関連する実験動画

Updated: Feb 22, 2026

Demonstrating the Simplicity and In Situ Temperature Monitoring of the Mechanochemical Synthesis of Metal Chalcogenides Suitable for Thermoelectrics
04:09

Demonstrating the Simplicity and In Situ Temperature Monitoring of the Mechanochemical Synthesis of Metal Chalcogenides Suitable for Thermoelectrics

Published on: August 30, 2024

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加速された金属酸化物の熱化学予測のための機械学習

Nickolas A Joyner1, Sarah Sprouse1, Haizley Herndon1

  • 1Department of Chemistry and Biochemistry, The University of Alabama, Shelby Hall, Tuscaloosa, Alabama 35487-0336, United States.

Journal of chemical theory and computation
|February 20, 2026
PubMed
まとめ

機械学習モデルは,金属酸化物のエネルギーを正確に予測し,大量結合エネルギーの計算を迅速に可能にします. このフレームワークは,標準化されたクラスタリングエネルギー (NCE) を使用して,計算コストを削減してDFTレベルの精度を達成します.

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