PNNL

Hadi (Mohammadhasan) Dinpajooh is a motivated theoretical/computational scientist. His research interests focus on statistical mechanics and modeling chemical processes/reactions in solutions, heterogeneous interfaces, and materials at equilibrium and non-equilibrium conditions. He has experience in complex data analysis, computer programing, machine/deep learning, high-performance computing, and molecular dynamics/Monte Carlo simulations. 

Dinpajooh received his PhD in chemistry from Arizona State University in 2016, where he used statistical mechanical approaches to study solvation and dielectric properties of solutions consisting of polar, polarizable, or charged solutes, as well as proteins. In his first postdoc at the University of Oregon, he developed coarse-graining approaches based on the liquid state theory and generalized Langevin methods to study polymer melts, proteins, and DNA molecules. In his second postdoc at the University of Pennsylvania, he developed effective Hamiltonians and non-equilibrium molecular dynamics simulation approaches to study the thermal conductance and the nature of heat transport along various polymer chains. 

He joined Pacific Northwest National Laboratory in July 2021 and is currently studying the properties of electrolyte solutions, investigating the forces between colloids or nanoparticles, developing machine-learning potentials, and liquid state theories. He is also studying the thermodynamic properties of nitric acid solutions for nuclear fuel dissolution and developing multiscale approaches for the optimization of chemical processing in real time for nuclear forensics.

View Dinpajooh’s Google Scholar page.

• Chemical reactivity and speciation of electrolyte solutions at bulk and heterogeneous interfaces at various conditions (equilibrium, nonequilibrium, exposure to external stimuli, and pH levels)
• Nucleation and aggregation in electrolyte solutions
• Developing ab initio-based machine learning potentials in bulk liquids and their interfaces
• Classical Density Functional Theory for liquids at various conditions 
• Developing generative AI models for metal complexes in solutions
• Heat transfer in molecular junctions and atomic force microscopy setups
• Effective Interactions between particles of various sizes (ions, nanoparticles, and colloids)

• Statistical mechanics
• Theoretical and computational chemistry
• Equilibrium and nonequilibrium molecular dynamics simulations
• Computer programming: Python, Fortran, Matlab, Mathematica, C++, and Unix shell
• Molecular simulations: performed classical and quantum mechanical calculations using software programs such as CP2K, LAMMPS, GROMACS, NAMD, NWChem, and Gaussian
• Machine/Deep learning: 
  • Used TensorFlow, Pytorch, and Scikit-learn to develop neural network and regression models 
  • Used Deepmd-kit and MACE to develop machine learning potentials

• PhD in chemistry, Arizona State University
• MS in chemistry, University of Minnesota, Twin Cities

• American Chemical Society
• American Institute of Chemical Engineers
• Materials Research Society

2025

• Dinpajooh M., G.L. Hightower, R.E. Overstreet, L.A. Metz, N.J. Henson, N. Govind, and A.M. Ritzmann, et al. 2025.  "On the Stability Constants of Metal-Nitrate Complexes in Aqueous Solutions" Physical Chemistry Chemical Physics In Press. PNNL-SA-205148 doi:10.1039/D4CP04295F

2024

• Dinpajooh M., N.N. Intan, T. Tim Duignan, E. Biasin, J.L. Fulton, S.M. Kathmann, and G.K. Schenter, et al. 2024. "Beyond the Debye-Hückel Limit: Towards a General Theory for Concentrated Electrolytes." Journal of Chemical Physics 161, no. 23:230901 (2024). PNNL-SA-203397. doi:10.1063/5.0238708
• Dinpajooh M., E. Biasin, E.T. Nienhuis-Marcial, S.T. Mergelsberg, C.J. Benmore, G.K. Schenter, and J.L. Fulton, et al. 2024. "Detecting Underscreening and Generalized Kirkwood Transitions in Aqueous Electrolytes." Journal of Chemical Physics 161, no. 15:151102. PNNL-SA-189103. doi:10.1063/5.0234518

2023

• Abraham E., M. Dinpajooh, C. Climent, and A. Nitzan. 2023. "Heat Transport with a Twist." The Journal of Chemical Physics 159, no. 17:174904. PNNL-SA-188812. doi:10.1063/5.0171680
• Chen R., M. Dinpajooh, and A. Nitzan. 2023. "Quantum bath augmented stochastic nonequilibrium atomistic simulations for molecular heat conduction." Journal of Chemical Physics 159, no. 13:Art. No. 134110. PNNL-SA-187803. doi:10.1063/5.0168117
• Dinpajooh M., J. Donley, J. Millis, and M. Guenza. 2023. "Chemical Potential of Flexible Polymer Liquid in a Coarse-Grained Representation." Journal of Physical Chemistry B. PNNL-SA-191234. doi:10.1021/acs.jpcb.3c06795

2022

• Chuev G.N., M. Dinpajooh, and M. Valiev. 2022. "Molecular-based analysis of nanoparticle solvation: classical density functional approach." The Journal of Chemical Physics 157, no. 18:Art. No. 184505. PNNL-SA-175215. doi:10.1063/5.0128817