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

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

501
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
501
Cattell's Theory of Intelligence01:25

Cattell's Theory of Intelligence

6.6K
Raymond Cattell, along with John Horn, made significant contributions to our understanding of intelligence by distinguishing between two types: fluid intelligence and crystallized intelligence.
Fluid intelligence involves the capacity to solve new problems and adapt to unfamiliar situations. It's the type of intelligence individuals use when they encounter a novel problem or puzzle that requires innovative thinking. For instance, figuring out how to operate a new gadget relies heavily on...
6.6K
Machines: Problem Solving II01:30

Machines: Problem Solving II

314
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
314
Machines: Problem Solving I01:22

Machines: Problem Solving I

333
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
333
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

170
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
170
Multiple Intelligences Theory01:20

Multiple Intelligences Theory

7.6K
Howard Gardner's theory of Multiple Intelligence proposes that there are nine distinct types of intelligence, each reflecting different ways of interacting with the world. Introduced in 1983 and expanded in subsequent years, Gardner's framework challenges the traditional notion of a single, generalized intelligence.
7.6K

You might also read

Related Articles

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

Sort by
Same author

From Gibbs-Shannon entropy and microscopic reversibility to entropy production, heat, and fluctuation theorems.

The Journal of chemical physics·2026
Same author

Questionnaire on efficacy of the competency-oriented integrated residency and fellowship training for ophthalmologists in Shanghai.

Frontiers in medicine·2026
Same author

Ising Density Functional Theory for Inhomogeneous Weak Polyelectrolytes.

The journal of physical chemistry. B·2026
Same author

Synergizing Chemical and AI Communities for Advancing Laboratories of the Future.

ACS central science·2026
Same author

Extracellular matrix remodeling and endothelial fibrosis in Sturge-Weber syndrome secondary glaucoma: Insights from aqueous humor proteomics.

Experimental eye research·2025
Same author

Neural operators for forward and inverse potential-density mappings in classical density functional theory.

The Journal of chemical physics·2025
Same journal

Linking Local Water Electrostatic Potentials to Measured Hydrogen Evolution Onset in Aqueous Electrolytes.

The journal of physical chemistry letters·2026
Same journal

Microsolvation Redirects Electron-Induced Chemistry in Nucleobases.

The journal of physical chemistry letters·2026
Same journal

Interfacial Microenvironment Effects on the Mechanism of Photocatalytic Methanol Conversion for Hydrogen Evolution.

The journal of physical chemistry letters·2026
Same journal

Noncovalent Interactions in Protein-Ti Binding: Titan Bonds at Work.

The journal of physical chemistry letters·2026
Same journal

Partial Phase Remixing of Segregated Mixed Halide Perovskite Nanocrystals Induced by an Instant Change in an External Electric Field.

The journal of physical chemistry letters·2026
Same journal

Pressure-Driven Dissociation of a Kr Clathrate in the Presence of Colloids.

The journal of physical chemistry letters·2026
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

One Dimensional Turing-Like Handshake Test for Motor Intelligence
14:05

One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

26.8K

Perfecting Liquid-State Theories with Machine Intelligence.

Jianzhong Wu1, Mengyang Gu2

  • 1Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, United States.

The Journal of Physical Chemistry Letters
|November 17, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning enhances liquid-state theories for predicting material properties. Future developments promise greater accuracy and efficiency in computational chemistry and materials science.

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

584
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

596

Related Experiment Videos

Last Updated: Jul 11, 2025

One Dimensional Turing-Like Handshake Test for Motor Intelligence
14:05

One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

26.8K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

584
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

596

Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Machine intelligence is increasingly used for predicting electronic structure, molecular force fields, and physicochemical properties in condensed systems.
  • Challenges persist in developing comprehensive frameworks for diverse atomic compositions and thermodynamic conditions.

Purpose of the Study:

  • To discuss future developments in liquid-state theories using functional machine learning.
  • To explore how machine learning can improve the accuracy, scalability, and efficiency of liquid-state theories.

Main Methods:

  • Leveraging advancements in functional machine learning.
  • Integrating theoretical analysis with machine learning techniques such as surrogate modeling, dimension reduction, and uncertainty quantification.

Main Results:

  • Anticipated significant improvements in the accuracy of liquid-state theories.
  • Expected enhancements in the scalability and computational efficiency of these theories.

Conclusions:

  • Machine learning integration is poised to revolutionize liquid-state theories.
  • This advancement will enable broader applications across diverse materials and chemical systems.