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

Metallic Solids02:37

Metallic Solids

20.5K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
20.5K
Structures of Solids02:22

Structures of Solids

17.6K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
17.6K
Conditions on Early Earth02:06

Conditions on Early Earth

101.0K
Around 4 billion years ago, oceans began to condense on earth while volcanic eruptions released nitrogen, carbon dioxide, methane, ammonia, and hydrogen into the primordial atmosphere. However, organisms with the characteristics of life were not initially present on earth. Scientists have used experimentation to determine how organisms evolved that could grow, reproduce, and maintain an internal environment.
101.0K
Machines01:19

Machines

564
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
564
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.0K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
20.0K

You might also read

Related Articles

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

Sort by
Same author

Slow slip modulates low-frequency seismicity on the Parkfield segment of the San Andreas Fault.

Nature communications·2026
Same author

Preparatory phase of large earthquakes illuminated by unsupervised categorization of earthquake catalog features.

Nature communications·2026
Same author

Nonlinear mesoscopic elasticity revealed by passive seismic monitoring of a rock column during drilling operations.

The Journal of the Acoustical Society of America·2026
Same author

Enhancing the resolution of microseismicity through dense array monitoring in complex extensional settings.

Scientific reports·2026
Same author

A clearer view of the current phase of unrest at Campi Flegrei caldera.

Science (New York, N.Y.)·2025
Same author

The forearc seismic belt: A fluid pathway constraining down-dip megathrust earthquake rupture.

Science (New York, N.Y.)·2025
Same journal

A native sulfur deposit in Gale crater, Mars.

Science (New York, N.Y.)·2026
Same journal

Coordinated demise of harmful algal blooms.

Science (New York, N.Y.)·2026
Same journal

Genetic effects put into context.

Science (New York, N.Y.)·2026
Same journal

Bacteria share proteins to survive antibiotics.

Science (New York, N.Y.)·2026
Same journal

Impacts shaped Earth's first continents.

Science (New York, N.Y.)·2026
Same journal

Erratum for the Report "Covalently bonded single-molecule junctions with stable and reversible photoswitched conductivity" by C. Jia <i>et al</i>.

Science (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

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

Machine learning for data-driven discovery in solid Earth geoscience.

Karianne J Bergen1,2, Paul A Johnson3, Maarten V de Hoop4

  • 1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.

Science (New York, N.Y.)
|March 23, 2019
PubMed
Summary
This summary is machine-generated.

Solid Earth geosciences face challenges in understanding complex subsurface processes. Machine learning offers a promising approach to accelerate progress by analyzing increased data and improving computer simulations for Earth science discovery.

More Related Videos

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.1K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

955

Related Experiment Videos

Last Updated: Jan 27, 2026

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
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.1K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

955

Area of Science:

  • Solid Earth geosciences
  • Geophysics
  • Geodynamics

Background:

  • Understanding Earth's behavior is crucial but hindered by complex, multiscale processes.
  • Direct observation of the Earth's subsurface is severely limited.
  • Advancements in data availability and computational simulations offer new avenues for research.

Purpose of the Study:

  • To review the current state of machine learning applications in solid Earth geosciences.
  • To identify challenges and opportunities for accelerating scientific discovery.
  • To provide recommendations for broadening and advancing the field.

Main Methods:

  • Review of existing literature and case studies on machine learning in geosciences.
  • Analysis of the potential of data-driven approaches and advanced simulations.
  • Synthesis of current capabilities and future research directions.

Main Results:

  • Machine learning (ML) is poised to play a pivotal role in advancing Earth science.
  • Increased data and sophisticated simulations, when combined with ML, can enhance understanding of Earth's complex systems.
  • The integration of ML requires addressing specific challenges in data handling and model interpretability.

Conclusions:

  • Machine learning is essential for overcoming the inherent complexities and observational limitations in solid Earth geosciences.
  • Further development and application of ML techniques are recommended to accelerate progress.
  • Interdisciplinary collaboration and strategic investment are key to realizing the full potential of ML in Earth science.