ArticleJournal of chemical theory and computation2019
Toward Prediction of Electrostatic Parameters for Force Fields That Explicitly Treat Electronic Polarization.
Article in Journal of chemical theory and computation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Non-Covalent Molecular Interaction Rules to Define Internal Dimer Coordinates for Quantum Mechanical Potential Energy Scans.Journal of computational chemistry · 2025Article
- CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.The journal of physical chemistry. B · 2024Review
- Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.The Journal of chemical physics · 2023Article
- Machine-Learning-Guided Discovery of Electrochemical Reactions.Journal of the American Chemical Society · 2022Article
- Artificial Intelligence Applied to Battery Research: Hype or Reality?Chemical reviews · 2022Review
- Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.Journal of chemical theory and computation · 2022Article
- On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods.Journal of chemical information and modeling · 2022Article
- Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field.Journal of chemical theory and computation · 2022Article
- CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field.Journal of computational chemistry · 2022Article
- Integration of Experimental Data and Use of Automated Fitting Methods in Developing Protein Force Fields.Communications chemistry · 2022Review
- Global Optimization of the Lennard-Jones Parameters for the Drude Polarizable Force Field.Journal of chemical theory and computation · 2021Article
- Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors.Chemical science · 2020Article
- Deriving a Polarizable Force Field for Biomolecular Building Blocks with Minimal Empirical Calibration.The journal of physical chemistry. B · 2020Article
- Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression.Frontiers in chemistry · 2020Article
- Molecular Dynamics Simulations of Ionic Liquids and Electrolytes Using Polarizable Force Fields.Chemical reviews · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The derivation of atomic polarizabilities for polarizable force field development has been a long-standing problem. Atomic polarizabilities were often refined manually starting from tabulated values, rendering an automated assignment of parameters difficult and hampering reproducibility and transferability of the obtained values. To overcome this, we trained both a linear increment scheme and a multilayer perceptron neural network on a large number of high-quality quantum mechanical atomic polarizabilities and partial atomic charges, where only the type of each atom and its connectivity were used as input. The predicted atomic polarizabilities and charges had average errors of 0.023 Å
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