Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Here, we present a protocol for the behavioral analysis of a project-based learning methodology for health sciences students (20-56 years old). The protocol facilitates the comparison of the participants' performance in e-Learning versus blended-Learning (b-Learning) through a monitoring tool. The results are analyzed using Educational Data Mining and qualitative...

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic...

The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders

The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders

The Evoked Potential Operant Conditioning System aids scientific investigation of sensorimotor function and can administer targeted neurobehavioral training that can impact sensorimotor rehabilitation in neuromuscular disorders. This article describes its capabilities and illustrates its application in modifying a simple spinal reflex to achieve lasting improvement in motor...

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...

You might also read

Related Articles

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

Sort by

Same author

Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials.

Journal of chemical theory and computation·2026

Same author

Bottom-up synthesis of molecular nanodiamond from nanographene.

Nature·2026

Same author

Accelerated Reaction Exploration across Scales: A Hybrid Operando and Modeling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide.

Journal of the American Chemical Society·2026

Same author

The role of neutrophils in the pathogenesis of abdominal aortic aneurysms.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026

Same author

Limitations of Cluster-Trained MLIPs for Liquid Density and Diffusivity.

Journal of chemical theory and computation·2026

Same author

Nox1-Derived ROS Amplifies Calcium Entry and Enhances Pneumolysin-Induced Lung Endothelial Barrier Dysfunction in Hyperglycemia.

Antioxidants (Basel, Switzerland)·2026

Same journal

Amorphous High-Entropy Oxides With High-Valent Metal and Oxygen-Vacancy Pairs for Thermally Stable Catalytic Oxidation.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

H<sub>2</sub>S Self-Supplied Micelles Reverse Tumor-Immune Effector Cells Energy Metabolisms to Boost Breast Cancer Immunotherapy With Microenvironment Normalization.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

Feed-Draw Printing Enables Monolithically Integrated Flexible Sensors With High Interfacial Toughness and Wide Linear Range.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

Space-Time Coding Conformal Metasurfaces for Multifrequency Beam Steering and Shaping.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

3D Printing of Magnetic Soft Materials for Functional Structures and Devices.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

Photothermal-Activable Artificial Macrophage With Amplified Systemic Antibacterial Responses to Combat Primary and Secondary Infection.

Advanced materials (Deerfield Beach, Fla.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 20, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.

Volker L Deringer^1,2, Miguel A Caro³, Gábor Csányi¹

¹Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK.

Advanced Materials (Deerfield Beach, Fla.)

|September 6, 2019

Summary

This summary is machine-generated.

Machine learning (ML) accelerates materials modeling by creating faster, accurate interatomic potentials. This enables advanced atomistic simulations for materials science applications.

Keywords:

amorphous solids atomistic modeling big data force fields molecular dynamics

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Related Experiment Videos

Last Updated: Jan 20, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Materials Science
Computational Materials Science
Machine Learning in Materials

Background:

Atomic-scale materials modeling is crucial but computationally expensive.
Explicit electronic-structure methods like density-functional theory (DFT) present significant computational barriers.

Purpose of the Study:

To demonstrate how machine learning (ML) enhances materials modeling realism and speed.
To introduce ML-based interatomic potentials as a powerful tool for atomistic simulations.

Main Methods:

Utilizing machine learning to learn from electronic-structure data.
Developing ML-based interatomic potentials for faster simulations.
Applying these potentials to specific materials science problems.

Main Results:

ML-based potentials achieve accuracy comparable to DFT but are orders of magnitude faster.
Enabled high-fidelity atomistic simulations previously unfeasible.
Demonstrated applications in phase-change materials, nanoparticle catalysts, and carbon-based electrodes.

Conclusions:

ML-based interatomic potentials offer a transformative approach to materials modeling.
Accelerated simulations open new avenues for research in diverse materials applications.
Encourages wider adoption of ML potentials in materials research for enhanced discovery.