ArticleChemical science2021
Machine learning of solvent effects on molecular spectra and reactions.
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 57 papers.
What it found
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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
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Who cites it
57 citing papers in PubMed.
- Cross Mol: cross-modal mask-predict pre-training for 3D molecular data.Bioinformatics (Oxford, England) · 2026Article
- High-throughput physics-based enzyme engineering.bioRxiv : the preprint server for biology · 2026Article
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Atomistic machine learning with irreducible Cartesian natural tensors.Nature communications · 2026Article
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- Infrared Spectra Prediction for a Carbonyl Group Utilizing a Graph Network Approach.Precision chemistry · 2026Article
- A versatile method for accurately predicting electronic absorption spectra of tetrapyrrole macrocycles.Scientific reports · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- How to Use Quantum Computers for Biomolecular Free Energies.Journal of chemical theory and computation · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Predicting Chromatographic Retention Times from Quantum-Chemical Solvation Free Energies: A Pilot Study of Oxysterols.The journal of physical chemistry. A · 2026Article
- Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions.The journal of physical chemistry. B · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules.Journal of chemical theory and computation · 2026Article
- Long-range electrostatics for machine learning interatomic potentials is easier than we thought.The Journal of chemical physics · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Artificial Intelligence-Powered Raman Spectroscopy through Open Science and FAIR Principles.ACS nano · 2025Review
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.Journal of chemical theory and computation · 2025Article
- Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.Journal of chemical theory and computation · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics/molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design.
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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.