Evidence map›Paper›PMID 36658720›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023

Interpretable Machine Learning of Two-Photon Absorption.

Yuming Su, Yiheng Dai, Yifan Zeng, Caiyun Wei, Yangtao Chen, Fuchun Ge, Peikun Zheng, Da Zhou, Pavlo O Dral, Cheng Wang

Open access · goldFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 27 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Interpretable Machine Learning of Two-Photon Absorption.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Yuming SuState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, iChem, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, 361005, Xiamen, P. R. China.
Yiheng DaiState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, iChem, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, 361005, Xiamen, P. R. China.
Yifan ZengState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, iChem, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, 361005, Xiamen, P. R. China.
Caiyun WeiState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, iChem, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, 361005, Xiamen, P. R. China.
Yangtao ChenState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, iChem, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, 361005, Xiamen, P. R. China.
Fuchun GeDepartment of Chemistry, College of Chemistry and Chemical Engineering, iChem, Xiamen University, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, 361005, Xiamen, P. R. China.
Peikun ZhengDepartment of Chemistry, College of Chemistry and Chemical Engineering, iChem, Xiamen University, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, 361005, Xiamen, P. R. China.
Da ZhouSchool of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computation, Xiamen University, Xiamen, 361005, P. R. China.
Pavlo O DralDepartment of Chemistry, College of Chemistry and Chemical Engineering, iChem, Xiamen University, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, 361005, Xiamen, P. R. China.
Cheng WangState Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, iChem, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, 361005, Xiamen, P. R. China.ORCID https://orcid.org/0000-0002-7906-8061
Xiamen University · CN

Funding

Fundamental Research Funds for the Central Universities 20720210092National Key R&D Program of China 2021YFA1502500National Natural Science Foundation of China 21721001National Natural Science Foundation of China 22003051National Natural Science Foundation of China 22071207National Natural Science Foundation of China 22125502NFFTBS J1310024
6 · The paper itself

Abstract

Molecules with strong two-photon absorption (TPA) are important in many advanced applications such as upconverted laser and photodynamic therapy, but their design is hampered by the high cost of experimental screening and accurate quantum chemical (QC) calculations. Here a systematic study is performed by collecting an experimental TPA database with ≈900 molecules, analyzing with interpretable machine learning (ML) the key molecular features explaining TPA magnitudes, and building a fast ML model for predictions. The ML model has prediction errors of similar magnitude compared to experimental and affordable QC methods errors and has the potential for high-throughput screening as additionally validated with the new experimental measurements. ML feature analysis is generally consistent with common beliefs which is quantified and rectified. The most important feature is conjugation length followed by features reflecting the effects of donor and acceptor substitution and coplanarity.

Indexed as

conjugation lengthmachine learningrational designtwo-photon absorption

Identifiers

PMID36658720
PMCPMC10015897
OpenAlexW4317568159

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read12
Read underepoch 390

Registered trials

None linked

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.