ArticleLight, science & applications2019
Emerging role of machine learning in light-matter interaction.
Article in Light, science & applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules.Materials (Basel, Switzerland) · 2024Article
- A newcomer's guide to deep learning for inverse design in nano-photonics.Nanophotonics (Berlin, Germany) · 2023Review
- Review
- Machine learning reduces soft costs for residential solar photovoltaics.Scientific reports · 2023Article
- Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Learning to simulate high energy particle collisions from unlabeled data.Scientific reports · 2022Article
- Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures.Nanomaterials (Basel, Switzerland) · 2022Article
- A learning based approach for designing extended unit cell metagratings.Nanophotonics (Berlin, Germany) · 2022Article
- Decoding Optical Data with Machine Learning.Laser & photonics reviews · 2021Article
- Advances and challenges for fluorescence nanothermometry.Nature methods · 2020Review
- Deep learning: a new tool for photonic nanostructure design.Nanoscale advances · 2020Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has provided a huge wave of innovation in multiple fields, including computer vision, medical diagnosis, life sciences, molecular design, and instrumental development. This perspective focuses on the implementation of machine learning in dealing with light-matter interaction, which governs those fields involving materials discovery, optical characterizations, and photonics technologies. We highlight the role of machine learning in accelerating technology development and boosting scientific innovation in the aforementioned aspects. We provide future directions for advanced computing techniques via multidisciplinary efforts that can help to transform optical materials into imaging probes, information carriers and photonics devices.
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What OpenQuestion holds
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.