欧州の研究者をお招きして「マテリアル探索自動化・自律化人材育成セミナー」を開催しました

日時: 2023年12月19日 (火) 14:00-16:00

オンライン参加ご希望の方はその旨をGoogle formにご記入ください。
後日、ZOOMのURLを直接お知らせします。

(変更しました。)

解説:21世紀、材料科学の進化はデータ駆動の時代を迎えています。このセミナーシリーズで今回は欧州のClaudia Draxl教授とMilica Todorović准教授をお呼びして,日々蓄積される膨大なデータを知識と価値に変えるための戦略やテクニックを紹介して頂きました。FAIRデータ基盤の重要性や異質なデータを活用した材料科学AI技術が紹介されました。大学院生から大学教授、産業界の研究者まで、材料科学でのデータからの知識獲得やAIに興味を持つすべての方を対象として開催されました。(NIMS木野日織)

Claudia先生からはFAIRの実情をお話しいただき、欧州でのデータシェアリングの進行を学びました。MEEPのPOC2:仮説駆動/データ駆動ハイブリッド研究手法、POC3:ナレッジシェアリングの参考になりました。
参考文献
Jacobsson, T.J., Hultqvist, A., García-Fernández, A. et al. An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles. Nat Energy 7, 107–115 (2022).

Milica先生からはベイズ最適化を用いた分子構造の探索等、MEEPの自律探索と強く関係する話題をいただき、MEEPのPOC1:自律実験・自律計測・計算科学を用いたマテリアル探索空間拡張の参考になりました。

ACS sustainable Chem Eng. 10, 9469 (2022)

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開催要項

日時: 2023年12月19日 (火) 14:00-16:00

オンライン参加ご希望の方はその旨をGoogle formにご記入ください。
後日、ZOOMのURLを直接お知らせします。

(Google form 申し込み終了)

Towards accelerated discovery of new materials

Claudia Draxl

Professor, Department and IRIS Adlershof, HU Berlin, Berlin, Germany
The enormous amounts of research data produced every day in the field of materials science represent a gold mine of the 21st century. How can we turn these data into knowledge and value? Here, a FAIR (Findable, Accessible, Interoperable, and Re-usable) data infrastructure plays a decisive role as this gold mine is of little value if the data are not comprehensively characterized and made available. Only then, data can be readily shared and explored by data analytics and artificial-intelligence (AI) methods. Making data Findable and AI Ready (another interpretation of the acronym) will change the way how science is done today.
In this talk, I will discuss how the NFDI consortium FAIRmat [1] is approaching these goals [2], making data from sample synthesis, various experimental probes, and computational materials science FAIR. A particular emphasis will be on the I, the interoperability. With selected examples, I will also show how knowledge can be gained from these data, be it with unsupervised [3] or supervised [4] machine-learning techniques.

References
[1]  https://fairmat-nfdi.eu
[2]  M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H.-J. Bungartz, C. Felser, M. Greiner, A. Groß, C. Koch, K. Kremer, W. E. Nagel, M. Scheidgen, C. Wöll, and C. Draxl, Nature 604, 635 (2022).
[3]  M. Kuban, S. Gabaj, W. Aggoune, C. Vona, S. Rigamonti, and C. Draxl, MRS Bulletin 47, 991 (2022).
[4]  T. Bechtel, D. Speckhard, J. Godwin, and C. Draxl, preprint.

Active learning for data-efficient optimisation of materials and processes

Milica Todorović

Assistant Professor, Materials Engineering, University of Turku, Finland
The arrival of materials science data infrastructures in the past decade has ushered in the era of data-driven materials science based on artificial intelligence (AI) algorithms, which has facilitated breakthroughs in materials optimisation and design. Of particular interest are active learning algorithms, where datasets are collected on-the-fly in the search for optimal solutions. We encoded such a probabilistic algorithm into the Bayesian Optimization Structure Search (BOSS) Python tool for materials optimisation [1].
BOSS builds N-dimensional surrogate models for materials’ energy or property landscapes to infer global optima, allowing us to conduct targeted materials engineering. The models are iteratively refined by sequentially sampling materials data with high information content. This creates compact and informative datasets. We utilised this approach for computational density functional theory studies of molecular surface adsorbates [2], thin film growth [3], solid-solid interfaces [4] and molecular conformers [5]. With experimental colleagues, we applied BOSS to accelerate the development of novel materials with targeted properties, and to optimise materials processing [7]. With recent multi-objective and multi-fidelity implementations for active learning, BOSS can make use of different information sources to help us discover optimal solutions faster in both academic and industrial settings.

[1] npj Comput. Mater., 5, 35 (2019)
[2] Beilstein J. Nanotechnol. 11, 1577-1589 (2020), Adv. Func. Mater., 31, 2010853 (2021)
[3] Adv. Sci. 7, 2000992 (2020)
[4] ACS Appl. Mater. Interfaces 14 (10), 12758-12765 (2022)
[5] J. Chem. Theory Comput. 17, 1955 (2020)
[6] MRS Bulletin 47, 29-37 (2022)
[7] ACS Sustainable Chem. Eng. 10, 9469 (2022)