Evidence map›Paper›PMID 40394175›Full record

ArticleScientific reports2025

Quantitative analysis of EXAFS data sets using deep reinforcement learning.

Eun-Suk Jeong, In-Hui Hwang, Sang-Wook Han

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. AI-EXAFS: Physics-Informed Prediction of EXAFS from Atomic Structure.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. 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

3 authors.

Eun-Suk JeongDepartment of Physics Education, Institute of Fusion Science, and Institute of Science Education, Jeonbuk National University, Jeonju, 54896, Korea.
In-Hui HwangPohang Accelerator Laboratory, POSTECH, Pohang, 37673, Korea.
Sang-Wook HanDepartment of Physics Education, Institute of Fusion Science, and Institute of Science Education, Jeonbuk National University, Jeonju, 54896, Korea. shan@jbnu.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extended X-ray absorption fine structure (EXAFS) serves as a unique tool for accurately characterizing the local structural properties surrounding specific atoms. However, the quantitative analysis of EXAFS data demands significant effort. Artificial intelligence (AI) techniques, including deep reinforcement learning (RL) methods, present a promising avenue for the rapid and precise analysis of EXAFS data sets. Unlike other AI approaches, a deep RL method utilizing reward values does not necessitate a large volume of pre-prepared data sets for training the neural networks of the AI system. We explored the application of a deep RL method for the quantitative analysis of EXAFS data sets, utilizing the reciprocal of the R-factor of a fit as the reward metric. The deep RL method effectively determined the local structural properties of PtO

Indexed as

Artificial intelligenceExtended X-ray absorption fine structureLocal structural propertyMachine learningReinforcement learning

Identifiers

PMID40394175
PMCPMC12092777

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.