ArticleChemical science2021
Physically inspired deep learning of molecular excitations and photoemission spectra.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- qsGW quasiparticle and GW-BSE excitation energies of 133,885 molecules.Scientific data · 2026Article
- Cartesian equivariant representations for learning and understanding molecular orbitals.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Rapid Detection of Perfluorooctanesulfonic Acid Using Surface-Enhanced Raman Spectroscopy and Deep Learning.ACS omega · 2025Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules.Journal of chemical information and modeling · 2025Article
- Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework.Journal of chemical theory and computation · 2025Article
- Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians.Nature communications · 2025Article
- Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales.Journal of chemical theory and computation · 2025Article
- A deep equivariant neural network approach for efficient hybrid density functional calculations.Nature communications · 2024Article
- Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors.Nature communications · 2024Article
- Electronic Excited States from Physically Constrained Machine Learning.ACS central science · 2024Article
- Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules.The journal of physical chemistry letters · 2024Article
- Physics-inspired machine learning of localized intensive properties.Chemical science · 2023Article
- Deep learning study of tyrosine reveals that roaming can lead to photodamage.Nature chemistry · 2022Article
- Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory andChemistry of materials : a publication of the American Chemical Society · 2022Article
- Machine Learning of Quasiparticle Energies in Molecules and Clusters.Journal of chemical theory and computation · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput screening is bound to fail without efficient methods to predict molecular excited-state properties across chemical space. In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations. The model exploits the fundamental underlying physics of molecular resonances as eigenvalues of a latent Hamiltonian matrix and is thus able to accurately describe multiple resonances simultaneously. The performance of this model is demonstrated for a range of organic molecules across chemical composition space and configuration space. We further showcase the model capabilities by predicting photoemission spectra at the level of the GW approximation for previously unseen conjugated molecules.
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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.