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Found 7 papers, 2 papers with code
Dynamical properties across different coarse-grained models for ionic liquids
We systematically compare five CG models: a model largely parametrized from experimental thermodynamic observables; a refinement of this model to increase its structural accuracy; and three models that reproduce a given set of structural distribution functions by construction, with varying intramolecular parametrizations and reference temperatures.
Soft Condensed Matter
Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability
Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging.
Chemical Physics Computational Physics
Data-driven equation for drug-membrane permeability across drugs and membranes
Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity.
Chemical Physics Soft Condensed Matter
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at odds with the multi-basin free-energy landscapes that typically arise from the identification of meaningful collective variables.
Resolution limit of data-driven coarse-grained models spanning chemical space
Increasing the efficiency of materials design and discovery remains a significant challenge, especially given the prohibitively large size of chemical compound space.
Chemical Physics Soft Condensed Matter
Accurate structure-based coarse-graining leads to consistent barrier-crossing dynamics
Structure-based coarse graining of molecular systems offers a systematic route to reproduce the many-body potential of mean force.
Soft Condensed Matter Chemical Physics Computational Physics
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters---optimized once and for all across compounds.
Chemical Physics
