不規則なシリコンにおける構造的および電子的移行の起源
Volker L Deringer1, Noam Bernstein2, Gábor Csányi3
1Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, UK. volker.deringer@chem.ox.ac.uk.
Nature
|January 7, 2021
まとめ
機械学習モデルは無形シリコンを明らかにする
科学分野:
- 材料科学
- 計算物理
- 固体化学
背景:
- 構造的に無秩序な材料は,相共存と変換に関する複雑な問題を提示します.
- これらの移行を理解することは重要ですが,現在の実験的,計算的技術によって制限されています.
研究 の 目的:
- 圧迫下での無形シリコンの構造的移行のメカニズム的理解を明らかにする.
- 高密度無形相の形成と多形変異を,高度な計算方法を用いて研究する.
主な方法:
- 量子力学的計算で訓練された 原子的な機械学習モデルです
- 大規模なシステム (100,000個の原子) のシミュレーションで,液体-無形および無形-無形の移行を研究する.
- メタリック性を予測するための状態の電子密度のための機械学習モデル.
主要な成果:
- 低密度と高密度の無形シリコン領域の共存が観測された.
- 非常に高密度無形 (VHDA) 段階への構造的崩壊を特定した.
- VHDAの一時的な性質を示し,多結晶構造に結晶化しました.
結論:
- この研究は,圧力下での無形シリコンの詳細な3段階の変換シーケンスを提供します.
- 機械学習により 大規模なシステムのシミュレーションが可能になり これまで観察されなかった 移行ダイナミクスを明らかにできます
- この研究は,予測的な材料モデリングのための機械学習駆動のアプローチを強調しています.
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