ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2019
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in internet financial risk management: A systematic literature review.PloS one · 2024Pooled it
- CRISP: Enhancing ASE Workflows With Advanced Molecular Simulation Post-Processing.Journal of computational chemistry · 2026Article
- A Multimodal Representation Learning Framework for Molecular Graph and NMR Spectrum Alignment.Entropy (Basel, Switzerland) · 2026Article
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- ML Workflows for Screening Degradation-Relevant Properties of Forever Chemicals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Deep Learning-Driven Intelligent Fluorescent Probes: Advancements in Molecular Design for Accurate Food Safety Detection.Foods (Basel, Switzerland) · 2025Review
- Single-Molecule SERS Detection of Phosphorylation in Serine and Tyrosine Using Deep Learning-Assisted Plasmonic Nanopore.The journal of physical chemistry letters · 2025Article
- Article
- Machine Learning Classification of Chirality and Optical Rotation Using a Simple One-Hot Encoded Cartesian Coordinate Molecular Representation.Journal of chemical information and modeling · 2025Article
- PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals.Scientific data · 2025Article
- Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Cluster-Based Deep Learning Model Perceiving Series Correlation for Accurate Prediction of Phonon Spectrum.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- High performance filtering and high-sensitivity concentration retrieval of methane in photoacoustic spectroscopy utilizing deep learning residual networks.Photoacoustics · 2024Article
- Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.Journal of the American Chemical Society · 2024Article
- Identification of extracellular vesicles from their Raman spectra via self-supervised learning.Scientific reports · 2024Article
- Article
- Beagle: a near-edge X-ray absorption fine-structure spectroscopy data processing solution for beamline experiments at Pohang Accelerator Laboratory.Journal of synchrotron radiation · 2024Article
- Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) are trained and assessed. The inputs for the neural networks are the coordinates and charges of the constituent atoms of each molecule. Already, the MLP is able to learn spectra, but the root mean square error (RMSE) is still as high as 0.3 eV. The learning quality improves significantly for the CNN (RMSE = 0.23 eV) and reaches its best performance for the DTNN (RMSE = 0.19 eV). Both CNN and DTNN capture even small nuances in the spectral shape. In a showcase application of this method, the structures of 10k previously unseen organic molecules are scanned and instant spectra predictions are obtained to identify molecules for potential applications.
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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.