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Classifying Matter by State02:49

Classifying Matter by State

103.2K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
90.3K
Physical and Chemical Properties of Matter02:57

Physical and Chemical Properties of Matter

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The characteristics that enable us to distinguish one substance from another are called properties.
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The Atomic Theory of Matter02:59

The Atomic Theory of Matter

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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...
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What is Matter?01:13

What is Matter?

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The substance of the universe—from a grain of sand to a star—is called matter. Scientists define matter as anything that occupies space and has mass. An object’s mass and its weight are related concepts, but not quite the same. An object’s mass is the amount of matter contained in the object and is the same whether that object is on Earth or in the zero-gravity environment of outer space. An object’s weight, on the other hand, is its mass as affected by the pull of...
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States of Matter01:20

States of Matter

2.8K
Solids, liquids, and gases are the three states of matter commonly found on Earth. A solid is rigid and possesses a definite shape. A liquid flows and takes the shape of its container, except it forms a flat or slightly curved upper surface when acted upon by gravity. Both liquid and solid samples have volumes nearly independent of pressure. A gas takes both the shape and volume of its container.
Scientists have discovered a fourth state of matter, plasma, that occurs naturally in the interiors...
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Related Experiment Video

Updated: Jan 29, 2026

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
06:44

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding

Published on: March 24, 2018

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Feature Engineering for Materials Chemistry-Does Size Matter?

Roger D Amos1, Rika Kobayashi1,2

  • 1ANU Supercomputer Facility , Leonard Huxley Building 56, Mills Road , Canberra , ACT 2601 , Australia.

Journal of Chemical Information and Modeling
|February 8, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning models for predicting material band gaps showed that structural features did not significantly improve accuracy. The most impactful enhancement came from incorporating density functional theory (DFT) calculations for band gaps.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Accurate prediction of material band gaps is crucial for applications like light harvesting.
  • Machine learning models offer a promising avenue for accelerating materials discovery.
  • Incorporating structural information can potentially enhance predictive models.

Purpose of the Study:

  • To investigate the impact of structural featurizers on machine learning model performance for band gap prediction.
  • To evaluate the predictive accuracy of models incorporating elemental and structural properties.
  • To assess the transferability of developed models to independent datasets.

Main Methods:

  • Applied machine learning to a dataset of silver nanoparticles and 2254 light-harvesting materials.
  • Extended elemental properties with structural features derived from Voronoi polyhedra.
  • Included band gaps calculated using density functional theory (DFT) as a key feature.

Main Results:

  • Structural featurizers did not yield a significant improvement in predictive performance.
  • The inclusion of DFT-calculated band gaps led to the most substantial model enhancement.
  • The model accurately predicted band gaps for 2254 materials (RMSE: 0.232 eV, MAE: 0.142 eV).
  • The model demonstrated good transferability to 72 independent experimental band gaps (RMSE: 0.91 eV, MAE: 0.76 eV).

Conclusions:

  • Structural features alone do not significantly improve machine learning-based band gap prediction.
  • Density functional theory (DFT) calculations are highly valuable for enhancing band gap prediction models.
  • The developed machine learning model shows promise for predicting band gaps in novel light-harvesting materials.