Liquid phase separations

My Ph.D. work with Dr. Sanket Deshmukh in the Department of Chemical Engineering at Virginia Tech aims at the integration of artificial intelligence (AI) and molecular simulations for the discovery and design of hybrid and functionalized materials, including metal-organic frameworks (MOFs), 2D sheets like graphene, MoS₂, for biomedical and energy applications.

Published work:

MOFs due to their adjustable pore size and void fraction, large volumetric surface area, high gravimetric density, and tunable physical and chemical properties are excellent adsorbent materials. As a drug delivery vehicle, the experiments of MOFs are conducted in the presence of solvents like ethanol, water, DMF, and so on. However, in modeling drug adsorption, researchers often ignore the solvents and neglect the critical role they may play in the adsorption process. Therefore, one of my recent works emphasized on studying the primary role of solvent in modeling drug adsorption in metal-organic frameworks, which was published in RSC advances.1 Moreover, a significant part of my research also focuses on the development of all-atom (AA) and coarse-grained (CG) models for suitable applications, that are accelerated by optimization algorithms, such as particle swarm optimization (PSO) and genetic algorithm (GA). Due to its many potential uses, including energy storage, sensor, and lubricant, molybdenum disulfide (MoS₂) has been receiving a lot of attention from the scientific community. However, MoS₂'s performance is affected by environmental conditions like temperature, pressure, and especially humidity. Recently my work on the development of forcefield interactions between 2D sheet MoS₂ and water was published and featured as supplementary cover in The Journal of Physical Chemistry C.2 This study was followed by the investigation of the structure and dynamics of water confined between hybrid 2D sheets of graphene, boron nitride and molybdenum disulfide, which was published in The Journal of Materials Science as an invited article.3 Currently, I am working on the development of embedded atom method (EAM) interaction potentials for the coarse-grained (CG) model for FCC metals. To develop a fundamental understanding of the sensitivity of the output to these errors, we describe the uncertainty of input parameters through the learnings from the output. The Gaussian process regression (GPR) assisted Bayesian parameter estimation (BPE) was employed to determine the uncertainty quantification of developed models.

Research Interests and current work:

The current global problems demand the efficient sequestration of energy and separation for desired applications. Novel functionalized MOFs with appropriate physical and chemical attributes can be key adsorbent and membrane materials to fulfill our needs. The performance of traditional polymer membranes can be enhanced by reinforcing them with nanoporous fillers like MOFs, known as mixed matrix membranes (MMMs). However, researchers find it challenging to synthesize these MMMs due to the phase separation of bulk polymer and MOF particles. With an aim to gain a fundamental understanding and improve compatibility, my current research work focuses on studying the molecular-level interfacial characteristics of polymer and MOF. This includes employing molecular dynamics simulations to functionalize the MOF with appropriate functional groups with the polymers of different physical and chemical characteristics.

Moreover, to tackle the problem of global warming, DOE issued a target for energy (H2) storage in addition to reducing greenhouse gases (CO2, CH4). MOFs due to their adjustable pore size and void fraction, large volumetric surface area, high gravimetric density, and tunable physical and chemical properties have shown the ability to capture and store such gases (H2 and CO2). Functionalizing MOFs with appropriate F.Gs results in enhanced gas capture. However, it remains challenging to explore the large design space due to the availability of a large number of functional groups in addition to tens of thousands of MOFs. Thus studying the critical role that these functional groups play in enhancing the adsorbent properties of MOFs is a cumbersome task. To this aid, machine learning has come to the rescue since it enables us to explore this parameter space with minimum computational cost and time. In the light of this, my passion and interest lie in the rapidly developing field of artificial intelligence (AI), which has a remarkable capability to accelerate material design through computational simulations, including Molecular Dynamics (MD) and Monte Carlo (MC).

In my role as a chemical engineer, my goal is to gain a deep fundamental understanding of the functions and behavior of various materials at the nanoscale and microscale levels using computational modeling techniques. Given the knowledge I have gained to date in my Ph.D. journey, I would like to exploit the spectacular aspects of AI to analyze, predict, optimize and accelerate material discovery for energy, biomedical, electrochemical, and nuclear applications.

Skills acquired:

In the area of molecular dynamics (MD), I have gained experience in modeling both all-atom and coarse-grained simulations in molecular dynamics (MD) using LAMMPS and NAMD software. Furthermore, through gas and drug adsorption simulations in RASPA 2.0, I have gained an understanding of grand canonical Monte Carlo (GCMC) methods. My involvement in the development of interatomic forcefield potentials for both AA and CG models provided me with valuable experience in metaheuristic and stochastic optimization techniques. Moreover, I am familiar with machine learning (ML) and deep neural networks (DNN) due to their extensive applications in the design of novel hybrid materials.

Selected Publications:

1. Investigation of structure and dynamics of water confined between hybrid layered materials of graphene, boron nitride, and molybdenum disulfide
   AT Sose, E Mohammadi, F Wang, SA Deshmukh The Journal of Materials Science 2022, 1-18 (Invited article)
2. Determination of accurate interaction parameters between the molybdenum disulfide and water to investigate their Interfacial properties
   AT Sose, E Mohammadi, PF Achari, SA Deshmukh, The Journal of Physical Chemistry C, 2022, 126 (4), 2013-2022
3. Dual-Force Zone Nonequilibrium Molecular Dynamics Simulations on Nanoporous Metal-Organic Framework Membranes for Separation of H2/CH4 Mixtures
   F Wang, AT Sose, S Singh, SA Deshmukh, ACS Applied Nanomaterials, 2022, 5 (3), 4048-4061
4. Modelling drug adsorption in metal-organic frameworks: the role of solvent
   AT Sose, HD Cornell, BJ Gibbons, AA Burris, AJ Morris, SA Deshmukh, RSC Advances, 2021, 11 (28), 17064-17071

References:

(1) Sose, A. T.; Cornell, H. D.; Gibbons, B. J.; Burris, A. A.; Morris, A. J.; Deshmukh, S. A. Modelling Drug Adsorption in Metal-Organic Frameworks: The Role of Solvent. RSC Adv. 2021, 11, 17064–17071.

(2) Sose, A. T.; Mohammadi, E.; Achari, P. F.; Deshmukh, S. A. Determination of Accurate Interaction Parameters between the Molybdenum Disulfide and Water to Investigate Their Interfacial Properties. J. Phys. Chem. C.

(3) Sose, A. T.; Mohammadi, E.; Wang, F.; Deshmukh, S. A. Investigation of Structure and Dynamics of Water Confined between Hybrid Layered Materials of Graphene, Boron Nitride, and Molybdenum Disulfide. J. Mater. Sci. 2022, 57, 10517–10534.