Dr. Biswarup Pathak, FNASc, FRSc
Associate Editor for ACS Applied Materials & Interfaces
Department of Chemistry
PoD Building, Room Number: 1A 724
Phone (Office): 0731-660-3348; Lab:5157
Email: biswarup[at]iiti.ac.in and biswarup.pathak[at]gmail.com
Research Areas
Our research group is dedicated to tackling pressing problems for the nation and the world at large using advanced computational modeling and machine learning techniques applied to nanomaterials, particularly nanoclusters. Our efforts have yielded significant contributions in key areas such as energy, catalysis, and bio-applications within a remarkably short timeframe.
Nanoclusters/Catalysis: Our group focuses on studying the catalytic activity of small-sized nanoclusters (~1-2 nm) with specific surface facets (e.g., 111, 100). Contrary to conventional wisdom, our research, as outlined in our review articles (WIREs Computational Molecular Science, 1508, 1, 2021), reveals that catalytic activity is not solely dependent on the number of unsaturated sites. In fact, excessive unsaturation can lead to surface poisoning, thereby diminishing catalyst performance. Furthermore, we have highlighted the significance of cluster fluxionality, especially in nanoclusters smaller than 1 nm. These findings provide crucial atomic-level insights into the mechanisms underlying the superior catalytic activity of nanoclusters compared to their bulk counterparts, promising significant impact within the scientific community.
Dual-ion Batteries: Our research group is actively involved in the computational design of nanomaterials for energy applications, with a particular focus on dual-ion batteries (DIBs). We specialize in developing nanomaterial-based electrodes tailored for DIBs and compare their performance with bulk systems. In 2017, our group introduced the staging mechanisms of dual-ion batteries, a seminal contribution published in Physical Chemistry Chemical Physics (19, 7980, 2017), which has been widely cited (~150 times). This pioneering work garnered significant interest in the scientific community, leading to a collaboration with Prof. Maksym V. Kovalenko's group at ETH Zurich, Switzerland. Together, we successfully developed a high-voltage dual-ion battery (DIB) achieving 4.7 V, as reported in Nature Communications (9, 4469, 2018), which has been cited over 100 times. Subsequently, we have continued to advance the field by proposing staging mechanisms for dual-ion batteries, elucidating solid-electrolyte phase formation mechanisms, and designing nanostructured electrode materials specifically for DIBs. Through these efforts, we aim to drive forward the development of efficient energy storage solutions using computational insights and innovative nanomaterial designs, contributing significantly to the field of battery technology.
DNA Sequencing: Since 2012, our research team has focused on advancing solid nanopores for ultrafast DNA sequencing, a promising technique for rapid genetic analysis. The recent COVID-19 pandemic underscored the critical need for swift DNA sequencing technologies. Our work includes employing computational methods such as transverse and ionic currents for third-generation sequencing of DNA and proteins. Our significant contributions, featured in publications like the Nano Letter (2023) and ACS Materials Letters (2023), as well as Digital Discovery (2023), have garnered considerable attention within the scientific community.
Machine Learning: Our primary focus is to elucidate the behaviors of nanoclusters and nanomaterials critical to various applications using Machine Learning (ML) and Artificial Intelligence (AI). These materials are renowned for their exceptional catalytic activities across diverse reactions. Leveraging ML techniques, we've conducted extensive screening of high-entropy catalysts to predict their efficacy and product selectivity in CO2 reduction reactions. Our approach employs supervised learning methodologies tailored to our expertise in this domain, demonstrating their utility in catalyst screening. Our research highlights the capability of ML to not only assess CO2 reduction catalysts but also predict product selectivity. In a recent study, we observed that alloy-based catalysts, particularly ternary and quaternary alloys, exhibit higher efficiency in achieving methanol selectivity compared to high-entropy alloy counterparts. These findings are detailed in our latest publications.
Nanoclusters/Catalysis: Our group focuses on studying the catalytic activity of small-sized nanoclusters (~1-2 nm) with specific surface facets (e.g., 111, 100). Contrary to conventional wisdom, our research, as outlined in our review articles (WIREs Computational Molecular Science, 1508, 1, 2021), reveals that catalytic activity is not solely dependent on the number of unsaturated sites. In fact, excessive unsaturation can lead to surface poisoning, thereby diminishing catalyst performance. Furthermore, we have highlighted the significance of cluster fluxionality, especially in nanoclusters smaller than 1 nm. These findings provide crucial atomic-level insights into the mechanisms underlying the superior catalytic activity of nanoclusters compared to their bulk counterparts, promising significant impact within the scientific community.
Dual-ion Batteries: Our research group is actively involved in the computational design of nanomaterials for energy applications, with a particular focus on dual-ion batteries (DIBs). We specialize in developing nanomaterial-based electrodes tailored for DIBs and compare their performance with bulk systems. In 2017, our group introduced the staging mechanisms of dual-ion batteries, a seminal contribution published in Physical Chemistry Chemical Physics (19, 7980, 2017), which has been widely cited (~150 times). This pioneering work garnered significant interest in the scientific community, leading to a collaboration with Prof. Maksym V. Kovalenko's group at ETH Zurich, Switzerland. Together, we successfully developed a high-voltage dual-ion battery (DIB) achieving 4.7 V, as reported in Nature Communications (9, 4469, 2018), which has been cited over 100 times. Subsequently, we have continued to advance the field by proposing staging mechanisms for dual-ion batteries, elucidating solid-electrolyte phase formation mechanisms, and designing nanostructured electrode materials specifically for DIBs. Through these efforts, we aim to drive forward the development of efficient energy storage solutions using computational insights and innovative nanomaterial designs, contributing significantly to the field of battery technology.
DNA Sequencing: Since 2012, our research team has focused on advancing solid nanopores for ultrafast DNA sequencing, a promising technique for rapid genetic analysis. The recent COVID-19 pandemic underscored the critical need for swift DNA sequencing technologies. Our work includes employing computational methods such as transverse and ionic currents for third-generation sequencing of DNA and proteins. Our significant contributions, featured in publications like the Nano Letter (2023) and ACS Materials Letters (2023), as well as Digital Discovery (2023), have garnered considerable attention within the scientific community.
Machine Learning: Our primary focus is to elucidate the behaviors of nanoclusters and nanomaterials critical to various applications using Machine Learning (ML) and Artificial Intelligence (AI). These materials are renowned for their exceptional catalytic activities across diverse reactions. Leveraging ML techniques, we've conducted extensive screening of high-entropy catalysts to predict their efficacy and product selectivity in CO2 reduction reactions. Our approach employs supervised learning methodologies tailored to our expertise in this domain, demonstrating their utility in catalyst screening. Our research highlights the capability of ML to not only assess CO2 reduction catalysts but also predict product selectivity. In a recent study, we observed that alloy-based catalysts, particularly ternary and quaternary alloys, exhibit higher efficiency in achieving methanol selectivity compared to high-entropy alloy counterparts. These findings are detailed in our latest publications.