ReviewNature communications2020
Designing and understanding light-harvesting devices with machine learning.
Review in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Cu-Enabled Photoinduced Charge Transfer in Green-Processed Cu-BPDC Metal-Organic Framework Films on FTO.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- Design Concept of Metal Sulfide Photocatalyst for Efficient Photocatalytic Hydrogen Evolution.Nano-micro letters · 2026Review
- Quantum Biology Using Ultrafast Integrative and Molecular Engineering Tools: A Perspective.Computational and structural biotechnology journal · 2026Review
- Dissipation Pathways in a Photosynthetic Complex.The journal of physical chemistry letters · 2025Article
- Predicting electronic screening for fast Koopmans spectral functional calculations.npj computational materials · 2024Article
- Exploration of Solution-Processed Bi/Sb Solar Cells by Automated Robotic Experiments Equipped with Microwave Conductivity.JACS Au · 2023Article
- Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets.The journal of physical chemistry letters · 2023Article
- AI-driven robotic chemist for autonomous synthesis of organic molecules.Science advances · 2023Article
- An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning.Molecules (Basel, Switzerland) · 2023Article
- Self-assembled supramolecular artificial light-harvesting nanosystems: construction, modulation, and applications.Nanoscale advances · 2023Review
- The principles, design and applications of fused-ring electron acceptors.Nature reviews. Chemistry · 2022Review
- Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics.Energy & environmental science · 2022Article
- Evaluation guidelines for machine learning tools in the chemical sciences.Nature reviews. Chemistry · 2022Review
- Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics.Nature communications · 2022Article
- Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization.Journal of cheminformatics · 2021Article
- Unsupervised Learning Methods for Molecular Simulation Data.Chemical reviews · 2021Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
- Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides.Scientific reports · 2021Article
- Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES.Chemical science · 2021Article
- Machine learning dielectric screening for the simulation of excited state properties of molecules and materials.Chemical science · 2021Article
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
Understanding the fundamental processes of light-harvesting is crucial to the development of clean energy materials and devices. Biological organisms have evolved complex metabolic mechanisms to efficiently convert sunlight into chemical energy. Unraveling the secrets of this conversion has inspired the design of clean energy technologies, including solar cells and photocatalytic water splitting. Describing the emergence of macroscopic properties from microscopic processes poses the challenge to bridge length and time scales of several orders of magnitude. Machine learning experiences increased popularity as a tool to bridge the gap between multi-level theoretical models and Edisonian trial-and-error approaches. Machine learning offers opportunities to gain detailed scientific insights into the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-harvesting devices.
Identifiers
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.