Introduction:

The research groups at the Sungkyunkwan University (Prof. Chang Woo Myung) & Ulsan National Institute of Science & Technology, UNIST (Profs. Geunsik Lee & Kwang S. Kim) are announcing exceptional research opportunities in the areas of machine learning (ML) potential development (Sparse Gaussian Process Regression potential) and quantum computing algorithm development.

Since conventional quantum mechanical simulations are too slow to be applied to large systems, our groups are focusing on developing machine learning (Bayesian machine learning potential, machine learning Bayesian-based sampling) methods that perform ab initio simulations fast and accurately to overcome these problems (Adv. Energy Mater. 12, 2202279 (2022); Adv. Energy Mater. 12, 2201497 (2022); Phys. Rev. Lett. 128, 045301 (2022); J. Phys. Chem. A 125, 9414 (2021); J. Phys. Chem. Lett. 12, 8115 (2021); Phys. Rev. B 103, 214102 (2021)). The Sparse Gaussian Process Regression (SGPR) potential is a scalable and accurate machine learning potential, and we aim to develop universal ab initio level SGPR potential for various materials (hydrocarbons, proteins, water, aqueous solutions, Li-battery materials etc.).

Quantum computing has recently gained attention as a promising way to overcome the curse of dimensionality in first-principles calculations. We aim to develop a classical-quantum hybrid algorithms for existing ab initio methods. Specifically, we combine the matrix product state methods (AIP Advances 6, 095024 (2016)) with quantum computing algorithms to explore the quantum advantage of classical-quantum hybrid algorithms. We develop a diffusion quantum Monte Carlo (J. Phys. Chem. A 123, 7785 (2019)) and Dynamical Mean Field Theory (DMFT) (Phys. Rev. Lett. 109, 177001 (2012)) methods. We also develop dimension-reduced matrix product state (MPS) and density matrix renormalization group (DMRG) algorithms to elucidate quantum dimensional transitions in the square lattice spin-1/2 antiferromagnetic Heisenberg model (Phys. Rev. B 99, 134441 (2019)).

Research areas:

SGPR machine learning potential: Join us in the development of SGPR machine learning potential. Our research aims include the development of electronic-structure-dependent SGPR ML potential and the development of universal SGPR potential for a wide range of materials (macro/bio-molecular systems and novel functional materials including electrocatalysts, perovskite solar cells, Li-battery materials, etc.).

Classical-quantum hybrid algorithms: The goal is to uncover the quantum advantages and limitations of variational quantum algorithms (VQAs), and to identify the types of applications and problems for which VQAs can provide quantum advantages. We aim to apply VQAs to quantum chemical systems, such as gas phase molecules, and condensed matter physics models/methods, such as the Hubbard model, MPS and Dynamical Mean Field Theory (DMFT), and demonstrate their quantum advantages over classical methods in solving these systems.

Collaboration and Support:

The selected candidates will have the opportunity to work closely with 3 joint group members, who will provide support throughout research. As for Quantum Computing, we are going to collaborate with research groups in Harvard Univ.

Eligibility:

We are actively seeking applications from candidates possessing a strong academic background in fields such as physics, chemistry, materials science, computer science, or related disciplines. Ideal candidates will demonstrate a vigorous interest in engaging in pioneering research at the nexus of machine learning and quantum computing.

Ph.D. positions:

Candidates should exhibit a strong background in theoretical physics and chemistry through the utilization of computational techniques.

Postdoctoral, Research Scientist, and Research Professor positions:

Candidates should have high-quality publications in the realm of theoretical/computational physics, chemistry, and materials science, complemented by specialized knowledge in machine learning or quantum computing.

Application Process:

Interested candidates are invited to submit their applications including a (i) detailed CV, (ii) lists of publications, and (iii) a statement of research interests. Please send your application by email to Chang Woo Myung ([email protected]), Geunsik Lee ([email protected]), or Kwang S. Kim ([email protected]).

Become a part of the vibrant and dynamic groups at the Sungkyunkwan University and UNIST. For further information about the research groups, please visit the below links:

- https://www.myung.skku.edu/

- https://sites.google.com/view/gslee2017/

- https://scholar.google.com/citations?hl=en&user=dWoR7zwAAAAJ

We invite you to submit your applications, and we are eager to extend a warm welcome to outstanding researchers who will join our team!

Chang Woo Myung, Geunsik Lee, and Kwang S. Kim

Department of Energy Science, Sungkyunkwan University, Korea

Department of Chemistry, Ulsan National Institute of Science & Technology (UNIST), Korea