基于Python的算法用于估计数据库中不可用的物质物理性质模型的参数
Jina Lee1, Wangyun Won2, Jun-Woo Kim1
1CJ BIO Research Institute, CJ CheilJedang Corp., Suwon-Si, Gyeonggi-do 16495, Republic of Korea.
ACS omega
|March 18, 2024
概括
一个新的Python算法使用SMILES字符串和双极时刻估计了诸如沸点和粘度之类的分子特性. 它为没有在现有数据库中的物质提供了准确的预测,作为一个有价值的参考.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
- 物理化学 物理化学
背景情况:
- 准确估计纯成分物理性质对于化学过程设计和模拟至关重要.
- 现有的属性估计方法可能无法适用于新型或复杂的分子.
- 对于广泛的分子性质,对可靠的预测工具的需求是显著的.
研究的目的:
- 开发和验证基于Python的算法,用于估计分子的关键物理性质.
- 为了利用简化分子输入线输入规范 (SMILES) 字符串和双极时刻作为输入.
- 为没有在当前属性数据库中存在的分子提供准确的属性估计.
主要方法:
- 开发了一个内部的Python算法,集成了多个已建立的模型 (Joback,Riedel,Gunn-Yamada,Clausius-Clapeyron,Brock-Bird,Letsou-Stiel,Chapman-Enskog-Brokaw,Sato-Riedel,Stiel-Thodos). 开发了一个内部的Python算法,集成了多个已建立的模型 (Joback,Riedel,Gunn-Yamada,Clausius-Clapeyron,Brock-Bird,Letsou-Stiel,Chapman-Enskog-Brokaw,Sato-Riedel,Stiel-Thodos).
- 使用分子动力学模拟与阿沃加德罗软件中的MMFF94力场计算二极点时刻.
- 在现有数据库中没有的六种新型化合物 (DHMF,FDA,DEMB,GSH,VITB5,HCYS,AH) 进行了案例研究.
主要成果:
- 该Python算法准确估计了正常沸点,关键性质,标准度,蒸汽压力,液体摩尔体积,热容量,粘度,导热率和表面张力.
- 交叉验证显示,大多数参数的结果与阿斯PCES几乎相同,通过Clausius-Clapeyron对蒸发预测的精确度.
- 该算法成功为缺乏先前实验数据的化合物提供了属性参数.
结论:
- 开发的基于Python的算法是估计各种纯组件属性参数的可靠工具.
- 它提供了有价值和明确的参考,特别是对于新型分子.
- 该方法在化学性质预测中表现出高精度和广泛适用性.
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