基于关键性质的机器和深度学习模型,用于预测基于关键性质的异环基烯化合物的高压密度
Amir Hossein Sheikhshoaei1, Ali Khoshsima2,3
1School of Petroleum and Chemical Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Scientific reports
|July 14, 2025
概括
机器学习准确地预测使用关键性质的thiophene衍生物的高压密度. 轻GBM模型展示了卓越的性能,为材料科学应用提供了有价值的工具.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 烯衍生物在材料科学中对于有机半导体,制药和聚合物至关重要.
- 准确预测热物理性质,如高压密度,对于材料设计和性能至关重要.
- 了解这些特性对于涉及极端条件的应用是必不可少的.
研究的目的:
- 使用机器学习和深度学习模型预测七种硫衍生物的高压密度.
- 为了评估各种模型的性能,包括决策树,AdaBoost-DT,LightGBM,GBoost,TabNet和DNN.
- 根据关键性质确定最有效的模型,以准确地预测密度.
主要方法:
- 作为输入参数,利用了关键性质 (关键温度,压力,体积,离心系数) 和沸点/分子量.
- 采用了一套机器学习和深度学习算法:决策树 (DT),自适应提升决策树 (AdaBoost-DT),LightGBM,梯度提升 (GBoost),TabNet和深度神经网络 (DNN).
- 应用统计错误评估指标 (AAPRE,RMSE,R2) 和数据验证的杆方法.
主要成果:
- 轻GBM模型表现出卓越的性能,平均绝对百分比相对误差 (AAPRE) 为0.0231.
- 该模型还报告了0.3499的根平均平方误差 (RMSE) 和0.9999.99的确定系数 (R2).
- 杆方法证实了99.10%的数据的有效性,表明模型可靠性强.
结论:
- 关键性质是预测硫衍生物的高压密度的有效输入.
- 在这种类型的化合物中,LightGBM模型提供了一种可靠和准确的方法来预测高压密度.
- 这种预测能力是推进材料科学和在极端条件下设计材料的宝贵工具.
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