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Updated: Jun 9, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Optimizing Reorganization Energies of Small Conjugated Molecules Using an Inverse-Design Approach.

Hao Xu1,2, Michael Springborg1

  • 1Laboratory of Theoretical Chemistry, Department of Chemistry, Namur Institute of Structured Matter (NISM), University of Namur, Rue de Bruxelles 61, 5000 Namur, Belgium.

The Journal of Physical Chemistry. A
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

We developed an inverse-design approach, Poor Man's Materials Optimization (PooMa), to efficiently discover high-performing organic molecules for organic solar cells (OSCs). This method combines genetic algorithms and DFTB calculations, reducing computational cost and identifying promising conjugated systems.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Organic Electronics

Background:

  • Organic solar cells (OSCs) are of significant scientific interest.
  • Identifying optimal organic molecules is challenging due to vast chemical space.

Purpose of the Study:

  • To apply an inverse-design approach for identifying conjugated systems with low reorganization energies.
  • To demonstrate the efficacy of the Poor Man's Materials Optimization (PooMa) method.

Main Methods:

  • Utilized an inverse-design approach combining global optimization via genetic algorithms (GA).
  • Employed a fast, efficient parametrized density-functional tight-binding (DFTB) method for electronic structure calculations.
  • Constructed organic molecules using benzene and pyridine as test systems with 20 functional groups.

Main Results:

  • Successfully constructed numerous novel organic structures.
  • Identified molecules with good performance for charge-transfer processes at low computational cost.
  • Demonstrated the capability of PooMa in navigating the chemical space efficiently.

Conclusions:

  • The PooMa approach is effective for discovering high-performance organic materials for OSCs.
  • The method offers a low-cost computational solution for materials optimization.
  • A supporting theory was developed to explain the study's findings.