Dr. Nabil Khossossi

Dr. Nabil Khossossi - Research Overview

Computational Materials Electrochemistry

For Energy and Sustainability

Pioneering interdisciplinary research at the intersection of materials science, electrochemistry, and artificial intelligence to accelerate the discovery of next-generation energy materials and sustainable technologies.

Research Vision

Renewable energy sources, such as solar and wind, have tremendous potential to minimize our dependence on fossil fuels and reduce greenhouse gas emissions. However, the intermittent and uncertain nature of photovoltaics and wind power creates significant challenges for traditional electricity grids at all levels. To make renewable energy a major contributor to the future energy portfolio—available whenever and wherever needed—we must develop efficient and cost-effective solutions for energy storage and conversion.

                    "My research is positioned at the crossroads of interdisciplinary energy research, with the ultimate goal of achieving unprecedented control of materials properties for next-generation energy applications while contributing to fundamental progress in materials electrochemistry and solid-state chemistry."
                

Storing grid electricity in batteries or catalytically converting electrical energy to renewable fuels and chemicals—such as hydrocarbons, alcohols, and ammonia—can overcome the mismatch between renewable energy production and demand. The development of these technologies requires efficient electrochemical materials that can operate at appropriate temperatures with minimal energy losses.

Revolutionary advances in materials chemistry are essential for discovering new materials with unprecedented predictions and functionalities. My research focuses on designing and predicting novel materials while exploring their physical, chemical, and electrochemical properties, as well as their applications in energy-related systems, including batteries, catalysis, and hydrogen utilization. To expedite the materials discovery process, I employ a combination of cutting-edge approaches, including Density Functional Theory, Classical Molecular Dynamics, and Machine Learning-based interatomic potentials.

Research Focus Areas

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Advanced Energy Storage

Developing next-generation battery materials and systems, including lithium-sulfur batteries, solid-state electrolytes, and novel electrode architectures. Focus on understanding interfacial phenomena, polysulfide shuttle mechanisms, and ion transport at the atomic level.

Li-S Batteries Solid-State Electrode Design Ion Transport Interface Engineering

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Electrocatalysis & Energy Conversion

Designing efficient catalysts for crucial energy conversion reactions, including CO₂ reduction, hydrogen evolution, and oxygen reduction. Emphasis on single-atom catalysts, 2D materials, and understanding structure-activity relationships at the molecular level.

CO₂ Reduction HER/OER Single-Atom Catalysts 2D Materials Reaction Mechanisms

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AI-Driven Materials Discovery

Leveraging machine learning and artificial intelligence to accelerate materials discovery and property prediction. Developing few-shot learning models, high-throughput screening frameworks, and physics-informed neural networks for materials design.

Machine Learning Few-Shot Learning High-Throughput Property Prediction Neural Networks

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Computational Methods & Theory

Advancing computational approaches in materials science, including density functional theory, molecular dynamics simulations, and multiscale modeling. Developing new theoretical frameworks to understand complex electrochemical phenomena.

DFT Molecular Dynamics Multiscale Modeling Electronic Structure Quantum Chemistry

Computational Methodology

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Density Functional Theory

First-principles quantum mechanical calculations to understand electronic structure, chemical bonding, and reaction mechanisms in energy materials.

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Molecular Dynamics

Classical and ab-initio molecular dynamics simulations to study atomic-scale dynamics, phase transitions, and transport properties.

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Machine Learning

Advanced AI algorithms including neural networks, few-shot learning, and high-throughput screening for accelerated materials discovery.

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Multiscale Modeling

Bridging different length and time scales from quantum to continuum to understand complex electrochemical systems comprehensively.