Work

Reparametrizing Joback's Group Additivity Theory – Thermochemical Building Blocks for Targeted Design of New Compounds

Year: 2024

Type: article

Abstract: This study aims to investigate if complex machine‐learning models are required to predict certain properties of materials, or if simple linear regression models also give sufficiently accurate predictions of thermochemical properties and density. An in‐depth analysis of Joback's method in combination with statistical models, and the density predictions by Holden, Keshavarz, and Bondarchuk are made. Updated Group increment tables for Joback's method are provided. The ‘Parametrization of Empirical Approximations using Statistical Methods’ (PEAS) is intended to evaluate if it could be better to resort to simpler methods in certain cases. Additionally, a data set analysis tool named “Group Extraction Analysis Tool for Deviations in approximating numerical modeling errors” (Great Dane©) is presented.

Source: Propellants Explosives Pyrotechnics

DOI: 10.1002/prep.202400060

Author: Sabrina Wahler

Institutions: Imperial College London, Stanford University

Language: English

Topic: Chemical Thermodynamics and Molecular Structure

Subfield: Organic Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,660

Funders: Imperial College London, Bundesministerium für Bildung und Forschung