Evidence map›Paper›PMID 41727839›Full record

ArticleACS nanoscience Au2026

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments.

Ralph Bulanadi, Jawad Chowdhury, Hiroshi Funakubo, Maxim Ziatdinov, Rama Vasudevan, Arpan Biswas, Yongtao Liu

Abstract read
In one paragraph

Article in ACS nanoscience Au, 2026. 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. 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

7 authors.

Ralph BulanadiCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
Jawad ChowdhuryCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
Hiroshi FunakuboDepartment of Material Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo, Yokohama 226-8502, Japan.ORCID https://orcid.org/0000-0002-1106-200X
Maxim ZiatdinovPhysical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
Rama VasudevanCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.ORCID https://orcid.org/0000-0003-4692-8579
Arpan BiswasUniversity of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, Tennessee 37996, United States.
Yongtao LiuCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.ORCID https://orcid.org/0000-0003-0152-1783

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS

Indexed as

artificial intelligenceautonomous experimentsferroelectricsmachine learningnoveltypiezoresponse force microscopy

Identifiers

PMID41727839
PMCPMC12921619

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.