Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

27.1K
An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
27.1K
Standard Electrode Potentials03:02

Standard Electrode Potentials

51.7K
On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
51.7K
The Electrical Double Layer01:30

The Electrical Double Layer

106
In the region where two bulk phases meet, an intricate electric charge distribution arises due to charge transfer, ion adsorption, molecular orientation, and charge distortion. This complex distribution is commonly referred to as the electrical double layer.When a solid electrode interfaces with ions in an electrolyte solution, the speed of electron transfer dictates the rates of oxidation and reduction. The electrode acquires a charge through the escape of atoms into the solution as cations or...
106
Theory of Strong Electrolytes01:23

Theory of Strong Electrolytes

55
The interionic forces of the strong electrolytes depend on the solvent's dielectric constant, which is the ability of a solvent to store electrical energy, based on its polarizability. and the solution's concentration. In high-dielectric solvents and in dilute solutions, weak electrostatic forces keep ions apart. However, in low-dielectric solvents or concentrated solutions, stronger interionic forces may cause ions to pair up as ionic doublets despite being fully ionized. The theory of strong...
55
π Electron Effects on Chemical Shift: Overview01:27

π Electron Effects on Chemical Shift: Overview

1.9K
An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0,...
1.9K
Thermodynamic Potentials01:26

Thermodynamic Potentials

1.7K
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
1.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Medium-Range Structural Order in Amorphous Arsenic.

Journal of the American Chemical Society·2026
Same author

The Zintl-Klemm Concept in the Amorphous State: A Case Study of Na-P Battery Anodes.

Angewandte Chemie (International ed. in English)·2025
Same author

A foundation model for atomistic materials chemistry.

The Journal of chemical physics·2025
Same author

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.

Digital discovery·2025
Same author

Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential.

Nature communications·2025
Same author

An automated framework for exploring and learning potential-energy surfaces.

Nature communications·2025
Same journal

Resolving Local and Global Conformational Heterogeneity of the Human Intrinsically Disordered Proteome.

Journal of chemical theory and computation·2026
Same journal

Molecular Modeling of Surfactant Interaction on Phospholipid Bilayers Mimicking Corneal Epithelium.

Journal of chemical theory and computation·2026
Same journal

PSFF-PTM: A Coarse-Grained Force-Field Parameter Patch for Modeling Post-Translational Modification Effects on Biomolecular Condensates.

Journal of chemical theory and computation·2026
Same journal

Low-Scaling Many-Body Green's Function Calculations for Molecular Systems via Interacting-Bath Dynamical Embedding Theory.

Journal of chemical theory and computation·2026
Same journal

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding.

Journal of chemical theory and computation·2026
Same journal

Reinventing Density Functional Theory with Machine Learning on Integral Features.

Journal of chemical theory and computation·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

759

Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials.

Natascia L Fragapane1, Volker L Deringer1

  • 1Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3QR, United Kingdom.

Journal of Chemical Theory and Computation
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

A new benchmark dataset, LiPS-25, and testing suite for machine-learned interatomic potentials (MLIPs) in solid-state electrolytes are introduced. This enables robust, automated evaluation of MLIPs for materials simulations.

More Related Videos

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
08:54

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Published on: January 25, 2020

6.1K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.8K

Related Experiment Videos

Last Updated: Mar 19, 2026

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

759
Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
08:54

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Published on: January 25, 2020

6.1K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.8K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Machine-learned interatomic potentials (MLIPs) are increasingly used in materials simulations.
  • Existing benchmarking methods for MLIPs lack robustness, automation, and chemical insight.
  • Standardized evaluation is crucial for advancing MLIP development and application.

Purpose of the Study:

  • To introduce LiPS-25, a curated benchmark dataset for Li-P-S solid-state electrolytes.
  • To present a comprehensive suite of performance tests for MLIPs.
  • To facilitate systematic numerical experiments for assessing MLIP performance and fine-tuning.

Main Methods:

  • Development of the LiPS-25 dataset, including crystalline and amorphous configurations.
  • Design of performance tests encompassing numerical error metrics and physically motivated tasks.
  • Application of the dataset and tests to graph-based MLIP architectures for numerical experiments.

Main Results:

  • Demonstration of systematic assessment of hyperparameter effects on MLIP task-level performance.
  • Analysis of fine-tuning behavior for pretrained MLIP models using the benchmark.
  • Validation of the benchmark's utility for evaluating MLIPs in Li-P-S electrolytes.

Conclusions:

  • The LiPS-25 benchmark provides a robust framework for evaluating MLIPs in solid-state electrolytes.
  • The methodology and code are adaptable to other material systems, promoting broader MLIP development.
  • This work advances automated, chemically informed benchmarking for materials simulations.