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

The Atomic Theory of Matter

The Atomic Theory of Matter

The earliest recorded discussion of the basic structure of matter comes from ancient Greek philosophers. Leucippus and Democritus argued that all matter was composed of small, finite particles that they called atomos, meaning “indivisible.” Later, Aristotle and others came to the conclusion that matter consisted of various combinations of the four “elements” — fire, earth, air, and water — and could be infinitely divided. Interestingly, these philosophers...

Scientific Laws and Theories

Scientific Laws and Theories

Scientific Laws

Attribution Theory

Attribution Theory

Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).

Shape and Texture of Coarse Aggregate

Shape and Texture of Coarse Aggregate

Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...

Sternberg's Triangular Theory of Love

Sternberg's Triangular Theory of Love

We typically love the people with whom we form relationships, but the type of love we have for our family, friends, and lovers differs. Robert Sternberg (1986) proposed that there are three components of love: intimacy, passion, and commitment. These three components form a triangle that defines multiple types of love: this is known as Sternberg’s triangular theory of love. Intimacy is the sharing of details and intimate thoughts and emotions. Passion is the physical attraction—the...

Social Exchange Theory

Social Exchange Theory

We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).

You might also read

Related Articles

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

Sort by

Same author

Ion-Pairing-Mediated Selective Transport of Rare Earth Elements through Functionalized Graphene Nanopores.

The journal of physical chemistry letters·2026

Same author

Ambient stability and surface adhesion of 2D polyaramid nanofilms.

Faraday discussions·2026

Same author

Tunable Gas-Liquid Separation by Surface Charge Modifications: Toward Membrane-Based Carbon Capture and Detection.

Nano letters·2026

Same author

Knowledge gaps for neuromorphic ionic computing.

Science (New York, N.Y.)·2026

Same author

Nonlinear ion transport in a 2D Janus membrane with an angstrom pore: memristive and negative differential resistance phenomena.

Faraday discussions·2026

Same author

Evolution of Electronic Properties of Graphene Nanoribbons with Progressive Carving: From Straight to Porous to Chevron Ribbons.

ACS nano·2026

Search research articles

Related Experiment Video

Updated: Jan 28, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Transfer-Learning-Based Coarse-Graining Method for Simple Fluids: Toward Deep Inverse Liquid-State Theory.

Alireza Moradzadeh¹, Narayana R Aluru¹

¹Department of Mechanical Science and Engineering, Beckman Institute for Advanced Science and Technology , University of Illinois at Urbana-Champaign , Urbana , Illinois 61801 , United States.

The Journal of Physical Chemistry Letters

|March 2, 2019

Summary

This summary is machine-generated.

Deep neural networks (DNNs) successfully predict Lennard-Jones (LJ) potential parameters using big data from molecular dynamics (MD) simulations. This machine learning approach addresses challenges in force-field development for atomic and coarse-grained models.

More Related Videos

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Related Experiment Videos

Last Updated: Jan 28, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Computational chemistry
Statistical mechanics
Machine learning

Background:

Developing accurate force-field parameters is crucial for molecular simulations but often challenging.
The inverse problem of determining potential parameters from observable properties like the radial distribution function (RDF) is complex.

Purpose of the Study:

To utilize deep neural networks (DNNs) to solve the inverse problem in liquid-state theory.
To establish a relationship between the radial distribution function (RDF) and Lennard-Jones (LJ) potential parameters.

Main Methods:

A deep neural network (DNN) framework was developed.
The DNN was trained on a large dataset of 1.5 TB from 26,000 distinct molecular dynamics (MD) systems with 52 μs cumulative simulation time.

Main Results:

The DNN successfully predicted Lennard-Jones (LJ) potential parameters for atomic liquids.
The DNN also demonstrated effectiveness in parametrizing LJ potentials for coarse-grained models of multiatom molecules.

Conclusions:

Deep neural networks offer an effective machine learning paradigm for force-field parameterization.
This approach can circumvent traditional difficulties in developing and optimizing interatomic potentials for diverse molecular systems.