对于富含的能量材料的冲击灵敏性的一般模型:一个结合的增量理论和遗传功能近似研究
Zhixiang Zhang1,2, Yiding Ma1,2, Chao Chen1,2
1Xi'an Modern Chemistry Research Institute, Xi'an, 710065, P. R. China.
概括
这项研究引入了基因功能近似 (GFA) 来预测各种化学家族中的冲击敏感性. 开发的模型提供了准确的预测,有助于安全处理高能耗材料.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 预测化学化合物的冲击灵敏度对于处理高能材料的安全性至关重要.
- 现有的灵敏度预测方法可能在计算上昂贵或范围有限.
研究的目的:
- 开发和验证一种使用基因函数近似 (GFA) 预测冲击灵敏性的新方法.
- 建立适用于广泛化学家族的准确预测模型.
主要方法:
- 使用基因函数近似 (GFA) 作为冲击灵敏度预测的回归技术.
- 开发了7个使用27-32个基本函数的经验模型,包括结构和拓描述符.
- 使用弗里德曼不合适作为评分函数,以防止过度拟合,并为一些描述符采用半经验性PM6计算.
主要成果:
- 在训练组 (0.80≤R2≤0.83,7.2 J≤RMSE≤7.8 J) 和测试组 (0.64≤R2≤0.70,11.2 J≤RMSE≤12.4 J) 实现了高预测准确度.
- 模型使用简单的,计算上便宜的描述符,避免复杂的量子化学计算.
- 预测过程很快,平均每种化合物在结构准备后几分钟.
结论:
- 基因函数近似提供了一种强大而有效的方法,用于预测各种化学家族的冲击灵敏度.
- 开发的模型为新的和现有的能量材料的初步安全评估提供了一个实用的工具.
- 该方法证明了GFA在化学信息学中的潜力,用于预测材料特性.
相关概念视频
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
54
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54
01:19Impact Loading
198
Impact loading occurs when a moving object collides with a stationary structure, such as a rod with a uniform cross-sectional area fixed at one end. Under these conditions, the rod absorbs the kinetic energy from the striking object, leading to deformation and subsequent stress development. As the rod returns to its original position and reaches maximum stress, the absorbed energy, initially manifested as kinetic energy, transforms entirely into strain energy.
In cases of elastic deformation,...
In cases of elastic deformation,...
198
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
41
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
41
03:33Genetic Drift
39.8K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
39.8K
01:09Mutation, Gene Flow, and Genetic Drift
58.4K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.4K
01:15Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)
317
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
317