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

Physical and Chemical Properties of Matter02:57

Physical and Chemical Properties of Matter

165.4K
The characteristics that enable us to distinguish one substance from another are called properties.
165.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

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

7.4K
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...
7.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data09:34

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

4.5K
This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic...
4.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

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

2.3K
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.
2.3K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

940
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...
940
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

56.6K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
56.6K

You might also read

Related Articles

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

Sort by
Same author

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package.

The Journal of chemical physics·2021
Same author

Recent developments in the PySCF program package.

The Journal of chemical physics·2020
Same author

Metadynamics for training neural network model chemistries: A competitive assessment.

The Journal of chemical physics·2018
See all related articles

Related Experiment Video

Updated: Jan 20, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.4K

Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models

John E Herr1, Kevin Koh1, Kun Yao1

  • 1Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA.

The Journal of Chemical Physics
|September 1, 2019
PubMed
Summary

We developed elemental modes, a new feature vector for atomic identity, improving machine learning accuracy for material property prediction. This method enhances neural network potentials, enabling broader elemental applications and alchemical calculations.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

4.5K
Physical and Chemical Properties of Matter
02:57

Physical and Chemical Properties of Matter

165.4K

Related Experiment Videos

Last Updated: Jan 20, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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

Published on: September 25, 2021

4.5K
Physical and Chemical Properties of Matter
02:57

Physical and Chemical Properties of Matter

165.4K

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Accurate prediction of material properties is crucial for discovering new materials.
  • Current machine learning models for materials often face limitations in scalability and the number of elements they can handle.
  • Developing efficient feature representations for atomic species is key to advancing materials modeling.

Purpose of the Study:

  • To create a novel, compressed vector representation of atomic species identity.
  • To improve the accuracy and scalability of machine learning models for predicting material properties, specifically formation energies.
  • To enable the application of high-dimensional neural network potentials (HD-NNPs) to a wider range of elements and complex chemical processes.

Main Methods:

  • Utilized an autoencoder to compress physical properties into a vector quantity representing atomic identity, termed elemental modes.
  • Trained neural networks using elemental modes as feature vectors to predict formation energies of elpasolite compounds.
  • Integrated elemental modes with geometric features for HD-NNPs, extending their capability to numerous atomic species and alchemical intermediate states.

Main Results:

  • Elemental modes significantly improved the accuracy of predicting elpasolite formation energies compared to previous methods.
  • The new approach overcomes the scaling limitations of traditional HD-NNPs, allowing for models with up to 11 atomic species (H, C, N, O, F, P, S, Cl, Se, Br, I).
  • Demonstrated the ability to define feature vectors for alchemical intermediate states, facilitating free energy calculations in systems with bond breaking/forming.

Conclusions:

  • Elemental modes offer a powerful and efficient feature representation for atomic species in machine learning.
  • This advancement expands the applicability of HD-NNPs to a larger chemical space and complex reaction pathways.
  • The method opens new avenues for alchemical free energy calculations, crucial for understanding chemical transformations.