Predictive modeling of physical properties in silane compounds using topological descriptors: A computational approach

Abstract Silane compounds are a class of chemical compounds composed of silicon (Si) and hydrogen (H), characterized by the general formula <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi mathvariant="normal">SiH</m:mi> </m:mrow> <m:mrow> <m:mn>4</m:mn> <m:mo>−</m:mo> <m:mi>x</m:mi> </m:mrow> </m:msub> <m:msub> <m:mrow> <m:mi>R</m:mi> </m:mrow> <m:mrow> <m:mi>x</m:mi> </m:mrow> </m:msub> </m:math> {{\rm{SiH}}}_{4-x}{R}_{x} , where <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>R</m:mi> </m:math> R represents various organic groups. The simplest member of this family is silane itself, with the chemical formula <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi mathvariant="normal">SiH</m:mi> </m:mrow> <m:mrow> <m:mn>4</m:mn> </m:mrow> </m:msub> </m:math> {{\rm{SiH}}}_{4} . Silanes have structures similar to alkane in hydrocarbon, where the silicon atoms replace the carbon atoms. These compounds are of significant interest in various scientific and industrial applications due to their unique physical and chemical properties. In this work, we estimate the physical properties of silane compounds by utilizing linear, quadratic, and logarithmic regression models. 12 silane compounds were taken into consideration, and six degree-based topological descriptors, namely, reciprocal Randić index, sum-connectivity index, harmonic index, forgotten index, augmented Zagreb index, and inverse sum indeg index were used to estimate the following six important physical properties: density, polar surface area, molecular weight (MW), boiling point, flash point, and refractive index. The statistical parameters, including correlation coefficient, F -value, and P -value, were calculated for each property. It was observed that the MW of these silane compounds can be estimated with particularly high accuracy. This work contributes to the understanding of silane chemistry by leveraging computational modeling to predict their physical behavior, offering valuable insights into their applications in soft matter and materials science, particularly in interdisciplinary studies involving chemistry, physics, and engineering.