Author ORCID Identifier
0009-0009-4426-9427
Document Type
Dissertation
Date of Award
12-31-2025
Degree Name
Doctor of Philosophy in Chemistry - (Ph.D.)
Department
Chemistry and Environmental Science
First Advisor
Farnaz A .Shakib
Second Advisor
Zeyuan Qiu
Third Advisor
Yanchao Zhang
Fourth Advisor
Michael Scott Eberhart
Fifth Advisor
Neepa T. Maitra
Abstract
Metal-organic frameworks (MOFs), with their modular architectures and tunable properties, represent an especially rich domain for accelerated material design and discovery for a range of diverse applications. Within this class of multifunctional materials, two-dimensional (2D) electrically conductive MOFs (EC MOFs) are of particular interest, as their 7r-stacked layered structures combine permanent porosity with electronic conductivity, enabling potential breakthroughs in energy storage, energy conversion, and quantum sensing. But the discovery and design of new EC MOFs based on expensive experimental screening is increasingly impractical due to the infinite chemical space. Furthermore, the practical implementation of EC MOFs for specific tasks depends on an atomistic level understanding of their structure-property-function relationship. And their structural flexibility, interlayer interactions, and dynamic response to the environment remain difficult to capture using conventional computational approaches.
In this dissertation, a data-driven framework was developed to address these challenges. First, by utilizing a comprehensive EC-MOF database, high-throughput density functional theory (DFT) computations are combined with machine learning (ML) regressors and classifiers for ranking and property prediction tasks. By employing carefully selected descriptors, these models achieve formation energy predictions with 95% accuracy and reliably distinguish between metallic and semiconducting systems, thereby enabling the rapid pre-screening of large structural libraries. Next, to bridge the gap between static calculations and dynamic simulations, the ab initio force fields (AIFFs) were developed for Co3(HHTP)2 and Cu3(HHTP)2, HHTP=2,3,6,7,10,11-hexahydroxytriphenylene, as two representatives of the layered family of 2D MOFs. The molecular dynamics (MD) simulations reproduce experimentally observed stacking patterns and reveal the existence of intrinsic deformation sites that govern water adsorption and hydrolysis. Third, to further improve the accuracy and efficiency of dynamic studies, the machine learning potentials (MLPs), namely high-dimensional committee neural network potentials (CNNPs) were developed for 2D MOFs for long-time and large-scale MD simulations. Active learning was used to maintain accuracy while reducing training costs. The CNNP models developed in this thesis, enable MD simulations on supercells that are an order of magnitude larger and longer than those achievable with DFT, while accurately reproducing the dynamic flexibility of the hierarchical framework. Finally, the fourth-generation neural network potentials (4G-NNPs), which combine short-range atomic energies with environment-dependent charges, was applied and validated These models achieve superior force accuracy and capture non-local electrostatics critical to interlayer coupling and adsorbate interactions.
Together, these contributions bridge static quantum calculations and long-timescale simulations, providing transferable and accurate models for complex 2D EC MOFs. By integrating quantum chemistry, database-driven machine learning, and next-generation neural network potentials, this work establishes a pathway for accelerated discovery of stable, conductive, and flexible low-dimensional materials, paving the way for their deployment in energy conversion and storage applications.
Recommended Citation
Shi, Yuliang, "Data-driven analysis and atomistic simulations of next-generation materials for energy conversion and storage" (2025). Dissertations. 1868.
https://digitalcommons.njit.edu/dissertations/1868
Included in
Computational Chemistry Commons, Materials Science and Engineering Commons, Physical Chemistry Commons
