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Ions

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Transfer learning based generalized framework for state of health estimation of Li-ion cells.

Scientific reports
Estimating the state of health (SOH) of batteries powering electronic devices in real-time while in use is a necessity. The applicability of most of the existing methods is limited to the datasets that are used to train the models. In this work, we p...

An open-source deep learning model for predicting effluent concentration in capacitive deionization.

The Science of the total environment
To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technology, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein...

AlphaFill: enriching AlphaFold models with ligands and cofactors.

Nature methods
Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecul...

Regimes of ion dynamics in current sheets: The machine learning approach.

Physical review. E
Current sheets are spatially localized almost-one-dimensional (1D) structures with intense plasma currents. They play a key role in storing the magnetic field energy and they separate different plasma populations in planetary magnetospheres, the sola...

Dip-coating electromechanically active polymer actuators with SIBS from midblock-selective solvents to achieve full encapsulation for biomedical applications.

Scientific reports
Soft and compliant ionic electromechanically active polymer actuators (IEAPs) are a promising class of smart materials for biomedical and soft robotics applications. These materials change their shape in response to external stimuli like the electric...

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation.

Journal of chemical information and modeling
Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchm...

A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.

Bioinformatics (Oxford, England)
MOTIVATION: Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in...

Identification of metal ion-binding sites in RNA structures using deep learning method.

Briefings in bioinformatics
Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA struct...

Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.

Nature communications
Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions,...

Ionmob: a Python package for prediction of peptide collisional cross-section values.

Bioinformatics (Oxford, England)
MOTIVATION: Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section...