Manzoor Ahmad Dar is an Assistant Professor in the Department of Chemistry, Islamic University of Science and Technology (IUST), J&K, India. He completed his Master’s degree in Physical Chemistry from the University of Kashmir and PhD from CSIR-National Chemical Laboratory, Pune. He later worked as a postdoctoral fellow in the Department of Chemistry at IISER Bhopal after which he joined IUST. His research focusses on data-driven approaches, including high-throughput first principles simulation based screening and machine learning for accelerating the discovery of stable single and double atom catalysts for energy conversion processes such as CO2RR and NRR while accounting for stability, aggregation resistance, and competitive reactions such as HER.
Read Manzoor’s Emerging Investigators Series article “Nickel single atom catalyst supported on the gallium nitride monolayer: first principles investigations on the decisive role of support in the electrocatalytic reduction of CO2” and read more about him in the interview below:
Your recent Emerging Investigators Series article focuses on Nickel single atom catalyst supported on the gallium nitride monolayer: first principles investigations on the decisive role of support in the electrocatalytic reduction of CO2. How has your research evolved from your first article?
My research in computational catalyst design for single-atom catalysts (SACs) has evolved from simple activity screening toward a more holistic, mechanism-driven and materials-realistic framework for energy conversion reactions such as CO₂ reduction (CO₂RR) and nitrogen reduction (NRR). Early studies from our group largely focused on identifying SACs on ideal supports using adsorption energies and limiting potentials as descriptors, establishing structure–activity relationships for key intermediates (*CO₂⁻, *COOH, *N₂, *N₂H). More recently, our efforts have expanded to double-atom catalysts (DACs), where synergistic electronic and geometric interactions between adjacent metal sites offer enhanced catalytic activity and improved reaction selectivity. In parallel, we have increasingly incorporated solvent effects to bridge the gap between idealized theoretical models and realistic electrochemical operating conditions. Furthermore, we employ data-driven strategies, including high-throughput screening and machine-learning approaches, to accelerate the discovery of stable and aggregation-resistant SAC/DAC motifs while explicitly accounting for competitive pathways such as the hydrogen evolution reaction (HER). Collectively, these advances reflect a clear transition from simple descriptor-based screening toward predictive, operando-relevant computational design of atomic-scale catalysts for sustainable energy conversion.
What aspect of your work excites you most right now?
The most exciting aspect of computational catalyst design for energy conversion reactions is the unprecedented ability to rationally engineer catalytic sites at the atomic level. First-principles simulations allow us to precisely correlate coordination environment, electronic structure, and reaction energetics, revealing how isolated metal atoms or synergistic bimetallic pairs break traditional scaling relationships and selectively stabilize key intermediates (*COOH, *CO, *N₂H, *NH₂). Coupled with machine learning and high-throughput screening, computational design is transforming catalyst discovery from trial-and-error to predictive, mechanism-driven optimization, accelerating the development of highly selective, low-overpotential catalysts for sustainable CO₂ conversion and ammonia synthesis.
Which profession would you choose if you weren’t a scientist?
If I weren’t a scientist, I would choose to be a teacher of poetry, a profession that blends the joy of guiding minds with the freedom of creative expression. Teaching would allow me to nurture curiosity, critical thinking, and a love for learning, much like science does, but through stories, discussions, and shared reflection. Poetry, on the other hand, would give me a language to explore emotions, nature, and human experiences beyond equations and data. Together, teaching and poetry would let me inspire others not only to understand the world, but also to feel it deeply, turning knowledge into meaning and learning into a lifelong conversation.
What one piece of career advice would you share with other early career scientists?
I would advise early career scientists to be patient and persistent, and to focus on developing a strong fundamental understanding rather than chasing trends. Building depth in one’s expertise, maintaining curiosity, and embracing interdisciplinary collaborations can lead to more meaningful and sustained research contributions. Rejections and setbacks are part of the process; treat them as feedback rather than failure.
How do you feel about Sustainable Energy & Fuels as a place to publish research on this topic?
Sustainable Energy & Fuels is an excellent platform for publishing research on energy conversion processes, as it sits at the intersection of fundamental science and real-world sustainability challenges. The journal values mechanistic insight, rigorous theory–experiment synergy, and clear relevance to low-carbon energy technologies, which aligns well with studies on electrocatalytic pathways, active-site engineering, and reaction selectivity in CO₂ and N₂ conversion. Its broad readership across chemistry, materials science, and energy research ensures strong visibility, while the emphasis on sustainability encourages authors to frame catalytic performance in terms of efficiency, scalability, and environmental impact rather than isolated metrics. Overall, it provides a credible and high-impact platform for advancing and contextualizing fundamental advances in CO₂RR and NRR within the global energy transition.
