Education

Chemistry

Ph.D.
Department of Chemistry, University of Alberta
2014

Applied Chemistry

M.S.
Department of Applied Chemistry and Chemical Technology, University of Dhaka
2007

Applied Chemistry

B.S.
Department of Applied Chemistry and Chemical Technology, University of Dhaka
2005

Academics

CHEM003

General Chemistry and Recitation.  4 credit lecture course. Deals with the fundamental principles of chemistry, the chemical and physical properties of the elements and their most common compounds, and methods of qualitative inorganic analysis.

CHEM004

General Chemistry and Recitation. 4 credit lecture course, it is a continuation of CHEM 003. Prerequisite: CHEM 003.

Research

Specialty

Classical and quantum molecular dynamics simulations and machine learning.

Group Information

Our molecular simulation group is interested in developing and employing computational tools based on statistical mechanics, quantum mechanics, and machine learning to study fundamental details of molecular properties in complex environments. Specific areas of current interest include understanding concentrated electrolytes and solid-electrolyte interfaces relevant to basic energy science, condensed phase materials relevant to neuromorphic computing, and air-water interfaces relevant to atmospheric chemistry.

A. Machine learned force field from DFT data:

Molecular dynamics simulation based on Density functional theory is an effective way to study systems where polarization plays an important role or chemical bond breaking and formation are involved. However, the computational cost of treating a large system ( having more than a few hundred atoms) with explicit atoms is currently prohibitively expensive and beyond the routine practice. In order to solve this bottleneck, we are currently using machine learning techniques (artificial neural network) to learn the forces and energies generated by appropriate density functional theory. When properly trained, the machine-learned force field can produce the accuracy of the DFT functional but provide a few orders of magnitude faster way of calculating energy and forces. This faster calculation can afford  simulating complex molecular phenomena on a quantum level that was not possible to explore previously.

B. Understanding solid-liquid interface in Li-ion batteries:

Electrode-electrolyte interface plays a critical role in the design of energy storage devices. Concentrated electrolytes ( e.g. ionic liquids) are promising electrolytes in Li and beyond-Li ion batteries. We employ machine learning force field and statistical mechanical tools to understand the structure and dynamics of graphite and metal electrode with ionic liquids and leverage that understanding to design new electrolytes with improved performance.

C. Designing intermetallic alloy nano-particles for electro-catalysis:

Metal nanoparticles are promising electrocatalysts for many electrochemical reactions e.g. oxygen reduction reaction, hydrogen evolution reaction, and CO2 reduction reaction. Platinum-group metals are the most commonly used electrocatalysts which are scarce and costly. Intermetallic nanoparticles alloy where low-cost transition metals can be mixed with platinum-group metal is a promising new technology to reduce the use of costly metals. We employ machine learning, quantum mechanics, and statistical mechanics to understand molecular level details and to build structure-activity correlation in intermetallic nanoparticles alloy and consequently pave the way for designing new electrocatalysts.

 

We are currently recruiting Ph.D. students and post-doctoral research associates in our group. Interested candidates are welcome to contact me via mirza.galib@howard.edu.

 

Publications: ( h-index 12, citations 656)

( https://scholar.google.com/citations?user=ymI99ZAAAAAJ&hl=en )

1. S.Gosh, H. Agarwal, M. Galib et. al., “ Near-Quantitative Predictions of First-Shell Coordination Structure of Hydrated First-Row Transition-Metal Ions Using K-Edge X-ray Absorption Near-Edge Spectroscopy”,  J. Phys. Chem. Lt., 13,27,6323-6330, 2022.
2. H. Liu, M. Galib et. al. “Controlled Formation of Conduction Channel in Memristive Devices Observed by X-ray Multimodal Imaging”, Advanced Materials, 34(35), 2203209, 2022.
3. K. Bajaj, S. Andres, D. Hofsommer, M. Galib et. al., “Linkage Isomers of a Bis(alkylthiocarbamato)Copper Complex with Antiproliferation Activity”,  Inorg. Chem., 61, 20, 7715-7719, 2022.
4. V.W.D. Cruzeiro, M. Galib, D. T. Limmer, A. W. Goetz, “Uptake of N2O5 by aqueous aerosol unveiled using chemically accurate many-body potentials”  Nature Communications, 13, 1266, 2022.
5. S. P. Niblett, M. Galib, and D. T. Limmer, D.T., “Learning intermolecular forces at liquid-vapor interfaces”,  J. Chem. Phys., 155, 164101, 2021. [Editor's pick]
6. M. Galib and D.  Limmer, "Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial process", Science, 371.6532: 921-925, 2021.
7. N. Nguyen, M. Galib and A. Nguyen, "Critical Review on Gas Hydrate Formation at Solid Surfaces and in Confined Spaces: Why and How Does Interfacial Regime Matter?", Energy & Fuels, 34, 6, 6751-6760, 2020.
8. T. Duignan, T., G. K.  Schenter, M. Galib, M. D. Baer, J.  Wilhelm, J. Hutter et al., "Quantifying the hydration structure of sodium and potassium ions: taking additional steps on Jacob's Ladder." Phys. Chem. Chem. Phys., 22, 10641-10652, 2020.
9. M. Galib et al., "Unraveling the Spectral Signatures of Solvent Ordering in K-edge XANES of Aqueous Na+", J. Chem. Phys., 149(12), 124503, 2018. [Editor's pick]
10. M. Galib et al., "Supersaturated calcium carbonate solutions are classical." Science Advances, 4(1), eaa06283, 2018.
11. S. Roy, S., M. Galib, M., G. K. Schenter, & C. J. Mundy, "On the relation between Marcus theory and ultrafast spectroscopy of solvation kinetics" Chem. Phys. Lt., 692, 407-415, 2017. [Invited Frontier Article]
12. M. Galib, T. T. Duignan, G. K. Schenter, C. J.  Mundy, “Mass density fluctuations in Quantum and Classical descriptions of liquid water”, J. Chem. Phys., 146, 244501, 2017. [Featured by the editor, A Scilight “Using density functional theory to understand the properties of water” on this article was published in AIP Scilight]
13. M. Galib, M. Baer, L.B. Skinner, C. J. Mundy, T. Huthwelker, G. K. Schenter, C.J. Benmore, J. L. Fulton,  “Revisiting the hydration structure of aqueous sodium ions (Na+)”, J. Chem. Phys., 146, 084504, 2017.
14. N. Nguyen, A. Nguyen, K. Steel, L. Dang, M. Galib, "Interfacial Gas Enrichment at Hydrophobic Surfaces and the Origin of Promotion of Gas Hydrate Formation by Hydrophobic Solid Particles" J. Phys. Chem. C, 121(7), 3830-3840, 2017.
15. L.B. Skinner, M. Galib, J. L. Fulton, C. J. Mundy, J. B Parise, Van-Thai Pham, G. K. Schenter, C.J. Benmore, "The structure of liquid water up to 360 MPa from x-ray diffraction measurements using a high Q-range and from molecular simulation", J. Chem. Phys., 144, 134504, 2016.
16.  M Galib and G. Hanna, “Molecular Dynamics Simulations Predict an Accelerated Dissociation of H2CO3 at the air-water Interface”, Phys. Chem. Chem. Phys., 16, 25573-25582, 2014.   
17.  M. Galib and G. Hanna, “The role of hydrogen bonding in the decomposition of H2CO3 in water: Mechanistic insights from ab initio metadynamics studies of aqueous clusters” J. Phys. Chem. B, 118 (22), 5983-5993, 2014.
18.  M. Galib and G. Hanna, “Mechanistic insights into the dissociation and decomposition of carbonic acid in water via the hydroxide route: An ab-initio metadynamics study”, J. Phys. Chem. B, 115 (50), 15024–15035, 2011.
19.  S. T. A Islam, M. Z. Alam, M. Ismail, M. Galib, N. Sharif, and M. Saha, “A Statistical Approach to Alkylation of p-Cresol with Cyclopentanol”, Asian J. Chem., 22 (2), 1245-1250, 2010.
20.  M. Galib, M. Z. Alam, D. Saha, M. Ismail, S. T. A Islam, and M. Saha, “Application of Plackett-Burman Design to tert.-Methylcyclohexylation of p-Chorophenol”, Chem. Technol. Fuel Oils, 45 (5), 336-342, 2009.