ArticleScientific data2024
COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine-Learning-Guided Discovery of Boron-Free Narrowband Blue Thermally Activated Delayed Fluorescence Emitters.Angewandte Chemie (International ed. in English) · 2026Article
- Size and Polarizability as Design Principles for Stereoselective Catalysis.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning.Chemical science · 2025Article
- From Rings to Properties: Understanding the Effect of Annelation on Pyrene.The Journal of organic chemistry · 2025Article
- Big-Data Analysis of Geometric Descriptors as Efficient Predictors of Energetic Stability in Nonplanar Polycyclic Aromatic Hydrocarbons.Journal of computational chemistry · 2025Article
- A database of steric and electronic properties of heteroaryl substituents.Scientific data · 2025Article
- Quantum chemical properties of chlorinated polycyclic aromatic hydrocarbons for delta machine learning.Scientific data · 2025Article
- COMPAS-4: A Data Set of (BN)Journal of chemical information and modeling · 2025Article
- COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems.Scientific data · 2024Article
- Hetero-polycyclic aromatic systems: A data-driven investigation of structure-property relationships.Beilstein journal of organic chemistry · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polycyclic aromatic systems are highly important to numerous applications, in particular to organic electronics and optoelectronics. High-throughput screening and generative models that can help to identify new molecules to advance these technologies require large amounts of high-quality data, which is expensive to generate. In this report, we present the largest freely available dataset of geometries and properties of cata-condensed poly(hetero)cyclic aromatic molecules calculated to date. Our dataset contains ~500k molecules comprising 11 types of aromatic and antiaromatic building blocks calculated at the GFN1-xTB level and is representative of a highly diverse chemical space. We detail the structure enumeration process and the methods used to provide various electronic properties (including HOMO-LUMO gap, adiabatic ionization potential, and adiabatic electron affinity). Additionally, we benchmark against a ~50k dataset calculated at the CAM-B3LYP-D3BJ/def2-SVP level and develop a fitting scheme to correct the xTB values to higher accuracy. These new datasets represent the second installment in the COMputational database of Polycyclic Aromatic Systems (COMPAS) Project.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.