ArticleJournal of chemical theory and computation2018
Force Field Parametrization of Metal Ions from Statistical Learning Techniques.
Article in Journal of chemical theory and computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Integration of Quantum Chemistry, Statistical Mechanics, and Artificial Intelligence for Computational Spectroscopy: The UV-Vis Spectrum of TEMPO Radical in Different Solvents.Journal of chemical theory and computation · 2022Article
- MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations.Chemical science · 2022Article
- Molecular Dynamics Simulations Enforcing Nonperiodic Boundary Conditions: New Developments and Application to the Solvent Shifts of Nitroxide Magnetic Parameters.Journal of chemical theory and computation · 2022Article
- Unlocking the computational design of metal-organic cages.Chemical communications (Cambridge, England) · 2022Review
- A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors.Proteins · 2022Article
- Modeling Kinetics and Thermodynamics of Guest Encapsulation into the [MJournal of chemical information and modeling · 2021Article
- A Perspective on Modelling Metallic Magnetic Nanoparticles in Biomedicine: From Monometals to Nanoalloys and Ligand-Protected Particles.Materials (Basel, Switzerland) · 2021Review
- Coarse-Grained Modeling and Molecular Dynamics Simulations of CaFrontiers in molecular biosciences · 2021Article
- Computational Spectroscopy in Solution by Integration of Variational and Perturbative Approaches on Top of Clusterized Molecular Dynamics.Journal of chemical theory and computation · 2020Article
- Upgraded AMBER Force Field for Zinc-Binding Residues and Ligands for Predicting Structural Properties and Binding Affinities in Zinc-Proteins.ACS omega · 2020Article
- Article
- Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins.The Journal of membrane biology · 2019Review
- The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.Chemical science · 2018Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A novel statistical procedure has been developed to optimize the parameters of nonbonded force fields of metal ions in soft matter. The criterion for the optimization is the minimization of the deviations from ab initio forces and energies calculated for model systems. The method exploits the combination of the linear ridge regression and the cross-validation techniques with the differential evolution algorithm. Wide freedom in the choice of the functional form of the force fields is allowed since both linear and nonlinear parameters can be optimized. In order to maximize the information content of the data employed in the fitting procedure, the composition of the training set is entrusted to a combinatorial optimization algorithm which maximizes the dissimilarity of the included instances. The methodology has been validated using the force field parametrization of five metal ions (Zn
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