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RESEARCH PAPER · Nature Reviews Chemistry · 2021

Molecular excited states through a machine learning lens

Pavlo O. Dral, Mario Barbatti

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Molecular excited states through a machine learning lensOriginalChoosing the right molecular machine learning potential181High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Mechanistic photophysics and photochemistry of unnatural bases and sunscreen molecules: insights from electronic structure calculations14Deep integration of machine learning into computational chemistry and materials science2Perspective on integrating machine learning into computational chemistry and materials science203Essentiality of the Basis Function in Deep Learning Physical Chemistry Properties0Machine Learning of Quasiparticle Energies in Molecules and Clusters17Speeding up quantum dissipative dynamics of open systems with kernel methods2Choosing the right molecular machine learning potential8Gaussian Process Regression for Materials and Molecules1221Speeding up quantum dissipative dynamics of open systems with kernel methods4MOLGENGO: Finding Novel Molecules with Desired Electronic Properties by Capitalizing on Their Global Optimization3OS100: A Benchmark Set of 100 Digitized UV-Visible Spectra and Derived Experimental Oscillator Strengths0Speeding up quantum dissipative dynamics of open systems with kernel methods41SPAHM: the Spectrum of Approximated Hamiltonian Matrices representations0Computational modeling of green hydrogen generation from photocatalytic H2S splitting: Overview and perspectives40Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface8Artificial intelligence-enhanced quantum chemical method with broad applicability143Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain42OS100: A Benchmark Set of 100 Digitized UV-Visible Spectra and Derived Experimental Oscillator Strengths0Ab initio neural network MD simulation of thermal decomposition of a high energy material CL-20/TNT59An open-source framework for fast-yet-accurate calculation of quantum mechanical features4Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models1Fingerprint-based deep neural networks can model thermodynamic and optical properties of eumelanin DHI dimers7Multi-fidelity prediction of molecular optical peaks with deep learning93SPA H M: the spectrum of approximated Hamiltonian matrices representations19The resolution- vs. -accuracy dilemma in machine learning modeling of electronic excitation spectra16OS100: A Benchmark Set of 100 Digitized UV–Visible Spectra and Derived Experimental Oscillator Strengths20Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites14Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental Chemistry59Hole utilization in solar hydrogen production155The atomistic modeling of light-harvesting complexes from the physical models to the computational protocol62Machine learning potential for interacting dislocations in the presence of free surfaces13Effect of molecular representation on deep learning performance for prediction of molecular electronic properties9Prediction of Maximum Absorption Wavelength Using Deep Neural Networks50Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43Simulations of molecular photodynamics in long timescales32Machine learned calibrations to high-throughput molecular excited state calculations28Inverse molecular design from first principles: Tailoring organic chromophore spectra for optoelectronic applications24Excited‐State Distortions Promote the Photochemical 4π‐Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations17Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning15A Look Inside the Black Box of Machine Learning Photodynamics Simulations54Ab initio machine learning of phase space averages18Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW69Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models2Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models0Extending machine learning beyond interatomic potentials for predicting molecular properties167Benchmarking Computational Approaches to Predict the UV-Vis Spectra of Organic Photocatalysts0Large-Scale Benchmarking of Multireference Vertical-Excitation Calculations via Automated Active-Space Selection43Non-Aromatic 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