Materials Horizons Emerging Investigator Series: Dr Dibyajyoti Ghosh and Dr Satyaprasad P. Senanayak, Indian Institute of Technology, Delhi and National Institute of Science Education and Research, India

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Abstract

Our Emerging Investigator Series features exceptional work by early-career researchers working in the field of materials science.

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Dr Dibyajyoti Ghosh (ORCiD https://orcid.org/0000-0002-6752-2466) is an assistant professor at the Indian Institute of Technology (IIT) Delhi, specializing in computational materials science and condensed matter physics. He completed his PhD from the Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Bangalore, India, where he focused on first-principles calculations and materials modeling. He has worked at the University of Bath, UK and Los Alamos National Laboratory, USA as a post-doctoral fellow. Ghosh has made contributions to the field of computational materials design, particularly in predicting and optimizing the behaviour of complex materials at the atomistic levels. His work employs advanced computational techniques, such as density functional theory, ab initio and non-adiabatic molecular dynamics and machine learning, to discover new materials and tailor their properties for technological applications. At IIT Delhi, Dr Ghosh is actively involved in teaching and mentoring students, fostering a collaborative research environment.

Dr Satyaprasad P. Senanayak (ORCiD https://orcid.org/0000-0002-8927-<?pdb_no 685X?>685X<?pdb END?>) is a reader of physics leading the Nanoelectronics and Device Physics Lab at the National Institute of Science Education and Research (NISER), Bhubaneswar, Odisha, India. He received his PhD in physics from JNCASR, Bangalore (2009 – 2015) for his work on developing high performance, low power, fast switching polymer field effect transistors. He then pursued his research at the Optoelectronics Group, Cavendish Laboratory, University of Cambridge, working on developing high performance n-type and p-type perovskite field-effect transistors which outperform conventional inorganic devices. He was awarded the prestigious Royal Society Newton Fellowship, EPSRC Global Challenge Award and Royal Society Alumni Fellowship, Early Career Research Award from the Science and Engineering Research Board, Government of India and Young Scientist Award from Odisha Bigyan Academy. He is an early-career member of the American Physical Society, associate of the Indian Academy of Sciences and Odisha Bigyan Academy.

Read Dibyajyoti Ghosh's and Satyaprasad P. Senanayak's Emerging Investigator Series article ‘Silver-purine MOFs for high-performance multi-terminal neuromorphic memory’ ( https://doi.org/10.1039/D4MH01425A ) and read more about them in the interview below:

Materials Horizons (MH): Your recent Materials Horizons Communication demonstrates silver-purine MOFs for high-performance multi-terminal neuromorphic memory. How has your research evolved from your first article to this most recent article and where do you see your research going in future?

Dibyajyoti Ghosh (DG): When I look back, I find that my research interests have evolved significantly with time. However, at the core, they remain always the same: trying to understand complex processes on the atomistic scale using several computational tools. I must acknowledge the importance of collaboration here; the detailed discussion among various groups with complementary expertise, significantly helps to gain clarity on research problems. And over time, I have become more and more interested in working on collaborative projects that attack challenging problems in the field.

Now and in the near future, our plan is to develop highly accurate data-driven models to understand the complex processes, reveal underlining correlations, and build ML models for materials screening. In terms of neuromorphic computation, we are already working on a few projects that focus on strategic material design for next-generation device applications.

Satyaprasad P. Senanayak (SPS): My research began with exploring a fundamental problem related to charge transport in polymeric field-effect transistors using ferroelectric dielectrics. I investigated the role of dynamic disorder at the interface in a field-effect transistor. From thereon, my journey has led to numerous surprises and has helped address fundamental issues related to charge transport in solution processed semiconductors, leading to the demonstration of ultra-low voltage operating fast switching organic logic devices, achieved room temperature operation in n-type and p-type perovskite transistors. Currently, I am more focussed on the evolving varied advanced applications in neuromorphic computing, extending beyond conventional materials and integrating neuromphic memory with FET architecture.

MH: What aspect of your work are you most excited about at the moment?

DG: We are most excited about using various ML-based tools to explore materials modelling for a wide range of applications from optoelectronics to catalysis to neuromorphic computing. The predictive power of these well-trained models are revolutionizing the materials search to identify the most promising candidates for targeted applications. In our team, we develop several ML models, not only for regular materials screening but also for revealing non-trivial highly non-linear structure–property correlations for functional materials.

SPS: Currently, in the Nanoelectronics and Device Physics Lab at NISER we are excited to develop the integration of neuromorphic memory and computing. Our goal is to explore and incorporate complex configurability of neurons, including advanced multi-terminal learning synapses, dendritic processing, and axonal connections, using Artificial Intelligence and ML.

MH: In your opinion, what are the most important questions to be asked/answered in this field of research?

DG: I think the computational search guiding accelerated discovery of materials that could substantially mitigate climate change and provide sustainable energy generation, is one of the most crucial questions to be answered.

SPS: In the field of neuromorphic computing, a critical question is the feasibility of achieving neuronal scalability to support a large number of neurons on a single or multi-chip system with appropriate communication for large scale integration, ensuring compatibility and adoption with other computing resources.

MH: What do you find most challenging about your research?

DG: As a computational materials scientist, I see exponential growth in terms of computational power, efficient algorithms, and better models to tackle complex problems. However, achieving high accuracy within a reasonable computational cost is a big challenge for materials modelling. In particular, problems dealing with excited states, thermal conductivity and phase transition, often suffer due to the associated computational cost for gaining adequate accuracy from atomistic modelling.

SPS: Understanding the behaviour of materials in response to external stimuli and planning the best way to modulate the material properties in a device, is what I believe is the most challenging task for us. In other words, designing the device to realize optimum device performance from the material is one of the key challenges in my research.

MH: In which upcoming conferences or events may our readers meet you?

DG: I am planning to attend a few national conferences like TCS, HyPe and international conferences like MRS Fall this year.

SPS: Material Research Society (USA), European Materials Research Society (Europe), Quantum Materials Conference (India) and the International Conference of Functional Materials (India), are some of the conferences where I will meet my peers to discuss our research and explore possible collaborations.

MH: How do you spend your spare time?

DG: I play football, read fiction and non-fiction, and spend time with family.

SPS: I spend my spare time travelling to the countryside or seaside with my family.

MH: Can you share one piece of career-related advice or wisdom with other early career scientists?

DG: Cultivate a growth mindset and embrace failure as part of the learning process. Science is inherently about exploration and discovery, which means setbacks, unexpected results, and even outright failures are inevitable. Instead of viewing these as discouragements, see them as opportunities to learn, refine your approach, and grow.

SPS: Materials science research is a highly engaging and dynamic field that is continuously evolving, with materials exhibiting novel properties previously unimaginable. These advancements encompass both fundamental physics, such as quantum criticality and neural networks, as well as various device applications. Pursuing a career in materials science is both enriching and challenging, offering opportunities for significant breakthroughs when we foster out-of-the-box thinking and embrace unconventional ideas and calculated risks. Early-career scientists should cultivate innovation and maintain a balanced approach to both fundamental and application-oriented research to ensure success in their scientific endeavours.

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