Experimental database of optical properties of organic compounds

Joung, Joonyoung F.; Han, Minhi; Jeong, Minseok; Park, Sungnam

doi:10.1038/s41597-020-00634-8

Cited by 62 publications

(60 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compiled experimental UV/Vis absorption data from several of the largest openlyavailable datasets: Deep4Chem, 17 ChemFluor, 16 Dye Aggregation (DyeAgg), 15 ChemDataExtractor (CDEx), 14 and the Dye-Sensitized Solar Cell Database (DSSCDB). 1 Among the datasets listed in Table 1, these were the largest and most easily machine-readable, and they included solvent information for each measurement.…”

Section: Methods Data Sources and Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Multi-fidelity prediction of molecular optical peaks with deep learning

Greenman

Green

Gómez‐Bombarelli

2021

Preprint

View full text Add to dashboard Cite

Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28,000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties.

show abstract

Section: Methods Data Sources and Preprocessingmentioning

confidence: 99%

“…a single dye family) because of the limited availability of large UV/Vis datasets. This data sparsity has been addressed recently with the publication of several experimental datasets, 1,[11][12][13][14][15][16][17][18] described in Table 1. There are also several large computed datasets of excitation energies available (Table 2).…”

Section: Introductionmentioning

confidence: 99%

Multi-fidelity prediction of molecular optical peaks with deep learning

Greenman

Green

Gómez‐Bombarelli

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…There are other experimental datasets available which are popular for machine learning (ML) techniques but can be used for benchmarking and basic calibration using many datapoints, for example the collection by J. F. Joung et al 190 includes the optical properties for over 7000 unique chromophores, ranging from small molecules to relevant molecules such as pyrene, 191 coumarin, 192 azobenzene 193 etc. However, many screening studies opt to use smaller sets of specialised molecules due to the availability of experimental data.…”

Section: Computable Properties: Benchmarking and Calibrationmentioning

confidence: 99%

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The organic compounds optical properties database [14] contains over 20,000 rows which are combination of 7,016 chromophores in 365 solvents and 17 solid matrices or in solid states. Chromophores included in the database consist of maximum 150 atoms (except H) of C, N, O, S, F, Cl, Br, I, Se, Te, Si, P, B, Sn, Ge.…”

Section: Datasetsmentioning

confidence: 99%

An Attempt to Boost Molecular Descriptors with Quantum-Derived Features in Prediction of Maximum Emission Wavelengths of Chromophores

Fliszkiewicz¹

2021

Preprint

View full text Add to dashboard Cite

The following research assesses the capability of machine learning in predicting maximum emission wavelength of organic compounds. The predictions are based on structure descriptors and fingerprints widely applied in cheminformatics. In an attempt to further improve accuracy, developed machine learning models were enriched with quantum mechanics derived features. Multi linear, gradient boosting and random forest regressions were applied. Computers were trained and tested with database of experimental data of optical properties.

show abstract

Experimental database of optical properties of organic compounds

Cited by 62 publications

References 22 publications

Multi-fidelity prediction of molecular optical peaks with deep learning

Multi-fidelity prediction of molecular optical peaks with deep learning

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

An Attempt to Boost Molecular Descriptors with Quantum-Derived Features in Prediction of Maximum Emission Wavelengths of Chromophores

Contact Info

Product

Resources

About