High-Level Ab Initio Predictions of Thermochemical Properties of Organosilicon Species : Critical Evaluation of Experimental Data and a Reliable Benchmark Database for Extending Group Additivity Approaches
Vuori, H. T., Rautiainen, J. M., Kolehmainen, E. T., & Tuononen, H. M. (2022). High-Level Ab Initio Predictions of Thermochemical Properties of Organosilicon Species : Critical Evaluation of Experimental Data and a Reliable Benchmark Database for Extending Group Additivity Approaches. Journal of Physical Chemistry Part A, 126(10), 1729-1742. https://doi.org/10.1021/acs.jpca.1c09980
Published inJournal of Physical Chemistry Part A
DisciplineEpäorgaaninen kemiaEpäorgaaninen ja analyyttinen kemiaNanoscience CenterOrgaaninen kemiaInorganic ChemistryInorganic and Analytical ChemistryNanoscience CenterOrganic Chemistry
© 2022 The Authors. Published by American Chemical Society
A high-level composite quantum chemical method, W1X-1, is used herein to calculate the gas-phase standard enthalpy of formation, entropy, and heat capacity of 159 organosilicon compounds. The results set a new benchmark in the field that allows, for the first time, an in-depth assessment of existing experimental data on standard enthalpies of formation, enabling the identification of important trends and possible outliers. The calculated thermochemical data are used to determine Benson group additivity contributions for 60 Benson groups and group pairs involving silicon. These values allow fast and accurate estimation of thermochemical parameters of organosilicon compounds of varying complexity, and the data acquired are used to assess the reliability of experimental work of Voronkov et al. that has been repeatedly criticized by Becerra and Walsh. Recent results from other computational investigations in the field are also carefully discussed through the prism of reported advancements. ...
PublisherAmerican Chemical Society (ACS)
Publication in research information system
MetadataShow full item record
Related funder(s)Research Council of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingAcademy of Finland: grant number 324680
Showing items with similar title or keywords.
Computational thermochemistry : extension of Benson group additivity approach to organoboron compounds and reliable predictions of their thermochemical properties Vuori, Hannu T.; Rautiainen, J. Mikko; Kolehmainen, Erkki T.; Tuononen, Heikki M. (Royal Society of Chemistry (RSC), 2022)High-level computational data for standard gas phase enthalpies of formation, entropies, and heat capacities are reported for 116 compounds of boron. A comparison of the results with extant experimental and computational ...
Experimental Approaches for Testing if Tolerance Curves Are Useful for Predicting Fitness in Fluctuating Environments Ketola, Tarmo; Kristensen, Torsten N. (Frontiers Media S.A., 2017)Most experimental studies on adaptation to stressful environments are performed under conditions that are rather constant and rarely ecologically relevant. Fluctuations in natural environmental conditions are ubiquitous ...
Theoretical and computational studies of magnetic anisotropy and exchange coupling in molecular systems Mansikkamäki, Akseli (University of Jyväskylä, 2018)The ﬁeld of molecular magnetism studies the magnetic properties of molecular systems as opposed to conventional metal-based magnets. The high chemical modiﬁability of the constituting molecules makes such materials highly ...
Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions Hyttinen, Noora; Pihlajamäki, Antti; Häkkinen, Hannu (American Chemical Society (ACS), 2022)We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The ...
Varis, Tuomo; Tuovinen, Tero (DIME Università di Genova, 2012)Solving new increasingly complex problems requires development of new methods and tools but verification of their correctness and efficiency in absence of actual experimental data is difficult. In this paper we propose an ...