Narayanan, Badri (University of Louisville)

Group Research Activities

Since the prehistoric discovery of bronze, development of new materials has always played a pivotal role in enabling transformative advancements in technology. For instance, steel was instrumental in empowering the 19th century Industrial revolution, while silicon in the 1970s, and more recently, lithium-ion batteries led to the emergence of the modern electronics/communication industry. Over the last few decades, pressing challenges in energy, global climate change, sustainability, and national security have necessitated design of novel functional materials at a rapid pace. Such unprecedented rate of innovations cannot be achieved by the traditional heuristic approaches involving repetitive, time-intensive, and intuition-driven experiments alone. Recent surge in high performance computing hardware, and availability of efficient algorithms for accurate prediction of material properties using quantum-chemical laws, have opened doors for the use of computational techniques to accelerate discovery of new materials. Nevertheless, the full potential of computations in designing materials for energy applications remains far from being realized, due to (a) dearth of accurate materials models that can capture complex materials chemistry over multiple length/time scales, (b) lack of fundamental understanding of chemical reactions, defect chemistry, solvation dynamics, transport phenomena, and structural evolution in multi-component material systems that underpin much of energy capture, conversion and storage by materials, and (c) paucity of robust codes to identify thermodynamically stable configurations of low-dimensional materials at any given composition. These issues need to be resolved urgently to truly realize the latent potential of computation methodologies in shifting the paradigm of materials design. Motivated by this immediate need, our research group (called Predictive Materials Modeling Laboratory) brings together innovations in multi-scale materials modeling, machine learning, and big-data analytics to address these grand challenges, with a focus on materials systems relevant for electrochemical energy storage, brain-like computing, and nanoelectronics.

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1. Design of electrolyte for rechargeable metal-sulfur batteries: The ever-growing demand for low-cost, high-energy density batteries in electric transportation, robotics, and grid storage has necessitated exploration of new electrochemistry beyond the conventional Li-ion technology. In this context, rechargeable metal-sulfur batteries (RMSBs) offer tremendous promise owing to high theoretical specific energy (~10 times Li-ion), natural abundance of materials, low-cost, and use of non-toxic components. Unfortunately, long-standing issues with polysulfide dissolution in electrolyte, flammability of electrolyte, high reactivity of the metal anodes, and dendrite proliferation have precluded development of safe RMSBs that can effectively compete with existing Li-ion technology. Most of these daunting issues hindering practical implementation of RMSBs stem from dearth of atomic-scale understanding of solvation chemistry, ion-transport, chemical reactions, and nucleation/growth of interphases at electrode/electrolyte interfaces. We employ a combination of first-principles calculations, ab initio/classical reactive molecular dynamics simulations and machine learning to gain such knowledge; and accelerate design of next-generation electrolytes for both conventional (liquid electrolyte) and emerging solid-state RMSBs.

2. Understanding defect dynamics in complex oxides for applications in brain-like computing: Resistive switching in correlated complex oxides is lucrative for emerging applications in neuromorphic computing, and densely scaled non-volatile memory. Electrical conductance of such complex oxides can be controllable switched across multiple orders of magnitude by either (a) electroforming a conduction channel (e.g., in tungsten oxide), or (b) inducing Mott-Hubbard transition (e.g., in rare-earth nickelates)– both via controlled migration of defects (such as oxygen vacancies) under applied bias. Nevertheless, the promise of such defect-driven electronic transitions are far from realized due to a lack of fundamental understanding of the atomic-scale processes that underlie migration and spatiotemporal evolution of oxygen vacancies over nano-to-mesoscopic length/timescales under applied electric field. In this project, we employ a synergistic integration of density functional theory (DFT) calculations, ab initio/classical molecular dynamics (AIMD/CMD) simulations, and machine learning (ML) to address this knowledge gap. Such an integrated approach offers to elucidate the correlations between subtle structural distortion and oxidation states; treat localized charge carriers; describe defect/ion transport in the presence of electric field; and, in turn, greatly advance the current understanding of microstructural evolution in complex oxides under applied bias. The fundamental knowledge gained from this work will enable precise control over hierarchical defect structures and unravel new routes to manipulate resistance states in complex oxides. This, in turn, will accelerate design of novel devices with desired set of neural functionalities, and high-speed densely-scaled resistive random access memory technologies.

3. Defect dynamics in two-dimensional materials for nano-electronic applications: Structural defects govern various physical, chemical, and optoelectronic properties of two-dimensional transition-metal dichalcogenides (TMDs). A fundamental understanding of the spatial distribution and dynamics of defects in these low-dimensional systems is critical for advances in nanotechnology. However, such understanding has remained elusive primarily due to the inaccessibility of (a) necessary timescales via standard atomistic simulations, and (b) required spatiotemporal resolution in experiments. To overcome these limitations, we combined genetic algorithms (GA), classical/ab initio molecular dynamics simulations to investigate the extended structure of point defects, their dynamical evolution, and their role in inducing the phase transitions in low-dimensional nanostructures.

4. Electrochemistry of anion-intercalated layered double hydroxides: Layered double hydroxides (LDHs) have two-dimensional positively charged nanosheets and host negatively charged ions and structural water molecules in the interlayer regions, offering advantages in a wide range of energy- and environment-related applications, including multivalent anion batteries, high-capacity desalination, and ion remediation. However, there is a lack of fundamental understanding of how the local structure and their atomic interaction with anions affect the reversible anion-hosting in LDHs. In particular, the interplay between ion-hydration, atomic transport, material defect, and charge transfer on anion insertion and extraction in transition metal oxide and hydroxide layered materials is not well understood. We combine density functional theory calculations, and classical molecular dynamics simulations to gain such knowledge.

5. Understanding electrochemical phenomena in lithium-sulfur batteries with ionic liquid electrolytes: Ionic liquid (IL) electrolytes offer tremendous promise to simultaneously control solvation structure, ion transport, and reaction pathways in a non-flammable environment for high-performance lithium-sulfur (Li-S) batteries. However, this promise remains far from being realized due to lack of fundamental understanding of connections between molecular structure of ionic liquids (ILs) and their stability and reactivity. The goal of this project is to integrate research and education in fundamental electrochemistry at multiple length/timescales to address this knowledge gap. Specifically, we propose to combine high-throughput density functional theory (HT-DFT) calculations, ab initio molecular dynamics (AIMD), machine learning (ML), and reactive molecular dynamics (RMD) simulations to gain atomic-scale understanding of the solvation chemistry, ion-transport, chemical reactions, and nucleation/growth of interphases at electrode/electrolyte interfaces. Additionally, we will leverage ML strategies to (a) computationally screen millions of IL electrolytes to identify promising candidates with prescribed electrochemical stability, (b) and develop reactive models that bridge different modeling scales.

Computational methods

Density functional theory (available in UKY)
Ab initio Molecular Dynamics (available in UKY)
Classical Molecular Dynamics (available in UKY)
Genetic Algorithms (codes developed in PI's group, can be installed and run on UKY cluster)
Machine Learning (codes developed in PI's group, can be installed and run on UKY cluster)

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VASP
CP2K
LAMMPS
Python

Collaborators

We have 4 PhD students in the group (at University of Louisville) who will be involved in the project