# Research Highlights

## Computational Exploration of Quantum Many-body Phenomena

We are exploring novel methods in computational physics based on stochastic method such as the Monte Carlo simulation, path-integral representation of quantum fluctuations, information compression by using the singular value decomposition and the tensor network, statistical machine learning, etc. By making full use of these powerful numerical methods, we aim to elucidate various exotic phases, phase transitions, and dynamics specific to quantum many-body systems, from strongly correlated systems such as the spin systems and the Bose-Hubbard model to real materials. We are also researching parallelization methods for leading-edge supercomputers, and developing and releasing open-source software for next-generation physics simulations.

- Development of simulation algorithms for strongly-correlated systems
- Application of machine learning technique to materials science
- Fundamental theory of quantum computer
- Novel state and critical phenomena in strongly correlated systems
- Cooperative phenomena in non-equilibrium and non-steady states
- Development of open-source software for next-generation parallel simulations

## 2018

## Crystal structure prediction by combined optimization of experimental data and first-principles calculation

Crystal structure prediction has been known as one of the most difficult problems, and various prediction methods have been developed so far. Recently, joint optimization of experimental data and the theoretical potential energy calculation has been proposed. In that method, a combined cost function, a sum of reproducibility of experimental data and the potential energy, are optimized. However, combined cost function loses the information of the local minima of each cost functions. We developed a new optimization algorithm, Combined Optimization Method (COM), to overcome this difficulty. For example, it is known that the determination of crystal structure for SiO2 systems is quite difficult due to the existence of a lot of local minimum arrangements. By using the COM, we confirmed that the success rate of crystal structure prediction increases significantly.

- Naoto Tsujimoto, Daiki Adachi, Ryosuke Akashi, Synge Todo, Shinji Tsuneyuki,
*Crystal structure prediction supported by incomplete experimental data,*Phys. Rev. Materials**2**, 053801 (2018). (preprint: arXiv:1705.08613)

## Non-ergodicity in Classical Harmonic Oscillator System

Unlike the normal Langevin equation, the generalized Langevin equation, which deals with the memory effects, shows various type diffusions depending on the memory function. Recently, the anomalous diffusion and the non-ergodicity have been actively studied in the terms of the generalized Langevin equation. There are, however, some confusions in the definition of the ergodicity and there are few analysis using physical models. We propose a new non-ergodic model, which consists of harmonic oscillators, and analyze the model by the molecular dynamics, the exact diagonalization, and the analytical solution. We also reconsider the definition of the ergodicity, and clarify that the non-ergodicity observed in our model is caused by the localized mode.

- Fumihiro Ishikawa, Synge Todo,
*Localized Mode and Nonergodicity of a Harmonic Oscillator Chain,*preprint: arXiv:1805.02923.

## Machine learning for molecular dynamics with strongly correlated electrons

Machine learning (ML) is emerging as a promising tool to help model various types of many-body phenomena. A promising research area is the molecular dynamics (MD) of strongly correlated electron materials. While quantum MD methods based on the density functional theory have been successfully applied to a wide variety of materials, they have limited validity in their treatment of electron correlations. On the other hand, most of the many-body techniques, such as the dynamical mean-field theory, are computationally too costly for MD simulations. We showed that ML can be effective for building fast, linear-scaling MD potentials that capture correlated electron physics. Specifically, we used ML to enable large-scale Gutzwiller MD simulations of a liquid Hubbard model and studied the Mott metal-insulator transition. For the systems considered in the present study, ML is up to 6 orders of magnitude faster than direct quantum calculations. Our work opens a path toward a large-scale dynamical simulation of realistic models of correlated materials.

- Hidemaro Suwa, Justin S. Smith, Nicholas Lubbers, Cristian D. Batista, Gia-Wei Chern, and Kipton Barros
*Machine learning for molecular dynamics with strongly correlated electrons*

Phys. Rev. B**99**, 161107(R) (2019). (preprint: arXiv:1811.01914)

## Machine learning approach to explore higher Young modulus materials with first principle calculation

Recently, data-driven approaches to design materials, so called ``materials informatics'', are attracting more attentions. Particularly, numerical approaches combining the first principle calculation and machine learning methods are intensively advanced. We apply the method combining the Bayesian optimization method and the first principle calculation for exploring the most rigid materials in certain binary hexagonal compounds. As the result, the combining method succeed in finding the best materials in the search space and the dataset including various type data makes the exploring process efficient.