Manuscript ready, August 2025
Stay tuned for updates on this exciting publication!
Manuscript ready: Fu, S.; Bruno, C.; Chua, A.; Póczos, B.; Washburn, N. RheoNet: A Physics-Inspired Neural Network for Predicting the Viscosity of Complex Fluids. Manuscript ready, 2025.
Under review, August 2025
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Under review: Fu, S.; Wang, D.; Henderiks, H.; Assis, A.; Charron, J.; Washburn, N. Autonomous Determination of Hansen Solubility Parameters via Active Learning. Under review, 2025.
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Under review, July 2025
Stay tuned for updates on this exciting publication!
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. Under review, 2025.
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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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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