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Matthew E. Riddle

Energy Scientist


Dr. Matthew Riddle is an Assistant Energy Scientist in the Energy Systems and Infrastructure Analysis division at Argonne National Laboratory.

His research interests include agent-based modeling and other computer simulation techniques to model complex energy and economic systems.

Since 2012, he has been the lead developer of Argonne’s GCMat, an agent-based market model of global critical material supply chains. GCMat has been used to support analysis for the U.S. Department of Energy’s Office of Policy and for the Defense Logistics Agency’s biannual report on strategic material stockpile requirements.

Riddle’s other work includes modeling combined PV and battery systems, evaluating the economic impacts of COVID-19, and projecting adoption trajectories for manufacturing technologies.


Ph.D., Economics, University of Massachusetts, Amherst, United States
B.A., Mathematics, Carleton College, Northfield, United States

Select Publications

Riddle, M.E., Tatara, E., Olson, C., Smith, B.J., Bennett Irion, A., Harker, B., Pineault, D., Alonso, E., Graziano, D.J. (2020). Agent-based modeling of supply disruptions in the global rare earths market. Forthcoming in Resources Conservation and Recycling.

Riddle, M.E., Tatara, E., Olson, C., Smith, B.J., Bennet Irion, A., Graziano, D.J. (2020). Argonne’s global critical materials agent-based model (GCMat). Argonne National Laboratory, ANL-20/25.

Hanes, R.J., Carpenter, A, Riddle, M. and Graziano, D., & Cresko, J. (2019). Quantifying adoption rates and energy savings over time for advanced energy-efficient manufacturing technologies. Journal of Cleaner Production, 232: 925-939

Huang, R., Riddle, M., Graziano, D., Warren, J., Das, S., Nimbalkar, S., Cresko, J., & Masanet, E. (2016). Energy and Emissions Saving Potential of Additive Manufacturing: The Case of Lightweight Aircraft Components. Journal of Cleaner Production, 135: 1559-1570.

Das, S., Graziano, D., Upadhyayula, V. K., Masanet, E., Riddle, M., & Cresko, J. (2016). Vehicle lightweighting energy use impacts in US light-duty vehicle fleet. Sustainable Materials and Technologies, 8, 5-13.

Riddle, M., Macal, C. M., Conzelmann, G., Combs, T. E., Bauer, D., & Fields, F. (2015). Global critical materials markets: An agent-based modeling approach. Resources Policy, 45, 307-321.

Warren, J. A., Riddle, M. E., Graziano, D. J., Das, S., Upadhyayula, V. K., Masanet, E., & Cresko, J. (2015). Energy Impacts of Wide Band Gap Semiconductors in US Light-Duty Electric Vehicle Fleet. Environmental Science & Technology, 49(17), 10294-10302.

Yao, Y., Graziano, D. J., Riddle, M., Cresko, J., & Masanet, E. (2015). Understanding variability to reduce the energy and GHG footprints of US ethylene production. Environmental science & technology, 49(24), 14704-14716.

Riddle, M. and Muehleisen, R. T. (2014). A Guide to Bayesian Calibration of Building Energy Models. ASHRAE/IBPSA-USA Building Simulation Conference 2014, 276-83. IBPSA-USA.
Orr, M. G., Galea, S., Riddle, M. and Kaplan, G.A. (2014). Reducing Racial Disparities in Obesity: Simulating the Effects of Improved Education and Social Network Influence on Diet Behavior. Annals of Epidemiology, 24(8): 563-569.

Riddle, M. (2002). The Minimum Forcing Number for the Torus and Hypercube. Discrete Mathematics, 245.1: 283-292.Efff