一种改进的 NDDO 衍生物半经验方法的参数化程序。
An improved parameterization procedure for NDDO-descendant semi-empirical methods.
机构信息
NUS High School of Mathematics and Science, 20 Clementi Avenue 1, 129957, Singapore, Singapore.
Centre for Quantum Technologies, National University of Singapore, 117543, Singapore, Singapore.
出版信息
J Mol Model. 2023 Mar 28;29(4):118. doi: 10.1007/s00894-023-05499-3.
CONCEPT
MNDO-based semi-empirical methods in quantum chemistry have found widespread application in the modelling of large and complex systems. A method for the analytic evaluation of first and second derivatives of molecular properties against semi-empirical parameters in MNDO-based NDDO-descendant models is presented, and the resultant parameter Hessian is compared against the approximant currently used in parameterization for the PMx models.
METHODS
As a proof of concept, the exact parameter Hessian is employed in a limited reparameterization of MNDO for the elements C, H, N, O and F using 1206 molecules for reference data (heats of formation, ionization energies, dipole moments and reference geometries). The correctness of our MNDO implementation was verified by comparing the calculated molecular properties with the MOPAC program.
概念
量子化学中的 MNDO 基半经验方法在大型复杂系统的建模中得到了广泛应用。本文提出了一种针对 MNDO 基 NDDO 衍生模型中半经验参数的分子性质一阶和二阶导数的解析评估方法，并将得到的参数 Hessian 与 PMx 模型参数化中当前使用的逼近值进行了比较。
方法
作为概念验证，我们使用 1206 个分子的参考数据（生成热、电离能、偶极矩和参考几何形状），在 MNDO 对 C、H、N、O 和 F 元素的有限重新参数化中，使用精确的参数 Hessian。我们通过将计算出的分子性质与 MOPAC 程序进行比较，验证了 MNDO 实现的正确性。
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