): 3508-3517.

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Conference Publications


[1] Graph Neural Network Architecture Search for Molecular Property Prediction. 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020.

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Book Chapters, Technical Reports, and Others


[1] Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing. Artificial Intelligence in Manufacturing: Concepts and Methods, Elsevier (2022).

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Under Review


[1] Extreme Electricity Price Forecasting Using Autoencoders and Long Short-Term Memory. (2022).

[2] Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors. (2022).

[3] Hybrid Deep Learning Model For Asthma Exacerbation Prediction Using CT Scans And Clinical Data. (2022).

In Preparation


[1] Shengli Jiang*, Shiyi Qin, Prasanna Balaprakash, Reid C Van Lehn, Victor M Zavala. Uncertainty Quantification and Neural Architecture Search Using Graph Neural Networks for Molecular Property Prediction. (2022).
[2] Shengli Jiang*, Joshua L Pulsipher, Tyler A Soderstrom, Reid C Van Lehn, Victor M Zavala. Spatial-Temporal Control of a Pastillation Process using Convolutional Neural Network Sensors. (2022).
[3] Shengli Jiang*, Alexander D Smith, James J Schauer, Benjamin de Foy, Victor M Zavala. Air Pollutant Concentration Analysis Using Preconditioned Dynamic Mode Decomposition. (2022).
[4] Shengli Jiang*, Atharva Kelkar, Reid Van Lehn, Victor M Zavala. Using Interfacial Hydrogen Bond Graphs to Predict Surface Hydrophobicity. (2022).
[5] Hyperspectral Imaging Analysis Using Topological Descriptors. (2022).