Interview with Mr. Grigor A. Mantashian—Winner of the Computation Best Paper Award
We recently had the opportunity to interview Mr. Grigor A. Mantashian, on behalf of his team, about his paper “Modeling of Quantum Dots with the Finite Element Method”, which was published in Computation (ISSN: 2079-3197) and has been highly praised by our evaluation committee members and readers.
The following is a short interview with Mr. Grigor A. Mantashian:
1. Could you briefly introduce yourself and tell us about your current research focus?
My name is Grigor Mantashyan. I am a researcher at the Quantum Materials and Nanophotonics Laboratory of the Institute of Chemical Physics, National Academy of Sciences of Armenia. Up to 2024, my research was dedicated to theoretical and computational modeling of semiconductor nanostructures—especially quantum dots and nanorods—and their integration into advanced optoelectronic devices. Around a year ago, I also embarked on an experimental research path, preparing my first experimental article and actively working in the laboratory. In parallel, I am currently undergoing a professional retraining at the Hong Kong University of Science and Technology, where I am gaining hands-on experience in liquid crystal nanostructure-based systems and advanced device preparation. This multifaceted approach is enabling me to bridge theoretical modeling with experimental realization, with the ultimate goal of becoming a well-rounded scientist capable of exploring complex physical phenomena from multiple perspectives.
2. Could you briefly describe the core contributions and findings of your award-winning paper?
The central contribution of our paper is the establishment of a finite element method (FEM) framework for modeling quantum dots (QDs) with arbitrary geometries and material compositions. Recognizing the growing diversity in experimentally fabricated QD structures, we demonstrated that an FEM enables the precise calculation of quantum energy levels and wavefunctions across a wide array of geometries—including conical QDs, quantum rings, nanotadpoles, and nanostars. We compared our results to exact analytical solutions and found strong agreement. The framework was also used to model spherical cadmium selenide/cadmium sulfide (CdSe/CdS) quantum dots. This model included advanced features like potential depth diffusion, which was simulated using piecewise Woods–Saxon potentials, and also accounted for position-dependent effective mass and dielectric permittivity. A notable finding was the emergence of a quasi-type-II band alignment around a core size of 2.2 nm, which aligns with experimental observations.
The modeling platform developed in this work has since served as a computational foundation for our research group’s subsequent projects, providing valuable insights into the effects of quantum confinement, geometry, and material inhomogeneity on nanostructure properties. As emphasized in the article, this FEM framework is readily extendable for the investigation of a wide range of electronic, photonic, and quantum optical phenomena, making it a versatile tool for both theoretical and experimental studies in nanoscience.
3. What were the main challenges in this research area, and what key innovations helped overcome them?
A major challenge in our research is the inherent complexity of the physics involved: quantum effects influence not only quantum-regime devices but also classical nanostructure-based devices such as LEDs and detectors. To make our work experimentally relevant, we must model both quantum phenomena—like quantum confinement and interface effects—and classical device properties, such as current transport and light extraction. Achieving this level of physical fidelity is especially demanding given the real-world complexity of nanostructure geometries and interfaces. We addressed these issues by developing a robust finite element method (FEM) framework that seamlessly integrates quantum simulations with classical modeling, enabling high computational accuracy across diverse device architectures.
An additional challenge has been our transition from a traditionally theoretical group into experimental research. This shift has required significant effort and adaptation. Fortunately, support from the Higher Education and Science Committee of Armenia has enabled us to acquire new laboratory equipment and to send early career researchers—including myself—to leading scientific institutions for advanced training. For example, while I am in the Hong Kong University of Science and Technology, my groupmates are in Belgium, Greece, and the Czech Republic. This combination of theoretical innovation and institutional support has been crucial in bridging our computational models with real-world device fabrication and experimental investigation.
4. Have there been tangible outcomes or follow-up projects inspired by your work?
Yes, the computational tools developed in our original work have laid the groundwork for both theoretical and experimental follow-up projects. Most recently, my research has expanded into the experimental preparation of nanostructure-based components, using modeling insights to optimize material and device properties.
5. Are there any methodological or practical lessons from your work you’d highlight for early career authors?
My advice to early career authors—especially those pursuing a PhD or master’s degree—is to be as proactive as possible. Complete all assigned tasks from your supervisor quickly and efficiently, and dedicate time to thorough literature review in your spare moments. If you choose to make research your career, it’s important to engage deeply in research activities and publish your findings as much as possible.
On the technical side, I strongly recommend integrating theoretical modeling with experimental validation and device preparation. Additionally, meticulous documentation and validation of your computational methods, as well as openness to constructive feedback from both peers and experimental data, are crucial for meaningful scientific growth and long-term impact.
6. Looking ahead, what are your future research plans or next steps in this area?
My immediate plans involve building on the foundation from my experimental and theoretical experience and expanding my research into complex optoelectronic devices that harness the unique properties of these hybrid materials. Ultimately, I seek to develop multiscale simulation and experimental frameworks that closely couple theoretical predictions with device-level performance, thereby enabling rational design and true innovation in nanostructured optoelectronics. In parallel, I am also eager to expand my research activities in both experimental and theoretical quantum optics, further broadening the scope and impact of my scientific work.
7. Is there anyone, such as collaborators, mentors, or institutions, you would like to thank for supporting your work?
I am deeply grateful to my supervisor, Professor Davit Hayrapetyan, for his unwavering mentorship and encouragement. A special thanks also goes to my co-author and the head of my research group, Dr. Paytsar Mantashyan, who played an integral role in helping me choose and pursue a career as a researcher and provided professional guidance, especially during my transition into experimental research. I would also like to thank my colleagues at the Institute of Chemical Physics. Special appreciation is due to the Ministry of Education, Science, Culture and Sports of Armenia for their sustained research funding. These communities have been essential to my progression as a scientist, both in theory and in experiment.
8. How was your experience with the editorial and peer-review process for Computation?
My experience with the editorial and peer-review process at Computation was highly positive. The reviewers offered constructive, detailed feedback that significantly improved the clarity and rigor of our manuscript. The editorial team was efficient and communicative, supporting a transparent and constructive review process. Additionally, I cannot emphasize enough the speed of the review process and publication.
9. What is your opinion of the open access model of publishing?
I strongly support open access publishing. It enables scientific knowledge—whether it is theoretical models, experimental results, or device innovations—to be shared freely and rapidly across the global scientific community. By ensuring that high-quality research is universally accessible, open access helps to level the playing field and promotes innovation worldwide.