Creation of an organic thin film deposition system by integration of information science

• Project/Area Number: 18K14126
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 29020:Thin film/surface and interfacial physical properties-related
• Research Institution: Kyoto University
• Principal Investigator: Packwood Daniel 京都大学, 高等研究院, 講師 (40640884)
• Project Period (FY): 2018-04-01 – 2020-03-31
• Project Status: Completed (Fiscal Year 2019)
• Budget Amount: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000); Fiscal Year 2019: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000); Fiscal Year 2018: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
• Keywords: 有機薄膜 / 低速電子線回折 / 走査トンネル顕微鏡 / 低速電子線回折シミュレーション / ベイズ最適化 / 教師なし機械学習 / 薄膜構造の解明 / 低速電子線解析 / 電子線回析シミュレーション / 結晶性 / 機械学習 / 最適化 / 構造解明 / 第一原理計算 / 実験・理論・情報の融合 / 薄膜構造解明

Outline of Final Research Achievements

Organic thin films on metallic substrates are widely used as charge transport layers in OLEDs (organic light emitting diodes) and other organic electronics.

In this project, we aimed to create a machine learning algorithm which can find the optimal deposition conditions for creating highly crystalline, small-molecule organic thin films. We succeeded to collect training data for this algorithm, and confirmed that it spans a wide range of thin film states (sub-monolayer to multilayer) using scanning tunneling microscopy. However, more training data is needed to run the optimization algorithm properly. In addition, we created a new computational method which can, in principle, determine the atomic structure of an organic thin film from low energy electron diffraction (LEED) data.

Academic Significance and Societal Importance of the Research Achievements

・By minimizing trial-and-error, the algorithm will reduce the time required to deposit high-quality small molecule thin films, and might accelerate the development of organic electronics based upon small-molecule films.

・Organic thin film structure might be elucidated with our computational method.

Research Products

• [Journal Article] Exploring the configuration spaces of surface materials using time-dependent diffraction patterns and unsupervised learning (2020)
  • Author(s): Daniel M. Packwood
  • Journal Title: Scientific Reports Volume: 10 Issue: 1 Pages: 5868-5879
  • DOI: 10.1038/s41598-020-62782-6
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Presentation] Structure prediction and control for functional surface materials (2020)
  • Author(s): Daniel M. Packwood
  • Organizer: Applied Math for Energy: Future Directions (workshop at I2CNER, Kyushu University)
  • Invited
• [Presentation] 表面上の分子集合体のための機械学習 (2020)
  • Author(s): Daniel M. Packwood
  • Organizer: 近畿化学協会コンピューター化学部会 第107回例会
  • Invited
• [Presentation] Informatics for self-assembled materials (2020)
  • Author(s): Daniel M. Packwood
  • Organizer: First Max Planck-VISTEC Symposium on Materials Science
  • Int'l Joint Research / Invited
• [Presentation] Machine learning for surface-assisted self-assembly (2020)
  • Author(s): Daniel M. Packwood
  • Organizer: NANOMAT2019 (CNRS, France)
  • Int'l Joint Research / Invited
• [Presentation] Low-energy electron diffraction from organic monolayers (2019)
  • Author(s): Daniel Packwood
  • Organizer: SPIRITS International Symposium 2019 - Regulation of cell fate and disease treatment
  • Invited
• [Presentation] Machine learning for nanomaterials assembly on surfaces (2018)
  • Author(s): Daniel Packwood
  • Organizer: Interfacing Machine Learning and Experimental Methods for Surface Structures
  • Int'l Joint Research / Invited
• [Remarks] Scientific Reports website
  • URL: https://www.nature.com/articles/s41598-020-62782-6
• [Remarks] Research group website
  • URL: http://www.packwood.icems.kyoto-u.ac.jp/