海歸學者發起的公益學術平臺
分享資訊，整合資源
交流學術，偶爾風月
來自中國南京資訊工程大學的張磊教授及其團隊成員，基於機器學習和材料計算，科學地評估了鹵化物鈣鈦礦的穩定性。在這項研究中，他們系統地研究了在不同表面上功能的多分子修飾的CH3NH3PbI3膜的水性光電化學穩定性，發現了一種有效的多分子鈣鈦礦材料體系“calcein + PbBr2 + DMSO +CH3NH3PbI3”，具有出色的液態水環境光電穩定性（相同水環境下光生電流是CH3NH3PbI3薄膜輸出的103倍)和92.5%的水穩定性。隨後，透過遺傳演算法和極端隨機樹的Shapley分析來檢測分子修飾鈣鈦礦材料的實驗水性光電化學性質，以提供機器解釋和解耦分子貢獻，強調這些相容分子的協同效應及其親水/親脂性對目標輸出的重要性。DFT計算表明，該多分子全域性最佳化體系存在大量氫鍵和陰離子··π表面相互作用以穩定其介面結構。除了預測高水穩定性的鈣鈦礦體系外，該研究團隊還優化了光電化學、機器學習和DFT模型總體資料驅動工作流程用於評估鹵化物鈣鈦礦穩定性。
該文近期發表於npj Computational Materials | (2024) 10:114，英文標題與摘要如下，點選左下角“閱讀原文”可以自由獲取論文PDF。
Figure 1 Post-hoc DFT
calculation (atomic and electronic structures)
calculation (atomic and electronic structures)
Figure 2 Fabrication details,
classification method and molecular variables.
classification method and molecular variables.
Figure 3 Feature analysis via
SHAP.
SHAP.
Figure 4 Overall workflow of
this study.
this study.
Data-driven optimization and machine learning analysis of compatible molecules for halide perovskite material
Shaojun Wang, Yiru Huang, Wenguang Hu & Lei Zhang
Optoelectronic
stability of halide perovskite material in hostile conditions such as water is
rather limited, preventing them from further industrial deployment. Here, we
optimize and perform machine learning analysis on CH3NH3PbI3materials with additives, solvents and post-treatment molecules using combined
experimental and data-driven methods. A champion system consisting of a
compatible tertiary molecular combination ‘calcein+PbBr2 + DMSO’
active at diverse surfaces is identified, delivering a large aqueous
photoelectrochemical (PEC) photocurrent of 10-5 A/cm2 and
an improved aqueous stability of 92.5%. Subsequently, machine interpretation is
provided to decouple the multi-molecule contributions with the assistance of
genetic programming (GP) and extra-trees (ET) machine learning models,
highlighting the intricate molecular features for the target outputs. The
posthoc density functional theory (DFT) calculation suggests the presence of
multiple hydrogen bond and anion··π surface interactions to stabilize the interfacial
structures. The present ‘PEC + GP + ET + DFT’ approach is suggested to be an
effective approach to design and comprehensively evaluate moleculemodified
materials.
stability of halide perovskite material in hostile conditions such as water is
rather limited, preventing them from further industrial deployment. Here, we
optimize and perform machine learning analysis on CH3NH3PbI3materials with additives, solvents and post-treatment molecules using combined
experimental and data-driven methods. A champion system consisting of a
compatible tertiary molecular combination ‘calcein+PbBr2 + DMSO’
active at diverse surfaces is identified, delivering a large aqueous
photoelectrochemical (PEC) photocurrent of 10-5 A/cm2 and
an improved aqueous stability of 92.5%. Subsequently, machine interpretation is
provided to decouple the multi-molecule contributions with the assistance of
genetic programming (GP) and extra-trees (ET) machine learning models,
highlighting the intricate molecular features for the target outputs. The
posthoc density functional theory (DFT) calculation suggests the presence of
multiple hydrogen bond and anion··π surface interactions to stabilize the interfacial
structures. The present ‘PEC + GP + ET + DFT’ approach is suggested to be an
effective approach to design and comprehensively evaluate moleculemodified
materials.
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