Analytical First Derivatives of the SCF Energy for the Conductor-Like Polarizable Continuum Model With Non-Static Radii

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Lukas Wittmann, Miquel Garcia-Ratés, Christoph Riplinger
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引用次数: 0

Abstract

Within this work, we present the derivation and implementation of analytical gradients for the Gaussian-switching (SwiG) Conductor-like Polarizable Continuum Model (CPCM) with general nuclear coordinate-dependent non-static radii used for the creation of van der Waals-type cavities. This is done using the recently presented dynamic radii adjustment for continuum solvation (Draco) scheme. This allows for efficient geometry optimization and reasonable numerical Hessian calculations. The derived gradient is implemented in ORCA, and therefore is easily applicable. The derivation and implementation is validated by comparing analytical and numerical gradients and testing geometry optimizations on a diverse test set, including small organic compounds, metal-organic complexes, and highly charged species. We additionally test the continuity of the potential energy surface using an example where very strong changes in the radii occur. The computational efficiency of the derived gradient is investigated.

Abstract Image

具有非静态半径的类导体极化连续体模型的SCF能量的解析一阶导数
在这项工作中,我们提出了高斯开关(SwiG)类导体极化连续体模型(CPCM)的解析梯度的推导和实现,该模型具有一般核坐标相关的非静态半径,用于创建范德华型腔。这是使用最近提出的连续溶剂化(Draco)方案的动态半径调整来完成的。这允许有效的几何优化和合理的数值黑森计算。推导出的梯度是在ORCA中实现的,因此易于应用。通过比较分析梯度和数值梯度,并在不同的测试集(包括小有机化合物、金属有机配合物和高电荷物质)上测试几何优化,验证了推导和实现。我们还使用一个半径发生非常强烈变化的例子来测试势能面的连续性。研究了所得梯度的计算效率。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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