チオレート,アグ合金AuナノクラスターにおけるCO吸収の機械学習予測
Gihan Panapitiya1, Guillermo Avendaño-Franco1, Pengju Ren2,3
1Department of Physics and Astronomy , West Virginia University , Morgantown , West Virginia 26506-6315 , United States.
Journal of the American Chemical Society
|November 9, 2018
まとめ
ゴールドナノクラスターの 炭素一酸化物 (CO) 吸収を予測する 機械学習モデルを開発しました このモデルは,吸着エネルギーに影響を与える銀原子の分布のような重要な構造的特徴を特定します.
科学分野:
- コンピュータ化学
- 材料科学
- 機械学習
背景:
- 硫酸塩で保護された金のナノクラスターは 触媒に不可欠です
- 炭素一酸化物 (CO) の吸収を予測することは,ナノクラスターの反応性を理解するために不可欠です.
- 複雑な構造と性質の関係を捉えるのに苦労しています.
研究 の 目的:
- 金ナノクラスターのCO吸収を予測するための機械学習モデルを開発する.
- CO吸収エネルギーに影響を与える主要な構造的記述者を特定する.
- 様々な金ナノクラスター組成にモデルの適用性を拡張する.
主な方法:
- ランダムフォレストの機械学習モデルが採用されました.
- モデルには2段階のアプローチが採用されました 特徴の選択と訓練です
- 初期開発と検証はAu25ナノクラスターで行われました.
主要な成果:
- このモデルはCO吸収エネルギーを 予測することに成功した.
- 特徴の重要性の分析は,吸収部位に対する銀原子の分布がAu25にとって重要であることを明らかにした.
- このモデルは,Ag合金Au36およびAu133ナノクラスタの予測能力を実証した.
結論:
- 機械学習はナノクラスターのCO吸収を予測するための効果的なアプローチを提供します.
- 構造-吸収関係を理解することは,ML主導の特徴分析によって強化できます.
- 開発されたモデルは,さまざまな金基ナノクラスターにおけるCO吸収を調査するための多用途のツールを提供します.
さらに関連する動画
関連する概念動画
Analyte Adsorption and Distribution
2.8K
In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
2.8K
Machines
579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
A free-body diagram of the...
579
Predicting Molecular Geometry
45.8K
VSEPR Theory for Determination of Electron Pair Geometries
45.8K
Machines: Problem Solving II
668
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
668
Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
Machines: Problem Solving I
714
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
714


