微分電子問題を解くことで密度関数の境界を押し広げること
James Kirkpatrick1, Brendan McMorrow1, David H P Turban1
1DeepMind, 6 Pancras Square, London N1C 4AG, UK.
Science (New York, N.Y.)
|December 9, 2021
まとめ
研究者は,密度関数理論における体系的なエラーを克服するために,ニューラルネットワークベースの新しい関数 DM21 を開発しました. この高度な量子化学のツールは 複雑な分子システムを正確にモデル化し 正確な普遍的な機能への道を提供します
科学分野:
- 量子化学について
- コンピュータ材料科学
- 科学における人工知能
背景:
- 密度関数理論 (DFT) は,物質の量子力学的性質を記述する上で極めて重要です.
- 既存のDFT近似は,正確な機能特性の違反による体系的なエラーを含んでいます.
- これらの誤差は,様々な化学および材料システムの DFT の精度を制限します.
研究 の 目的:
- 伝統的な密度関数理論の近似の基本的制限を克服する.
- 微分電荷とスピンを有するシステムを正確に記述する 新しい機能を開発する.
- 量子力学的計算のための正確な普遍的な機能への道を確立する.
主な方法:
- 分子データでニューラルネットワークを 訓練する
- 訓練中に小数の電荷とスピンを持つ架空のシステムを組み込む.
- ニューラルネットワークの出力に基づくDM21 (ディープマインド21) の機能開発.
主要な成果:
- DM21は,メイングループの原子と分子に対するベンチマークにおいて,従来の機能よりも優れたパフォーマンスを示しています.
- 新しい機能は,水素連鎖,充電されたDNA塩基対,およびダイラジカル移行状態を含む,挑戦的なシステムを正確にモデル化します.
- DM21は人工的な電荷移転と強い相関現象を正しく記述しています.
結論:
- DM21の機能は密度関数理論の重要な進歩を表しています.
- データを駆動した 制約に基づく方法論は 正確な普遍的な機能への 実行可能な経路を提供します
- このアプローチは,幅広い量子化学および材料科学の応用において,より高い精度を約束します.
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