Optimization of Core–Shell Nanoparticles Using a Combination of Machine Learning and Ising Machine

Author:

Urushihara Makoto1,Karube Masaya1,Yamaguchi Kenji1,Tamura Ryo23ORCID

Affiliation:

1. Innovation Center Mitsubishi Materials Corporation 1002-14 Mukohyama Naka Ibaraki 311-0102 Japan

2. Center for Basic Research on Materials National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan

3. Graduate School of Frontier Sciences The University of Tokyo 5-1-5 Kashiwa-no-ha Kashiwa Chiba 277-8561 Japan

Abstract

Machine‐learning‐based optimization techniques are widely used for designing complex materials. However, an efficient search for the complex systems, where a combinatorial explosion occurs in the materials search space, is still challenging. Core–shell nanoparticles (CSNPs) are an example of a complex system with a high degree of freedom owing to their complicated structure and multiple constituent materials. In this study, a new black box optimization technique is developed. In this method, the structure of the CSNPs is optimized using an Ising machine and their constituent materials are selected using Bayesian optimization with the optical properties of the materials as the “descriptors”. Aiming for applications to i‐line photolithography, the authors search for CSNPs that are transparent to ultraviolet light of wavelength 355–375 nm and opaque to visible light of wavelength 400–830 nm. The transmittance spectra of the nanoparticles are obtained using a Mie theory calculator. The proposed nanoparticles with the best optical properties have a multilayered structure with a radius of approximately 40 nm and an outer shell composed of either Mg or Pb. The results indicate that a combination of various optimization techniques is more efficient in discovering the better complex materials.

Publisher

Wiley

Subject

Pharmacology (medical),Complementary and alternative medicine,Pharmaceutical Science

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