Expertise:
- Computational materials design
- Ab-initio and atomistic molecular dynamics
- Applied machine learning property modeling
Education:
- Ph.D., Materials Science, University of Wisconsin-Madison, 2021
- B.S., Mechanical Engineering, Texas A&M University, 2014
About Dr. Benjamin Afflerbach
Dr. Benjamin Afflerbach is a visiting instructor of Physics & Engineering at Fort Lewis College. He has worked for the past four years as director of an undergraduate research program, the Informatics Skunkworks, which has impacted over 500 students through group project-based research projects centered around various applied machine learning topics. Additionally, his research at the University of Wisconsin-Madison employed computational materials design methods including ab-initio and atomistic simulations to identify novel materials with a focus on bulk metallic glasses. He has also developed machine learning approaches to accelerate the materials design process with accessible featurization techniques.
2025 Afflerbach CV
Selected Publications
- Schultz, L. E., Afflerbach, B. T., Voyles, P. M., & Morgan, D. (2025). Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization. Journal of Materiomics, 11(4), 100964.
- Afflerbach, B. T., Fathema, N., Gillian-Daniel, A. L., Crone, W. C., & Morgan, D. (2022) Authentic Undergraduate Research in Machine Learning with The Informatics Skunkworks: A Strategy for Scalable Apprenticeship Applied to Materials Informatics Research. ASEE Annual Conference and Exposition. Aug 23, 2022
- Afflerbach B. T., Francis C., Schultz L. E., Erickson J., Meschke V., Strand E., Ward L., Perepezko J., Thoma D., Voyles P. M., Szlufarska I., Morgan D. (2022) Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features. Chem. Mat. 2022, 34, 2945−2954.