学習可能な構造記述器支援ニューラルネットワークと転送学習による酸化物の帯域調整

Shin Kiyohara^1,2, Yoyo Hinuma³, Fumiyasu Oba^1,4

¹Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, R3-7, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan.

Journal of the American Chemical Society

|March 28, 2024

まとめ

機械学習は,塊と表面データを用いて,オキシドの半導体帯の配列を正確に予測します. このアプローチは,電子機器の材料の理解とスクリーニングを加速します.

科学分野:

材料科学
コンピュータ化学
凝縮物質物理学

背景:

半導体,断熱器,および介電物の帯域配列は,デバイスの性能にとって極めて重要です.
イオン化ポテンシャルと電子親和性は,表面依存の帯域エッジ位置を決定する.
正確な決定には複雑な実験やシミュレーションが必要です.

研究の目的:

非金属酸化物における帯の配列を予測するための機械学習モデルを開発する.
様々な酸化物表面のバンド位置を迅速かつ体系的に予測できるようにする.

主な方法:

約3000の酸化物表面の高通量第一原理計算データセットを使用しました.
大量構造と表面端末情報で訓練されたニューラルネットワークモデルを開発しました.
モデルを拡張し,複数のカチオン効果を組み込み,三元性酸化物に適用しました.

主要な成果:

ニューラルネットワークはリラックスされた二次酸化物表面のバンド位置を正確に予測します
このモデルは,多重カチオン効果と三元性酸化物への移行を効果的に処理します.
積層構造と表面端末データのみを使用して正確な予測を達成しました.

結論:

機械学習は非金属酸化物における帯の配列を決定する効率的な方法を提供します.
このアプローチは,電子アプリケーションのための体系的な理解と材料のスクリーニングを容易にする.
幅広い固体表面の帯の配列を予測することができます.

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