Research themes

My primary research interests currently focus on methodological developments in ab initio Quantum Monte Carlo (QMC) methods and implementing these developed theories into versatile computational software. The quantum mechanical behavior of electrons, which governs the chemical and physical properties of materials, can, in principle, be predicted by solving the Schrödinger equation. However, except for special cases, analytical solutions are unattainable. Therefore, numerical solutions obtained via ab initio calculations using high-performance computing resources are typically employed.

At present, wavefunction theory and density functional theory (DFT) represent the most widely used first-principles computational approaches, achieving notable successes across quantum chemistry, materials science, and condensed matter physics. Nevertheless, wavefunction-based methods suffer from difficulties in handling periodic boundary conditions and poor computational scaling with electron numbers. Meanwhile, DFT faces significant issues arising from the arbitrary choice of exchange-correlation functionals. Thus, there is a strong demand for developing more rigorous, next-generation electronic structure calculation methods.

Ab initio Quantum Monte Carlo (QMC) methods numerically solve the many-body Schrödinger equation using Monte Carlo integration techniques. Among current electronic structure methods, QMC provides solutions that are closest to the exact solution. Unlike DFT, QMC eliminates the arbitrariness associated with selecting exchange-correlation functionals and readily facilitates parallel computing, maximizing the potential of modern high-performance computing resources. Consequently, QMC possesses disruptive innovation potential and is capable of replacing existing electronic structure calculation methods.

Historically, the computational cost of QMC methods has limited their practical applicability to realistic material systems of interest. However, the recent availability of exascale-class supercomputers, such as the Fugaku supercomputer at Japan’s RIKEN institute, has made it feasible to conduct practical QMC calculations within realistic computation times. Nevertheless, due to methodological limitations and lacking well-established general-purpose software, QMC has not yet supplanted conventional wavefunction and DFT methods. Many research groups are actively working on novel methodologies and corresponding software implementations to address these issues.

Currently, I am developing two computational packages central to my research activities:

  • TurboRVB, which implements the core computational kernels for ab initio QMC calculations. Originally initiatedabout two decades ago by Professor Sandro Sorella (International School for Advanced Studies, Italy) and researcher Michele Casula (CNRS, Sorbonne University, France), TurboRVB has benefited from the contributions of numerous researchers. I joined its development team in 2017 and currently serve as its primary developer and maintainer following Professor Sorella’s passing. TurboRVB, written in Fortran90, is undergoing refactoring to adopt modern coding practices (new contributors are welcome!).

  • TurboGenius, an open-source Python package designed to facilitate high-throughput ab initio QMC calculations using TurboRVB as the computational kernel. TurboGenius automates complex computational workflows required by QMC calculations, from generating trial wavefunctions to managing comprehensive calculation processes via Python scripting. Additionally, TurboGenius expands TurboRVB’s capabilities. For instance, whereas TurboRVB originally used a built-in DFT module (which lacked features like DFT+U), TurboGenius allows trial many-body wavefunctions to be generated from external localized-basis DFT codes (e.g., PySCF, CRYSTAL23, Gaussian).

Current and future research directions include: - Methodological developments in predicting material properties with QMC, particularly force calculations and their implementation into TurboRVB.

  • Establishing a database of localized (Gaussian) basis sets optimized for periodic boundary conditions in QMC calculations.

  • Developing Gaussian basis sets optimized for parameterizing Jastrow functions.

  • Large-scale refactoring of TurboRVB and TurboGenius codes.

  • Applying materials informatics approaches through screening calculations using TurboWorkflows.

  • Constructing and applying (machine-learned) molecular dynamics force fields based on QMC-calculated data.

  • Developing a new Python-based ab initio QMC computational code.

Although I currently do not formally run a research laboratory, I independently conduct research within my affiliated national research institution. While my institution cannot directly confer degrees, master’s and doctoral students can be accepted through joint graduate programs with partner universities such as the University of Tsukuba, Hokkaido University, Waseda University, Kyushu University, Osaka University, and Yokohama National University. Interested students are strongly encouraged to contact me.

My research collaborators are affiliated with various European institutions (e.g., UCL, UK; CNRS, France; SISSA, Italy; UZH, Switzerland), and we regularly engage in research discussions and coding sessions, both online and face-to-face. Therefore, strong English communication skills are essential. Initial fluency is not required, but committed efforts toward proficiency are necessary. Opportunities for short-term international research stays are frequent. Thus, familiarity with international travels is also desirable.