Electro-optic and spectroscopic properties of push–pull-chromophores with non-aromatic π-bridges

Rinderspacher, B. Christopher

doi:10.1016/j.cplett.2013.08.082

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The hyperpolarizability of each respective material was then computed using the CNDO level of theory. 12 Spectra of the molecules were computed using BH&HLYP/6-31+G(d), 60 within the framework of time-dependent density functional theory (TD-DFT) 58,62 using Gaussian 09. 60…”

Section: Computational Detailsmentioning

confidence: 99%

Smooth heuristic optimization on a complex chemical subspace

Elward

Rinderspacher

2015

Phys. Chem. Chem. Phys.

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Several algorithms for optimizing a combinatorial subspace of chemical compound space with constraints are compared. The test system is a library of organic chromophores for electro-optic applications. The constraints on the optimization include the maximization of the candidate structure hyperpolarizability while keeping the absorption within acceptable limits in the range of 400-700 nm. The best pay-off in terms of primary objective, feasibility and computational cost is achieved using a heuristic reordering of orthogonal search directions.

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Section: Computational Detailsmentioning

confidence: 99%

Smooth heuristic optimization on a complex chemical subspace

Elward

Rinderspacher

2015

Phys. Chem. Chem. Phys.

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“…The keto–enol tautomerization process is highly responsive to stimuli like changing the pH of the chemical environment as well as exposure to low-voltage current. Colors are manipulated via the donor–acceptor setup, which is also found in other optical applications such as nonlinear optics. , …”

Section: Introductionmentioning

confidence: 99%

“…Colors are manipulated via the donor−acceptor setup, which is also found in other optical applications such as nonlinear optics. 45,46 This article is organized as follows. We first outline the underlying theory and algorithmic considerations.…”

Section: ■ Introductionmentioning

confidence: 99%

Heuristic Global Optimization in Chemical Compound Space

Rinderspacher

2020

J. Phys. Chem. A

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We report developments in combinatorial optimization under constraints in chemical space. We considered random functions, which serve as a baseline to measure performance, and constrained optimizations over two databases of electrochromic molecules (∼5000 and 10 12 , respectively). These problems were optimized using sequential heuristic next-neighbor search (HNNS) (introduced in Elward and Rinderspacher Phys. Chem. Chem. Phys. 2015, 17, 24322 and Elward and Rinderspacher Mol. Syst. Des. Eng. 2018, 3, 485) and kernel-based efficient global optimization (EGO) with two reordering strategies. In addition to the average ordering method introduced with sequential HNNS, a new reordering based on a locally separable linear model is formulated and applied. Presented is the analysis of periodic kernels for EGO: the modified Dirichlet kernels D ̃n(x) = ∑ i n [cos(2πix) − (−1) i ]/i, the minimalist kernel K N (x) = cos(πx/N) 2 /2 − δ x /2, and an analogue to the popular Gaussian kernel, G N σ (x) = e −|x mod N| 2 /(2σ 2 ), where n is the order of the Dirichlet kernel, N is the number of choices on a combinatorial site, and σ is a broadening factor. We find that reordering is pivotal for high optimization efficiency, in both the average and worst-case scenarios. The new linear-estimation ordering paradigm is superior in the chemistry context. Furthermore, with judicious use of hyperparameters and algorithmic choices, EGO outperforms HNNS. The global optimum for the small chemical problem is found with >98% likelihood for all global search methods employing a linear reordering heuristic for organizing the search space.

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