Arunima Singh | Computational Methods | Best Researcher Award

Published on by High Energy Physics

Prof. Arunima Singh | Computational Methods | Best Researcher Award

Assistant Professor at Arizona State University | United States

Dr. Arunima K. Singh is an Assistant Professor in the Department of Physics at Arizona State University (ASU) and a graduate faculty member in Materials Science and Engineering. Her research bridges computational materials science, applied physics, and machine learning, focusing on discovering novel materials for energy and electronic applications. She holds a Ph.D. from Cornell University and has conducted postdoctoral research at both NIST and Lawrence Berkeley National Lab. With over 57 publications, her work is highly regarded in the scientific community, earning prestigious awards, editorial roles, and invitations to speak globally on advanced materials research.

šŸ‘Øā€šŸŽ“Profile

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šŸŽ“ Early Academic Pursuits

Dr. Singh’s academic journey began with a B.Tech. (Honors) in Metallurgical and Materials Engineering from IIT Kharagpur, where she earned multiple academic awards and graduated with a departmental silver medal. She pursued graduate studies at Cornell University, receiving both M.S. and Ph.D. degrees in Materials Science and Engineering, with a minor in Applied Physics. Under the guidance of Prof. Richard G. Hennig, her doctoral work focused on theoretical materials design. Her education was supported by prestigious fellowships including the McMullen Fellowship and Dow Chemical Fellowship, laying a strong foundation for her future research career.

šŸ’¼ Professional Endeavors

Dr. Singhā€™s professional experience spans national labs and academia. Following her Ph.D., she held postdoctoral appointments at the National Institute of Standards and Technology (NIST) and Lawrence Berkeley National Lab (LBNL), collaborating with leaders like Dr. Francesca Tavazza and Prof. Kristin Persson. Since 2018, she has been a faculty member at ASU, where she also contributes as a graduate mentor and research leader. Beyond teaching and research, she serves on editorial boards, national committees, and plays an active role in shaping research programs in the DOE Energy Frontier Research Center and TMS divisions.

šŸ”¬ Contributions and Research Focus

Dr. Singh specializes in computational materials discovery, leveraging density functional theory (DFT), GW-BSE methods, and machine learning to uncover materials for photocatalysis, solar energy, and 2D electronics. She has developed high-throughput workflows like pyGWBSE, enabling scalable simulations for optoelectronic properties. Her notable contributions include predictive models for nanoscroll formation, ultra-wide band gap semiconductors, and surface film protectiveness. She is a pioneer in integrating AI techniques with first-principles simulations, pushing the boundaries of how materials are discovered and optimized for real-world applications, with her work often featured in high-impact journals like npj Computational Materials and Advanced Functional Materials.

šŸŒ Impact and Influence

With over 4,300 citations, an h-index of 25, and continuous recognition in global venues, Dr. Singhā€™s influence is widespread. Her research has made foundational contributions to photocatalytic energy materials, grain boundary physics, and 2D nanomaterials. She has mentored students who have gone on to win prestigious poster and research awards, reflecting her impact as an educator and scientist. Invited to give keynote speeches and colloquia across institutions, from Caltech to international webinars, she is recognized as a thought leader in her field. She plays a key role in shaping policy and research strategy through MaRDA, DOE, and TMS platforms.

šŸ“Š Academic Cites

Dr. Singhā€™s work has been published in top-tier journals like npj 2D Materials & Applications, Nano Letters, and Annual Review of Condensed Matter Physics. Her publications are frequently cited, reflecting both depth and breadth of research impact across fields including computational materials science, nanotechnology, and machine learning in physics. Her most cited works address COā‚‚ reduction photocatalysts, vibrational EELS theory, and strain-induced nanoscrolls. As of March 2025, her Google Scholar profile records 4,396 citations, a 25 h-index, and 35 i10-index, a clear testament to the lasting relevance and utility of her contributions in cutting-edge research.

šŸ§Ŗ Research Skills

Dr. Singh brings expertise in first-principles simulations, high-throughput computing, and machine learning for materials design. She has built custom computational workflows like pyGWBSE and developed data-driven algorithms for stability and performance prediction. Her skillset includes GW-BSE optical simulations, phonon and defect state analysis, and interface science. She collaborates with both theory and experiment teams, enhancing the real-world applicability of her computational models. Proficient in Python, VASP, Quantum ESPRESSO, and emerging AI frameworks, her skills position her at the frontier of materials informatics, enabling novel discoveries in photocatalysis, electronics, and energy storage.

šŸ‘©ā€šŸ« Teaching Experience

As an Assistant Professor at ASU, Dr. Singh has taught and mentored students in Physics and Materials Science, often integrating cutting-edge research topics into her coursework. Her mentorship has led to student-led publications, poster awards, and graduate research accolades. She actively supervises Ph.D. students, guiding them through interdisciplinary research spanning condensed matter physics, AI in materials, and 2D materials design. Beyond classroom teaching, she regularly delivers technical workshops, participates in graduate admissions, and contributes to curriculum development. Her commitment to fostering the next generation of scientists is evident in her consistent student-centered approach.

šŸ† Awards and Honors

Dr. Singh has earned numerous national and institutional accolades, including the 2023 DOE Early Career Research Award, the 2024 TMS Young Leaders Professional Development Award, and several graduate fellowships from Cornell and Dow Chemical. She has been recognized for her contributions to women in applied physics, being featured in special issues and highlighted by AIP. Her students have also received competitive honors, reflecting her impact as a mentor. These awards underscore her leadership, innovation, and dedication to excellence in research and education, solidifying her status as a standout researcher in materials physics and computational science.

šŸ”® Legacy and Future Contributions

Dr. Singh is on a trajectory to become a defining voice in AI-enabled materials design and computational physics. Her legacy will likely include tools and frameworks that democratize high-performance computing for materials discovery. As she continues to shape research agendas at DOE centers and through editorial influence, her work will foster sustainable energy solutions, new semiconductor technologies, and broader STEM participation. With a proven record of mentoring, publishing, and innovating, Dr. Singh is building a future where data, physics, and computation converge to revolutionize how materials power the world.

Top Noted Publications

Many-body physics and machine learning enabled discovery of promising solar materials
  • Authors: T. Biswas, A. Gupta, and A. K. Singh*
    Journal: RSC Advances
    Year: 2025
Predicting the structure and stability of oxide nanoscrolls from dichalcogenide precursors
  • Authors: A. Gupta, and A. K. Singh*
    Journal: APL Materials
    Year: 2025
Atomic-Resolution Mapping of Localized Phonon Modes at Grain Boundaries
  • Authors: B. Haas, T. M. Boland, C. Elsasser, A. K. Singh, K. March, J. Barthel, C. T. Koch, and P. Rez
    Journal: Nano Letters
    Year: 2023
Ab Initio-Based Metric for Predicting the Protectiveness of Surface Films in Aqueous Media
  • Authors: R. Gorelik, and A. K. Singh*
    Journal: npj Materials Degradation
    Year: 2023
pyGWBSE: A High Throughput Workflow Package for GW-BSE Calculations
  • Authors: T. Biswas, and A. K. Singh*
    Journal: npj Computational Materials
    Year: 2023

 

 

Zhaocang Meng | Computational Methods | Best Researcher Award

Published on by High Energy Physics

Assist. Prof. Dr. Zhaocang Meng | Computational Methods | Best Researcher Award

Institute of Modern Physics, Chinese Academy of Sciences | China

Dr. Zhaocang Meng is a materials physicist specializing in first-principles simulations, irradiation damage modeling, and additive manufacturing of advanced materials. He earned his Ph.D. in Science through a joint program between the Institute of Modern Physics, Chinese Academy of Sciences (CAS) and Lanzhou University. His research spans the atomic-scale behavior of defects, mechanical property evaluation, and high-throughput screening for material optimization. Currently based at the Institute of Modern Physics, CAS, he is an integral contributor to strategic projects funded by both national and provincial Chinese foundations.

šŸ‘Øā€šŸŽ“Profile

Scopus

šŸŽ“ Early Academic Pursuits

Dr. Meng began his academic journey at Northwest Normal University, majoring in Physics and Electronic Engineering, where he laid the groundwork in material science and theoretical physics. He continued his master’s studies at the Institute of Modern Physics, CAS, focusing on radiation effects and material behavior. His intellectual curiosity and growing expertise led to a Ph.D. (2018ā€“2021) in a joint doctoral program between CAS and Lanzhou University, where he honed his skills in density functional theory (DFT) and multi-scale simulations, preparing him for a robust career in theoretical and computational materials science.

šŸ’¼ Professional Endeavors

Since July 2021, Dr. Meng has served as a researcher at the Institute of Modern Physics, Chinese Academy of Sciences, contributing to major national research initiatives, including the CAS Strategic Priority Program. His role encompasses both theoretical modeling and applied computation for nuclear-grade materials, ceramics, and metallic systems. He is actively involved in Grain Boundary Segregation Engineering for SiC and BeO, and supports the development of neural network potentials. His practical contributions extend to thermophotovoltaic energy systems and irradiation-resilient structural materials, demonstrating a bridge between computational insight and real-world application.

šŸ”¬ Contributions and Research FocusĀ 

Dr. Mengā€™s primary contributions lie in the atomistic modeling of radiation-induced defects, grain boundary behavior, and mechanical performance of ceramics and metals. His first-principles investigations in materials like Tiā‚ƒAlCā‚‚, BeO, SiC, and Beā‚ā‚‚Ti have revealed novel insights into defectā€“impurity interactions, hydrogen/helium diffusion, and segregation phenomena under extreme environments. He has also made impactful strides in the development of neural network potentials for materials like SiC, allowing large-scale simulations with quantum-level accuracy. His work directly supports the advancement of materials for nuclear reactors, space missions, and extreme-condition engineering.

šŸŒ Impact and Influence

Dr. Meng’s work has influenced fields such as nuclear materials, condensed matter theory, and computational materials science. His articles in high-impact journals like Physical Chemistry Chemical Physics, Journal of Nuclear Materials, and RSC Advances have become key references in radiation material modeling. His collaborations across diverse domains, from hydrogen embrittlement to deep potential learning for FCC copper, highlight his versatility. The adoption of his findings in defect prediction and grain boundary design has practical implications for materials used in reactors and space technology, positioning him as a rising figure in next-generation material research.

šŸ“š Academic CitesĀ 

With a growing body of 14+ peer-reviewed publications, Dr. Mengā€™s research outputs have earned significant citations in domains like irradiation defect dynamics, machine-learned interatomic potentials, and grain boundary engineering. His work on Tiā‚ƒAlCā‚‚ and Beā‚ā‚‚Ti systems has been cited for its pioneering insights into defect clusters and transmutation effects, while his 2023 papers on SiC doping and neural network-based modeling have gained traction among materials engineers and computational physicists. His interdisciplinary footprint, combining physics, chemistry, and mechanical engineering, enhances his recognition across both academic and applied research networks.

šŸ› ļø Research SkillsĀ 

Dr. Meng demonstrates mastery in first-principles methods (DFT), molecular dynamics, machine learning potentials, and multi-scale simulation frameworks. His computational toolkit includes VASP, Quantum ESPRESSO, LAMMPS, and deep learning platforms like DeePMD-kit. He excels in automated high-throughput screening, grain boundary structure prediction, and radiation damage modeling. His ability to link atomic-level processes to macroscopic properties allows him to tackle engineering problems with atomic precision. He is adept at designing simulation protocols that align with experimental validations, ensuring a feedback loop between theory and practice a critical skill in todayā€™s data-driven research environment.

šŸ‘Øā€šŸ« Teaching ExperienceĀ 

While primarily a researcher, Dr. Meng has informally mentored junior scientists and graduate students during his tenure at the Institute of Modern Physics. He has contributed to internal training modules and simulation workshops focusing on first-principles methods and materials modeling software. As his academic journey matures, he is well-positioned to engage in formal teaching or curriculum development, especially in computational material science, AI-driven simulations, and solid-state physics. His clarity in technical writing and collaborative style suggest strong potential as a future university lecturer or postgraduate supervisor.

šŸ… Awards and HonorsĀ 

Although specific awards are not mentioned, Dr. Mengā€™s selection for national strategic research programs (e.g., CAS Grant No. XDA0410000) and provincial funding initiatives like Guangdong Basic Research Foundation reflect institutional recognition of his capabilities. His consistent publication record in top-tier international journals underscores his scientific credibility. Being chosen to lead studies involving Grain Boundary Engineering and deep learning potentials in cutting-edge materials confirms his reputation among peers and senior collaborators. With this trajectory, formal honors such as Young Scientist Awards or Outstanding Researcher Fellowships are highly likely in the near future.

šŸ”® Legacy and Future ContributionsĀ 

Dr. Zhaocang Meng is poised to leave a lasting legacy in predictive materials design. His work in irradiation resistance, grain boundary tailoring, and AI-driven material exploration sets a solid foundation for next-gen energy systems, including fusion reactors, radioisotope thermoelectric generators, and space propulsion materials. Future contributions may include cross-disciplinary collaboration with AI scientists, sustainable materials discovery, and experimental validation partnerships. His potential to transition from a leading researcher to a thought leader and educator is evident. Dr. Meng represents a new era of materials scientists who bridge theory, computation, and practical innovation.

Top Noted Publications

Segregation and aggregation behavior of impurity atoms at grain boundaries of BeO: A first-principles study

  • Authors: Xuejie Wang, Teng Shen, Canglong Wang, Kai He, Zhaocang Meng*, et al.
    Journal: Journal of Nuclear Materials
    Year: 2025

Screening and manipulation by segregation of dopants in grain boundary of Silicon carbide: First-principles calculations

  • Authors: Z.C. Meng, C.L. Wang, Y.L. Wang, et al.
    Journal: Ceramics International
    Year: 2023

First-principles investigations of oxygen interaction with hydrogen/helium/vacancy irradiation defects in Tiā‚ƒAlCā‚‚

  • Authors: Zhaocang Meng, Canglong Wang, Jitao Liu, Yinlong Wang, Xiaolu Zhu, Lei Yang, Liang Huang
    Journal: Physical Chemistry Chemical Physics
    Year: 2021

New insight into the interaction between divacancy and H/He impurity in Tiā‚ƒAlCā‚‚ by first-principles studies

  • Authors: Zhaocang Meng, Canglong Wang, Jitao Liu, Yinlong Wang, Xiaolu Zhu, Lei Yang, Liang Huang
    Journal: Physical Chemistry Chemical Physics
    Year: 2020

Deep potential for a face-centered cubic Cu system at finite temperatures

  • Authors: Y.Z. Du, Z.C. Meng, Q. Yan, et al.
    Journal: Physical Chemistry Chemical Physics
    Year: 2022