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Posts
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Portfolio
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Projects
Machine Learning for Predicting the Viscosity of Complex Fluids and Aiding Formulation Design
May 2023
This project aims to develop a machine learning model for predicting the viscosity of complex fluids and to use it to aid in the formulation design process.
Autonomous Determination of Hansen Solubility Parameters (HSPs) in an Automated Lab
July 2023
This aim of this project is to leverage machine learning and automated lab technologies to autonomously determine Hansen Solubility Parameters (HSPs) for a wide range of chemicals in a closed-loop fashion.
ML-Based Sustainable Development of Formulation for Stable Emulsions
October 2023
This project aims to develop a machine learning model for predicting the stability of emulsions and to use it to develop sustainable formulations with greener chemicals.
Publications
Yttrium–Sodium Halides as Promising Solid-State Electrolytes with High Ionic Conductivity and Stability for Na-Ion Batteries
Published in The Journal of Physical Chemistry Letters, 2020
This paper is about demonstrating a type of Yttrium–Sodium Halide-based materials as promising solid-state electrolytes for Na-ion batteries.
Recommended citation: Qie, Y.; Wang, S.; Fu, S.; Xie, H.; Sun, Q.; Jena, P. Yttrium–Sodium Halides as Promising Solid-State Electrolytes with High Ionic Conductivity and Stability for Na-Ion Batteries. J. Phys. Chem. Lett. 2020, 11 (9), 3376–3383. 🔗
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Chemistry-Inspired Diffusion with Non-Differentiable Guidance
Published in ICLR'25, 2025
This paper is about leveraging domain knowledge from quantum chemistry as a non-differentiable oracle to guide an unconditional diffusion model in generating more stable 3D molecular structures.
Recommended citation: Shen, Y.*; Zhang, C.*; Fu, S.*; Zhou, C.; Washburn, N.; Póczos, B. Chemistry-Inspired Diffusion with Non-Differentiable Guidance. The Thirteenth International Conference on Learning Representations. ICLR, 2025. 🔗
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(manuscript under review) Predictive Modeling of Emulsion Stability via Hierarchical Machine Learning
Manuscript under review, July 2025
Stay tuned for updates on this exciting publication!
Manuscript under review: Roguski, M.*; Fu, S.*; Walker, L.; Tilton, R.; Washburn, N.; Cochran, B.; Stone, C.; Barker, M.; Jamadagni, S.; Johnson, E. Predictive Modeling of Emulsion Stability via Hierarchical Machine Learning. Manuscript under review, 2025.
(manuscript under review) Autonomous Determination of Hansen Solubility Parameters via Active Learning
Manuscript under review, August 2025
Stay tuned for updates on this exciting publication!
Manuscript under review: Fu, S.; Wang, D.; Henderiks, H.; Assis, A.; Charron, J.; Washburn, N. Autonomous Determination of Hansen Solubility Parameters via Active Learning. Manuscript under review, 2025.
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(in preparation) Machine Learning for Predicting the Viscosity of Complex Fluids and Aiding Formulation Design
In preparation, August 2025
Stay tuned for updates on this exciting publication!
In preparation: Fu, S.; Bruno, C.; Chua, A.; Póczos, B.; Washburn, N. Machine Learning for Predicting the Viscosity of Complex Fluids and Aiding Formulation Design. In preparation, 2025.
Presentations and Posters
Molecular Representation for Property Prediction: Wanders in the Chemical Space
December 2, 2022
Talk, Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania
From the early days of QSAR to the current deep learning models such as variational autoencoders and graph neural networks, molecular representation has been a central theme in cheminformatics. In this talk, I reviwed the history of molecular representation and discussed the current state-of-the-art models for property prediction.
Formulation AI for the Guided Viscosity Design of Complex Fluids
February 12, 2024
Poster, Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania
The design of complex fluids is a challenging task due to the high-dimensional formulation space. In this work, we developed a genetic algorithm-based and physics-informed formulation AI to predict the dynamic viscosity of non-Newtonian complex fluids. We showed how AI can be implemented to discover new viscosity models that are more related to real-life datasets than empirical models such as the Cross model. More information here.
Closed-loop and Autonomous: CMU Cloud Lab for Measuring Hansen Solubility Parameters
June 27, 2024
Talk, Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania
Autonomous labs can be a tranformative tool for the future of research to accelerate scientific discovery, but they are capital intensive and require a high level of expertise to design and operate. In this talk, I introduced our approach to use the CMU cloud lab as a proxy for designing autonomous workflows. Specifically, we demonstrated how the cloud lab can be used to measure the Hansen solubility parameters of a series of polymers in a closed-loop and autonomous fashion. Everything, including the design and selection of experiments, the execution of the experiments, and the analysis of the experiment findings, is done autonomously with no human intervention, except the neccessary human involvement in the cloud lab facility.
Teaching
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