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.
Profile
Scopus
š 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. (2018ā2021) 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.
š¼ 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.
š¬ Contributions and Research FocusĀ
Dr. Mengās 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 TiāAlCā, BeO, SiC, and BeāāTi have revealed novel insights into defectāimpurity 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.
š 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.
š Academic CitesĀ
With a growing body of 14+ peer-reviewed publications, Dr. Mengās research outputs have earned significant citations in domains like irradiation defect dynamics, machine-learned interatomic potentials, and grain boundary engineering. His work on TiāAlCā and BeāāTi 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.
š 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 todayās data-driven research environment.
šØāš« 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.
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Awards and HonorsĀ
Although specific awards are not mentioned, Dr. Mengās 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.
š® 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
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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
First-principles investigations of oxygen interaction with hydrogen/helium/vacancy irradiation defects in TiāAlCā
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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 TiāAlCā by first-principles studies
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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