Journal Publications

[J15] S. Jiang, A. B. Dieng, and M. A. Webb. Property-Guided Generation of Complex Polymer Topologies Using Variational Autoencoders. npj Computational Materials, 2024, 10, 139.

[J14] S. Jiang, S. Qin, R. C. Van Lehn, P. Balaprakash, and V. M. Zavala. Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search. Digital Discovery (accepted), 2024.

[J13] F. Long, S. Jiang, E. Bar-Ziv, and V. M. Zavala. Robust plastic waste classification using wavelet transform multi-resolution analysis and convolutional neural networks. Computers & Chemical Engineering, 2024,181:108516.

[J12] S. Jiang, N. Bao, A. D. Smith, S. Byndoor, M. Mavrikakis, R. C. Van Lehn, N. L. Abbott and V. M. Zavala. Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Data Analysis. Journal of Physical Chemistry C, 2023, 127, 32, 16081–16098.

[J11] S. Qin, S. Jiang, J. Li, P. Balaprakash, R. C. Van Lehn, and V. M. Zavala. Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium. Digital Discovery, 2023, 2, 138.

[J10] F. Long, S. Jiang, A. G. Adekunle, V. M. Zavala, and E. Bar-Ziv. Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy. ACS Sustainable Chemistry & Engineering, 2022, 10, 48, 16064-16069.

[J9] N. Bao, S. Jiang, A. D. Smith, J. J. Schauer, M. Mavrikakis, R. C. Van Lehn, V. M. Zavala, and N. L. Abbott. Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals using 3D Convolutional Neural Networks. ACS Sensors, 2022, 7, 9, 2545-2555.

[J8] S. Zinchik, S. Jiang, S. Friis, F. Long, L. Høgstedt, V. M. Zavala, and E. Bar-Ziv. Accurate Characterization of Mixed Plastic Waste using Machine Learning and Fast Infrared Spectroscopy. ACS Sustainable Chemistry & Engineering, 2021, 9, 42, 14133-14151.

[J7] S. Jiang and V. M. Zavala. Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications. AIChE Journal, 2021, 67, 9, e17282.

[J6] S. Jiang, Z. Xu, M. Kamran, S. Zinchik, S. Paheding, A. G. McDonald, E. Bar-Ziv, and V. M. Zavala. Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste. Computers & Chemical Engineering, 2021, 155, 107547.

[J5] S. Jiang, J. Noh, C. Park, A. D. Smith, N. L. Abbott, and V. M. Zavala. Endotoxin Detection Using Liquid Crystal Droplets and Machine Learning. Analyst, 2021, 146, 1224-1233.

[J4] A. K. Chew, S. Jiang, W. Zhang, V. M. Zavala, and R. C. Van Lehn. Fast Predictions of Liquid- Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks. Chemical Science, 2020, 11, 12464-12476.

[J3] F. Ji, L. Wang, J. Yang, X. Wu, M. Li, S. Jiang, S. Lin, and Z. Chen. Highly Compact, Free-Standing Porous Electrodes from Polymer-Derived Nanoporous Carbons for Efficient Electrochemical Capacitive Deionization. Journal of Materials Chemistry A, 2019, 7, 4, 1768-1778.

[J2] F. Ji, Y. Shi, M. Li, S. Jiang, G. Chen, F. Liu, and Z. Chen. Scalable Synthesis of Uniform Nanosized Microporous Carbon Particles from Rigid Polymers for Rapid Ion and Molecule Adsorption. ACS Applied Materials & Interfaces, 2018, 10, 30, 25429-25437.

[J1] Y. Shi, H. Tang, S. Jiang, L. V. Kayser, M. Li, F. Liu, F. Ji, D. J. Lipomi, S. P. Ong, and Z. Chen. Understanding the Electrochemical Properties of Naphthalene Diimide: Implication for Stable and High-Rate Lithium-Ion Battery Electrodes. Chemistry of Materials, 2018, 30, 10, 3508-3517.

Conference Publications

[C2] Y. Peng, Z. Wang, I. Castillo, S. Jiang, and L. Gunnell. A new modeling framework for real-time extreme electricity price forecasting. In 12th IFAC International Symposium on Advanced Control of Chemical Processes. IFAC, 2024.

[C1] S. Jiang and P. Balaprakash. Graph Neural Network Architecture Search for Molecular Property Prediction. In IEEE International Conference on Big Data, 2020, 1346-1353.

Book Chapters

[B1] S. Jiang, S. Qin, J. L. Pulsipher, and V. M. Zavala. Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing. Artificial Intelligence in Manufacturing: Concepts and Methods, 2022.

In Preparation

[I3] S. Dhamankar, S. Jiang, and M. A. Webb. Accelerating multicomponent phase-coexistence calculations with machine learning, 2024.

[I2] S. Jiang and M. A. Webb. Physics-guided neural networks for transferable prediction of polymer properties, 2024.

[I1] S. Jiang and M. A. Webb. Active learning of viscosity-modifying polymers with complex topologies, 2024.

Under Review

[U1] S. Jiang, K. Carey, L. Denlinger, N. Jarjour, M. Schiebler, R. Sorkness, A. Hahn, S. B. Fain, and V. M. Zavala. Hybrid Deep Learning Model For Asthma Progression Prediction Using CT Scans And Clinical Data. Under Review, 2023.