PNNL

Osman Mamun obtained a PhD in chemical engineering from the University of South Carolina in 2017, followed by a postdoctoral appointment at Stanford University’s SLAC (Stanford Linear Accelerator Center) national laboratory. He is currently working at Pacific Northwest National Laboratory as a postdoctorate research associate where he is applying predictive and generative modeling to enable faster discovery of novel materials.

• Applications of machine learning for accelerated materials development.
• High-throughput screening of materials for targeted applications.
• Uncertainty quantification and Bayesian optimization for risk assessment of the predictive model.
• Computational simulation of catalytic phenomena occurring on heterogeneous catalyst surfaces.

• Programming
  • Python, R, Fortran, MATLAB, C++
• Numerical Computing
  • NumPy, SciPy, Pandas Database PostGreSQL, MySQL, MongoDB
• Data Visualization
  • Matplotlib, Seaborn, Plotly, Tableau
• Machine Learning
  • Scikit-learn, Pytorch, TensorFlow, Keras, LightGBM, XGBoost
• Quantum Chemistry
  • VASP, VASPsol, ASE, Dlpoly, TURBOMOLE, COSMO, COSMO-RS

• PhD in Chemical Engineering, University of South Carolina (2017)
• BS in Chemical Engineering, Bangladesh University of Engineering & Technology (2011)

2021

Mamun O., Wenzlick M., Sathanur A., Hawk J., Devanathan R. ”Machine Learning Augmented Predictive and Generative Model for Rupture Life in 9-12% Cr and Austenitic Alloy Materials.” npj Mater Degrad. 5, 20 (2021). https://doi.org/10.1038/s41529-021-00166-5

Mamun, O., Wenzlick, M., Hawk, J. et al. A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys. Sci Rep11, 5466 (2021). https://doi.org/10.1038/s41598-021-83694-z

Wenzlick, M., Mamun, O., Devanathan, R. et al. Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction. J. of Materi Eng and Perform 30, 823–838 (2021). https://doi.org/10.1007/s11665-020-05340-5

2020

Yang W., Solomon R.V., Lu J., Mamun O., Bond J.Q., Heyden A. 2020. “Unraveling the Mechanism of the Hydrodeoxygenation of Propionic Acid over a Pt (1 1 1) Surface in Vapor and Liquid Phases.” Journal of Catalysis 381, 547-560 https://doi.org/10.1016/j.jcat.2019.11.036

Mamun, O., Winther, K.T., Boes, J.R. et al. 2020. “A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts.” npj Comput Mater 6, 177 (2020). https://doi.org/10.1038/s41524-020-00447-8

Yang W., Solomon R.V., Mamun O., Bond J.Q., Heyden A. 2020. “Investigation of the Reaction Mechanism of the Hydrodeoxygenation of Propionic Acid over a Rh(111) surface : A First Principles Study” Journal of Catalysis 391 (2020) 98–110 h https://doi.org/10.1016/j.jcat.2020.08.015

2019

Boes J. R., Mamun O., Winther K., Bligaard T. 2019. “Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation.” Journal of Physical Chemistry A 123 (11), 2281-2285 https://doi.org/10.1021/acs.jpca.9b00311

Mamun O., Saleheen S., Bond J. Q., Heyden A. 2019. “Investigation of Solvent Effects on the Hydrodeoxygenation of Levulinic Acid over Ru(0001).” Journal of Catalysis 379, 164-179 https://doi.org/10.1016/j.jcat.2019.09.026

Winther, K.T., Hoffmann, M.J., Boes, J.R. et al. Catalysis-Hub.org, an open electronic structure database for surface reactions. Sci Data 6, 75 (2019). https://doi.org/10.1038/s41597-019-0081-y

Mamun O., Winther K., Boes J., Bligaard T. 2019. “High-throughput Calculations of Catalytic Properties of Bimetallic Alloy Surfaces.” Nature Scientific Data 6(1) 1-9 https://doi.org/10.1038/s41597-019-0080-z

Saleheen M., Verma A.M., Mamun O., Lu J., Heyden A. 2019. “Investigation of Solvent Effects on the Hydrodeoxygenation of Guaiacol over Ru Catalysts.” Catalysis Science and Technology 9(22) 6253-6273 https://pubs.rsc.org/en/content/articlelanding/2019/cy/c9cy01763a#!divAbstract

Hansen M., Torres J., Jennings P., Wang Z., Boes J., Mamun O., Bligaard T. 2019. “CatLearn - an Atomistic Machine Learning Package for Surface Science and Catalysis.” arXiv preprint arXiv:1904.00904

2018

Chowdhury A., Yang W., Walker E., Mamun O., Heyden A., and Terejanu G. 2018. “Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surface Using Machine Learning.” Journal of Physical Chemistry C 122(49), 28142-28150 ACS Editor’s Choice Article https://doi.org/10.1021/acs.jpcc.8b09284

2017

Mamun O., Saleheen S., Bond J. Q., Heyden A. 2017. “Importance of Angelical Lactone Formation in the Hydrodeoxygenation of Levulinic Acid to γ-Valerolactone over Ru catalysts.” Journal of Physical Chemistry C 121(34), 18746-18761 https://doi.org/10.1021/acs.jpcc.7b06369

2016

Behtash S., Lu J., Walker E., Mamun O., Heyden A. 2016. “Solvent effects in the liquid phase hydrodeoxygenation of methyl propionate over a Pd (111) catalyst model.” Journal of Catalysis 333, 171-183 https://doi.org/10.1016/j.jcat.2015.10.027

Mamun O., Walker E., Faheem M., Heyden A. 2016. “Theoretical Investigation of the Hydrodeoxygenation of Levulinic Acid to γ-Valerolactone over Ru(0001).” ACS Catalysis 7 (1), 215-228 https://doi.org/10.1021/acscatal.6b02548

Behtash S., Lu J., Mamun O., Williams CT., Monnier JR., Heyden A. 2016. “Solvation Effects in the Hydrodeoxygenation of Propanoic Acid over a Model Pd(211) Catalyst.” Journal of Physical Chemistry C 120(5),2724-2736 https://doi.org/10.1021/acs.jpcc.5b10419

2015

Lu J., Behtash S., Mamun O., Heyden A. 2015. “Theoretical Investigation of the Reaction Mechanism of the Guaiacol Hydrogenation over a Pt (111).” ACS Catalysis 5 (4), 2423-2435 https://doi.org/10.1021/cs5016244