Alex K. Chew

About Me

Welcome to my page! I grew up in Brooklyn, NY, where I acquired a Dual Bachelors/Masters degree at NYU Tandon School of Engineering in 2016. I then received my Ph.D. in Chemical and Biological Engineering from the University of Wisconsin-Madison under the supervision of Prof. Reid C. Van Lehn, where I applied molecular simulations and machine learning to engineer new chemical processes and materials. Throughout my graduate school career, I have grown passionate in developing more efficient algorithms, sharing code through useful tutorials, and communicating complex behavior in layman’s terms using interpretable schematics/visualizations. Outside of the office, I enjoy cooking/baking, hiking, and gaming. I am a big believer that scientific boundaries are pushed the furthest through productive collaborations, so don’t hesitate to reach out and say hello!

Publications

1. Chew, A. K.; Afzal, M. A. F.; Kaplan, Z.; Collins, E. M.; Gattani, S.; Misra, M.; Chandrasekaran, A.; Leswing, K.; Halls, M. D. "Leveraging High-throughput Molecular Simulations and Machine Learning for the Design of Chemical Mixtures" npj Comput. Mater. 2025, 11, 72. [Link]

2. Chew, A. K.; Afzal, M. A. F; Chandrasekaran, A.; Kamps, J. H.; Ramakrishnan, V. "Designing the next generation of polymers with machine learning and physics-based models" Mach. Learn.: Sci. Technol. 2024, 5, 045031. [Link]

3. Dinda, S.; Bhola, T.; Pant, S.; Chandrasekaran, A.; Chew, A. K.; Halls, M. D.; Sastry, M. "Machine learning-based design of pincer catalysts for polymerization reaction" Journal of Catalysis 2024, 115766. [Link]

4. Chew, A. K.; Sender, M.; Kaplan, Z.; Chandrasekaran, A.; Browning, A. R.; Kwak, H. S.; Halls, M. D.; Afzal, M. A. F. "Advancing Material Property Prediction: Using Physics-Informed Machine Learning Models for Viscosity" J. Cheminform. 2024, 16, 31. [Link] [Data]

5. Abroshan, H.; Kwak, H. S.; Chandrasekaran, A.; Chew, A. K.; Fonari, A.; Halls, M. D. "High-Throughput Screening of Hole Transport Materials for Quantum Dot Light-Emitting Diodes" Chem. Mater. 2023. [Link]

6. Smith, A.; Runde, S.; Chew, A. K.; Kelkar, A. S.; Maheshwari, U.; Van Lehn, R. C.; Zavala, V. M. "Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic." J. Chem. Theory Comput.  2023, 19, 5. [Link]

7. Chew, A. K.; Pedersen, J. A.; Van Lehn, R. C.*. "Predicting the Physicochemical Properties and Biological Activities of Monolayer-Protected Gold Nanoparticles Using Simulation-Derived Descriptors." ACS Nano. 2022, 16(4), 6282-6292. [Link]

8. Lochbaum, C. A.*; Chew, A. K.*; Zhang, X.; Rotello, V.; Van Lehn, R. C.; and Pedersen, J. A.. “Lipophilicity of cationic ligands promotes irreversible adsorption of nanoparticles to lipid bilayers.” ACS Nano. 2021, 15, 4, 6562-6572. [Link]

9. Chew, A. K.; Dallin, B. C.; and Van Lehn, R. C. “Interplay of Ligand Properties and Core Size Dictate the Hydrophobicity of Monolayer-Protected Gold Nanoparticles.” ACS Nano. 2021, 15, 3, 4534–4545. [Link]

10. Walker, T. W.; Frelka, N.; Shen, Z.; Chew, A. K.; Bannick, J.; Grey, S.; Dumesic, J. A.; Van Lehn, R. C.*; and Huber, G. W.*. “Recycling of multilayer plastic packaging materials by solvent-targeted recovery and precipitation.” Science Advances. 2020, 6(47), eaba7599. [Link] [Data]

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

12. Walker, T. W.*; Chew, A. K.*, Van Lehn, R. C.; Dumesic, J. A.; and Huber, G. W. “Rational Design of Mixed Solvent Systems for Acid-Catalyzed Biomass Conversion Processes Using a Combined Experimental, Molecular Dynamics and Machine Learning Approach.” Topics in Catalysis 2020. [Link]

13. Chew, A. K.*; Walker, T. W.*; Demir B.; Shen, Z.; Witteman, L.; Euclide, J.; Huber, G. W.; Dumesic, J. A.; and Van Lehn, R. C. “Effect of mixed-solvent environments on the selectivity of acid-catalyzed dehydration reactions.” ACS Catalysis. 2020, 10, 1679-1691. [Link]

14. Chew, A. K. and Van Lehn, R. C. “Quantifying the Stability of the Hydronium Ion in Organic Solvents with Molecular Dynamics Simulations.” Front. Chem. 2019, 7(439). [Link]

15. Chew, A. K. and Van Lehn, R. C. “Effect of Core Morphology on the Structural Asymmetry of Alkanethiol Monolayer-Protected Gold Nanoparticles.” J. Phys. Chem. C 2018, 122 (45), 26288-26297. [Link] [Data]

16. Walker, T. W.*; Chew, A. K.*; Li, H.; Demir, B.; Zhang, Z. C.; Huber, G. W.; Van Lehn, R. C.; and Dumesic, J. A. “Universal kinetic solvent effects in acid-catalyzed reactions of biomass-derived oxygenates.” Energy & Environmental Science 2018, https://doi.org/10.1039/C7EE03432F. Featured as Energy & Environmental Science 2018 HOT articles collection. [Link] [Data]

Presentations

Oral presentations

1. Chew, A. K.; Afzal, M. A. F; Chandrasekaran, A.; Kamps, J. H.; Ramakrishnan, V. "Accelerating Polymer Design with Targeted Properties Using Machine Learning and Physics-Based Models." Industrial Applications of Computational Chemistry and Molecular Simulation Session. American Institute of Chemical Engineers Annual Meeting 2024, San Diego, USA, October 27-31, 2024. [Abstract]

2. Chew, A. K.; Afzal, M. A. F.; Chandrasekaran, A.; Halls, M. D. "Leveraging Physics-Based Simulations and Machine Learning to Identify Promising Formulations for Materials Science." AI Modeling & Simulation Session. TechConnect World Innovation 2024, Washington, DC, USA, June 17-19, 2024.

3. Chew, A. K.; Afzal, M. A. F.; Sender, M.; Chandrasekaran, A.; Chen, L.; Browning, A. R.; Halls, M. D. "Predicting Small-Molecule Viscosities Using Machine Learning and Physics-Informed Approaches." MRS Spring 2023, San Francisco, California, USA, April 10-14, 2023.

4. Chew, A. K. and Van Lehn, R. C. "Predicting the Behavior of Monolayer-Protected Gold Nanoparticles Using Molecular Simulation-Derived Descriptors." American Chemical Society. Virtual Spring Meeting, April 5-16, 2021. [Link]

5. Chew, A. K. "Predicting the Behavior of Ligand-Coated Gold Nanoparticles using Molecular Simulation-Derived Descriptors." Soft Matter Group Meeting, Madison, WI, Feb. 11, 2021. [Link]

6. Chew, A. K.; Dallin, B. C.; and Van Lehn, R. C. "Predicting Biological Interactions to Monolayer-Protected Gold Nanoparticles Using Molecular Simulation-Derived Descriptors." American Institute of Chemical Engineers Annual Meeting, Nanoparticle Coatings and Nanocoatings on Particles, Virtual Meeting, November 13-16, 2020. [Link] [iPoster] [YouTube]

7. Chew, A. K. and Van Lehn, R. C. "Quantifying Anisotropic Properties Emerging from Uniformly Coated Gold Nanoparticles." American Institute of Chemical Engineers Annual Meeting, Symposium on Anistropic Particles: Synthesis, Characterization, Modeling, Assembly, and Applications, Pittsburgh, PA, October 28-31, 2019. [Link]

8. Chew, A. K.; Walker, T. W.; Demir, B.; Witteman, L.; Euclide, J.; Huber, G. W.; Dumesic, J. A.; and Van Lehn, R. C. "Tuning mixed-solvent environments to direct selectivities in acid-catalyzed reactions." Midwest Thermodynamics and Statistical Mechanics Meeting, Urbana, IL, June 2-4, 2019. [Link]

9. Chew, A. K.; Walker, T. W.; Demir, B.; Li, H.; Zhang, Z. Conrad; Huber, G. W.; Van Lehn, R. C.; and Dumesic, J. A. “Understanding Solvent Effects in Biomass Conversion Reactions using Molecular Dynamics Simulations." American Institute of Chemical Engineers Annual Meeting, Symposium on Computational Catalysis IV: Biomass Chemistry and Chemicals Production, Pittsburgh, PA, October 28-31, 2018. [Link]

10. Chew, A. K. “Molecular dynamics-derived Molecular Descriptors for Materials Design.” Soft Matter Group Meeting, Madison, WI, Feb. 7, 2018. [Link]

11. Chew, A. K. “Clustering Algorithms.” CBE970 Computing Seminar, Madison, WI, Nov. 9, 2018. [Link]

Poster presentations

1. Chew, A. K. "Combining Molecular Simulations and Machine Learning for Nanomaterial and Reaction Design." American Institute of Chemical Engineers Annual Meeting, Meet the Industry Candidates Poster Session, Virtual Meeting, November 13-16, 2020. [Link] [iPoster] [YouTube]

2. Chew, A. K.; Walker, T. W.; Huber, G. W.; Dumesic, J. A.; and Van Lehn, R. C. "Designing Mixed-Solvent Environments for Acid-Catalyzed Biomass Conversion Processes." Genomic Sciences Program Annual Principal Investigator Meeting, Washington, DC, February 24-25, 2020.  [Link]

3. Chew, A. K.; Jiang, S.; Zhang, W.; Zavala, V. M.; and Van Lehn, R. C. "Integrating Molecular Dynamics Simulations and Machine Learning to Predict Solvent Effects on Acid-Catalyzed Reactions." Computing in Engineering Forum, Madison, WI, September 10-11, 2019. [Link]

4. Chew, A. K.; Walker, T. W.; Jiang, S., Zavala, V. M.; Huber, G. W.; Dumesic, J. A.; and Van Lehn, R. C. "Understanding the Effect of Solvent Mixtures on Biomass Conversion Reactions Using Molecular Dynamics Simulations." Hougen Symposium in Honor of Professor James A. Dumesic, Madison, WI, November 21-22, 2019. [Link]

5. Chew, A. K.; Jiang, S.; Huber, G. W.; Dumesic, J. A.; Zavala, V. M.; and Van Lehn, R. C. "Understanding the Effect of Solvent Mixtures on Biomass Conversion Reactions Using Molecular Dynamics Simulations." Great Lakes Bioenergy Research Center Annual Science Meeting, Lake Geneva, WI, May 21-23, 2019.  [Link]

6. Chew, A. K.; Walker, T. W.; Huber, G. W.; Dumesic, J. A.; and Van Lehn, R. C. "Design of Solvent Composition for Acid-catalyzed Reactions of Biomass-derived Oxygenates Using Molecular Simulation-derived Observables." Midwest Thermodynamics and Statistical Mechanics Meeting, Pittsburgh, PA, June 10-12, 2018. [Link]

Blog posts

1. "Quantum Leap in QLEDs: Pioneering the Future of Optoelectronics with Smart Material Discovery", March, 2024. [Link]

2. Ramakrishnan, V.; Zyl, A.; Chew, A. K.; Afzal, A. "Fast-Tracking Next-Gen Polymers: How SABIC is Leveraging Machine Learning and Physics," November 29, 2023. [Link]