CHEM.5.B

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The student is expected to predict the properties of element in chemical families, including alkali metals, alkaline earth metals, halogens, noble gases, and transition metals, based on valence electron patterns using the Periodic Table; and
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Knowledge and Skills Statement

Science concepts. The student understands the development of the Periodic Table and applies its predictive power.

Some major chemical properties include flammability, toxicity, heat of combustion, pH value, rate of radioactive decay, and chemical stability.

any of the six chemical elements that make up Group 1 (Ia) of the periodic table comprising lithium (Li), sodium (Na), potassium (K), rubidium (Rb), cesium (Cs), and francium (Fr)

any of the divalent strongly basic metals of Group 2 of the periodic table comprising beryllium (Be), magnesium (mg), calcium (Ca), strontium (Sr), barium (Ba), and radium (Ra)

a pure substance that is made up of one type of atom

any of the chemical elements fluorine (F), chlorine (Cl), bromine (BA), iodine (I), and astatine (At)

any of a group of rare gases that include helium (He), neon (Ne), argon (Ar), krypton (Kr), xenon (Xe), and usually radon (Rn); exhibit great stability and extremely low reaction rates

an organizational chart of all the known elements on Earth, organized based on the physical and chemical properties of each

any of various metallic elements such as chromium (Cr), iron (Fe), and nickel (Ni) that have valence electrons in two shells instead of only one

a number of electrons (between one and eight) in the outer shell of an atom that are responsible for the chemical properties of the atom

Research

"Onwuli, Anthony,  Ashish V. Hegde, Kevin V. T. Nguyen, Keith T. Butler, and Aron Walsh. "Element Similarity in High-Dimensional Materials Representations." Digital Discovery 2, no. 5 (2023): 1558-1564.doi:10.1039/D3DD00121K. "

Summary:

The atomic number alone is insufficient for training statistical machine learning models to describe and extract composition-structure-property relationships of the elements. Here, we assess the similarity and correlations contained within high-dimensional local and distributed representations of the chemical elements. These include known physical properties, crystal structure analysis, natural language processing, and deep learning models.