[Speaker] Yuto Ashida (Tokyo Univ. )[ 蘆田 祐人氏 (東大物工)]

[Date ] Sep. 17th 16:00-

[Place] 22棟物理学科会議室

[Title] Learning the best nanoscale thermoelectric heat engines through evolving network topology

[Abstract]

The quest to identify the best heat engine has been at the center of science and technology. Considerable studies have so far revealed the potentials of nanoscale engines to yield an enhanced thermodynamic efficiency in noninteracting regimes. However, the full benefit of many-body interactions is yet to be investigated; identifying the optimal interaction is a hard problem due to combinatorial explosion of the search space, which makes brute-force searches infeasible. In this talk, I will present our attempt [1] to tackle this problem with a framework for reinforcement learning of network topology in interacting thermal systems. Applying our framework to thermoelectric devices as representative of nanoscale thermal machines, we show that the maximum possible values of the figure of merit and the power factor could be enhanced by orders of magnitudes for generic single-electron levels. The versatility of the developed reinforcement-learning framework could allow one to identify full potential of a broad range of nanoscale systems in terms of multiple objectives.

[1] YA and T. Sagawa, arXiv:1908.04866 (2019).