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Michael J. Davis

Senior Chemist (Theoretical Chemistry)


Theoretical Chemist, Argonne National Laboratory, 1982-present

Postdoctoral Associate, University of Texas, 1981-1982


Ph.D., Chemical Physics, University of California, Los Angeles, 1981.

B.S., Carnegie Mellon University, 1975.

Research interests

The work explores chemically reactive systems using novel numerical analyses.  One focus of the work is exploration and theoretical validation of chemical-kinetic mechanisms, using global sensitivity analysis.  An effort on reaction pathway analysis has been underway.  The expertise developed in the earlier work has led to the implementation of these techniques for studying problems in chemical reactivity, including isolated chemical kinetics and dynamics.  There is a major effort underway for using such techniques for fitting potential energy surfaces.  Another major effort underway is the application of computational optimal transport to molecular spectroscopy and dynamics.  This effort includes studies of the mixing of probability densities using optimal transport and related methods.  The mixing includes geodesics along positive semidefinite matrices and density matrices, which may have quantum-information applications. In addition, there is an effort for using Bayesian analysis in quantum information applications.

Recent publications

  • N. A. Seifert, K. Prozument, and M. J. Davis, Application of Computational Optimal Transport to Molecular Spectra”, in preparation (2020).  
  • A. Mannodi-Kanakkithodi, M. Y. Toriyama, F. G. Sen, M. J. Davis, R. F. Klie, and M. K. Y. Chan, Machine-Learned Impurity Level Prediction for Semiconductors: The Example of Cd-Based Chalcogenides”, NPJ Comput. Mater. 6, Article 39 (2020).
  • A. W. Jasper and M. J. Davis, Parameterization Strategies for Intermolecular Potentials for Trajectory-Based Collision Parameters”, J. Phys. Chem. A 123, 3464-3480 (2019).
  • S. Bai, R. Sivaramakrishnan, M. J. Davis, and R. T. Skodje, A Chemical Pathway Perspective on the Kinetics of Low-Temperature Ignition of Propane”, Combustion and Flame 202, 154-178 (2019).
  • G. M. Magnotti, Z. Wang, W. Liu, R. Sivaramakrishnan, S. Som, and M. J. Davis, Sparsity Facilitates Chemical Reaction Selection for Engine Simulations”, J. Phys. Chem. A 122, 7227-7237 (2018).
  • M. J. Davis, W. Liu, and R. Sivaramakrishnan, Global Sensitivity Analysis with Small Sample Sizes: Ordinary Least Squares Approach”, J. Phys. Chem. A 121, 553-570 (2017).
  • A. Kinaci, B. Narayanan, F. G. Sen, M. J. Davis, S. K. Gray, M. Chan, and S. K. Gray, M. K. Y. Chan, and S. K. R. S. Sankaranarayanan, Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search”, Scientific Reports 6, Article number: 34974 (2016).
  • B. Narayanan, A. Kinaci, F. G. Sen, M. J. Davis, S. K. Gray, M. K. Y. Chan, and S. K. R. S. Sankaranarayanan, Describing the Diverse Geometries of Gold from Nanoclusters to Bulk—A First-Principles-Based Hybrid Bond-Order Potential”, J. Phys. Chem. C. 120, 13787-13800 (2016).
  • B. Narayanan, K. Sasikumar, Z.-G. Mei, A. Kinaci, F. G. Sen, M. J. Davis, S. K. Gray, M. Chan, and S. K. Gray, M. K. Y. Chan, and S. K. R. S. Sankaranarayanan, Development of a Modified Embedded Atom Force Field for Zirconium Nitride Using Multi-Objective Evolutionary Optimization”, J. Phys. Chem. C 120, 1747517483 (2016).
  • S. Bai, M. J. Davis, and R. T. Skodje, The Sum Over Histories Representation for Kinetic Sensitivity Analysis:  How Chemical Pathways Change When Reaction Rate Coefficients Are Varied”, J. Phys. Chem A 119, 11039-11052 (2015).
  • S. Bai, D. Zhou, M. J. Davis, and R. T. Skodje, Sum over Histories Representation for Chemical Kinetics”, J. Phys. Chem. Lett.  6, 183188 (2015).
  • D. M. A. Karwat, M. S. Wooldridge, S. J. Klippenstein, and M. J. Davis, Effects of New Ab Initio Rate Coefficients on Predictions of Species Formed during n-Butanol Ignition and Pyrolysis”, J. Phys. Chem. A 119, 543-551 (2015).
  • Y. Pei, M. J. Davis, L. M. Pickett, S. Som, Engine Combustion Network (ECN): Global sensitivity analysis of Spray A for different combustion vessels”, Combustion and Flame 162, 2337-2347 (2015).
  • F. G. Sen, A. Kinaci, B. Narayanan, S. K. Gray, M. J. Davis, S. K. R. S. Sankaranarayanan, and M. K. Y. Chan, Towards Accurate Prediction of Catalytic Activity in IrO2 Nanoclusters Via First Principles-Based Variable Charge Force Field”,  J. Mater. Chem A 3, 18970-18982 (2015).
  • Y. Pei, R. Shan, S. Som, T. Lu, D. E. Longman, and M. J. Davis, Global Sensitivity Analysis of a Diesel Engine Simulation with Multi-Target Functions”, Proceedings of the Society of Automotive Engineers (SAE) World Congress, 2014, SAE Paper 2014-01-1117.
  • W. Liu, R. Sivaramakrishnan, M. J. Davis, S. Som, D. E. Longman and T. F. Lu, Development of a reduced biodiesel surrogate model for compression ignition engine modeling”, Proc. Combust. Inst. 34, 401-409 (2013).
  • D. D. Y. Zhou, M. J. Davis, and R. T. Skodje, Multi-Target Global Sensitivity Analysis of n-Butanol Combustion”, J. Phys. Chem. A 117, 3569-3584 (2013).
  • S. Som, W. Liu, D. D. Y. Zhou, G. M. Magnotti, R. Sivaramakrishnan, D. E. Longman, R. T. Skodje, and M. J. Davis, Quantum Tunneling Affects Engine Performance”, J. Phys. Chem. Lett 4, 2021-2025 (2013).