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RESEARCH PAPER · Advanced Science · 2019

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Kunal Ghosh, Annika Stuke, Milica Todorović, Peter Bjørn Jørgensen, Mikkel Nørgaard Schmidt, Aki Vehtari, Patrick Rinke

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Deep Learning Spectroscopy: Neural Networks for Molecular Excitation SpectraOriginalComparative analysis of methods for ascertainment the similarity between reflected spectra obtained from skin lesions2Machine learning based temperature prediction of poly( N -isopropylacrylamide)-capped plasmonic nanoparticle solutions5Chemical diversity in molecular orbital energy predictions with kernel ridge regression78Chemical diversity in molecular orbital energy predictions with kernel ridge regression70Data‐Driven Materials Science: Status, Challenges, and Perspectives700High-throughput screening for improved microbial cell factories, perspective and promise75DScribe: Library of descriptors for machine learning in materials science845Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning65Revealing ferroelectric switching character using deep recurrent neural networks49Dataset’s chemical diversity limits the generalizability of machine learning predictions127Decoding Phases of Matter by Machine-Learning Raman Spectroscopy25Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Machine Learning Transition Temperatures from 2D Structure10Polarimetry with Disordered Photonic Structures8A library of ab initio Raman spectra for automated identification of 2D materials62Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach56Pushing property limits in materials discovery via boundless objective-free exploration36Raman spectroscopy for extracellular vesicle study2Atomic structures and orbital energies of 61,489 crystal-forming organic molecules101Deep neural network for x-ray photoelectron spectroscopy data analysis26Quantum Chemistry in the Age of Machine Learning417Deep Learning for Optoelectronic Properties of Organic Semiconductors58Quantifying similarity for spectra with a large number of overlapping transitions: Examples from soft X-ray spectroscopy10Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy9Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals13Deep learning as phase retrieval tool for CARS spectra59Deep learning for NLTE spectral opacities45Spectroscopy as a tool for detection and monitoring of Coronavirus (COVID-19)48Removing non-resonant background from CARS spectra via deep learning93Machine learning and excited-state molecular dynamics62A library of ab initio Raman spectra for automated identification of 2D materials75Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics20Unravelling the Kinetic Model of Photochemical Reactions via Deep Learning22Wasserstein metric for improved quantum machine learning with adjacency matrix representations17Binary Stochastic Filtering: feature selection and beyond2Machine Learning for Absorption Cross Sections77Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments47Experimental database of optical properties of organic compounds112Designing and understanding light-harvesting devices with machine learning112Molecular Machine Learning: The Future of Synthetic Chemistry?72Molekulares maschinelles Lernen: Die Zukunft der Synthesechemie?3Unsupervised search of low-lying conformers with spectroscopic accuracy: A two-step algorithm rooted into the island model evolutionary algorithm39Machine-learning Prediction of Infrared Spectra of Interstellar Polycyclic Aromatic Hydrocarbons39Machine learning a molecular Hamiltonian for predicting electron dynamics17Machine learning in materials design: Algorithm and application*62AI-enabled real-time dual-comb molecular fingerprint imaging29Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space72Prediction of Molecular Electronic Transitions Using Random Forests54Designing thermal functional materials by coupling thermal transport calculations and machine learning33A Machine Learning Protocol for Predicting Protein Infrared Spectra107Artificial Neural Networks for Noise Removal in Data‐Sparse Charged Particle Imaging Experiments11Machine Learning for Electronically Excited States of Molecules398Ab initio machine learning in chemical compound space4Application of Ligand Field Theory for Simulation of the Pre-Edge Structure of X-Ray Absorption Spectra of Amorphous Systems0Choosing the right molecular machine learning potential181Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism11Machine learning dielectric screening for the simulation of excited state properties of molecules and materials28Perceptron: Basic Principles of Deep Neural Networks2Physically inspired deep learning of molecular excitations and photoemission spectra65Robustness and accuracy improvement of data processing with 2D neural networks for transient absorption dynamics3Machine Learning Predictions of Transition Probabilities in Atomic Spectra6Deep learning‐based automatic detection of multitype defects in photovoltaic modules and application in real production line72Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules29Comment on acp-2020-12580Comment on acp-2020-12580Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design2Review—Recent Development of Detection Methods for Controlling COVID-19 Outbreak19Neural Networks for Analysis of Optical Properties in 2D Layered Hybrid Lead Halide Perovskites8Deep Learning Optical Spectroscopy Based on Experimental Database: Potential Applications to Molecular Design170Salt content in saline-alkali soil 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integrating machine learning into computational chemistry and materials science203Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network45A machine learning vibrational spectroscopy protocol for spectrum prediction and spectrum-based structure recognition61Future directions of chemical theory and computation6Removal of cross-phase modulation artifacts in ultrafast pump–probe dynamics by deep learning23The Rise of Neural Networks for Materials and Chemical Dynamics92Interpreting convolutional neural network for real-time volatile organic compounds detection and classification using optical emission spectroscopy of plasma42Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion208Machine Learning of Quasiparticle Energies in Molecules and Clusters17A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds3Atomic Simulation Recipes: A Python framework and library for automated workflows73Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data8Ab Initio Machine Learning in Chemical Compound Space195Choosing the right molecular machine learning potential8Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning33Time Frequency Representations and Deep Convolutional Neural Networks: A Recipe for Molecular Properties Prediction3Machine Learning with Explainable Artificial Intelligence Vision for Characterization of Solution Conductivity Using Optical Emission Spectroscopy of Plasma in Aqueous Solution21An Artificial Intelligence‐Based Motion Trajectory Prediction of Fibrous Matters3An Introduction to High Performance Computing and Its Intersection with Advances in Modeling Rare Earth Elements and Actinides4Machine learning inversion design and application verification of a broadband acoustic filtering structure37Molecular Screening for Terahertz Detection with Machine-Learning-Based Methods11Comparing End-to-End Machine Learning Methods for Spectra Classification14Machine learning prediction of UV–Vis spectra features of organic compounds related to photoreactive potential40Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing16Molecular representations for machine learning applications in chemistry93Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra12Effect of non-resonant background on the extraction of Raman signals from CARS spectra using deep neural networks17Machine Learning Applied for Spectra Classification in X-ray Free Electorn Laser Sciences10Deep Learning for Photonic Design and Analysis: Principles and Applications30Roadmap on Machine learning in electronic structure180Gaussian process regression for absorption spectra analysis of molecular dimers3Deep Learning in Computational Linguistics for Chinese Language Translation12Prediction of Maximum Absorption Wavelength Using Deep Neural Networks50Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43Recent advances and applications of deep learning methods in materials science1161Convolutional neural network-based retrieval of Raman signals from CARS spectra17Prediction of multiple pH compartments by deep learning in magnetic resonance spectroscopy with hyperpolarized 13C-labelled zymonic acid1Beyond Woodward–Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy20Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery26Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors36Machine learning sparse tight-binding parameters for defects23Emulator-based decomposition for structural sensitivity of core-level spectra9Application of Machine Learning in Developing Quantitative Structure–Property Relationship for Electronic Properties of Polyaromatic Compounds10Deep Learning and Infrared Spectroscopy: Representation Learning with a β-Variational Autoencoder26Graph representation-based machine learning framework for predicting electronic band structures of quantum-confined nanostructures21Enhanced Raman Distributed Temperature Sensor Based on Self-Constructed Fully Connected Neural Network7Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW69Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space18Predicting the maximum absorption wavelength of azo dyes using an interpretable machine learning strategy51Machine learning in interpretation of electronic core-level spectra9VOC-Net: A Deep Learning Model for the Automated Classification of Rotational THz Spectra of Volatile Organic Compounds20EasyCID: Make component identification easy in Raman spectroscopy20Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling5Learning excited-state properties2Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning9Couplet Analysis of Linguistic Topology Using Deep Neural Networks in Cognitive Linguistics0Machine Learning Spectroscopy Based on Group Contribution and Molecule Contribution Methods0Multispectral Portable Fibre-Optic Reflectometer for the Classification of the Origin of Chicken Eggshells in the Case of Mycoplasma synoviae Infections2Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers26Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra22Machine learning methods in photochemistry and photophysics10Machine Learning Spectroscopy Using a 2-Stage, Generalized Constituent Contribution Protocol14Towards structural reconstruction from X-ray spectra13Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models8Transfer learning as a method to reproduce high-fidelity non-local thermodynamic equilibrium opacities in simulations8

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