首页|Data from University of Arkansas Little Rock Advance Knowledge in Artificial Int elligence (Artificial Intelligence Modeling of Materials’ Bulk Chemical and Phys ical Properties)
Data from University of Arkansas Little Rock Advance Knowledge in Artificial Int elligence (Artificial Intelligence Modeling of Materials’ Bulk Chemical and Phys ical Properties)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from Little Rock, Arkansas, by NewsRx correspondents, research stated, “Energies of the atomic and molecula r orbitals belonging to one and two atom systems from the fourth and fifth perio ds of the periodic table have been calculated using ab initio quantum mechanical calculations.” The news journalists obtained a quote from the research from University of Arkan sas Little Rock: “The energies of selected occupied and unoccupied orbitals surr ounding the highest occupied and lowest unoccupied orbitals (HOMOs and LUMOs) of each system were selected and used as input for our artificial intelligence (AI ) software. Using the AI software, correlations between orbital parameters and s elected chemical and physical properties of bulk materials composed of these ele ments were established. Using these correlations, the materials’ bulk properties were predicted. The Q2 correlation for the single-atom predictions of first ion ization potential, melting point, and boiling point were 0.3589, 0.4599, and 0.1 798 respectively. The corresponding Q2 correlations using orbital parameters des cribing two-atom systems increased the capability to predict the experimental pr operties to the respective values of 0.8551, 0.8207, and 0.7877.”
University of Arkansas Little RockLitt le RockArkansasUnited StatesNorth and Central AmericaArtificial Intellig enceEmerging TechnologiesMachine LearningSoftware
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