No abstract available

No abstract available

No abstract available

[en] Assuming random mixing of atoms, design of high entropy alloys (HEAs) was used to follow a simple route by maximizing their configurational entropy of mixing. Here we propose a single-parameter design paradigm taking into account formation enthalpy and the excessive entropy of mixing, which arises from dense atomic packing and atomic size misfit. The proposed paradigm is verified using the data hitherto reported and proven to be a physically accepted thermodynamic parameter for the design of HEAs

[en] The correlation has been established between the quantity of 5f-electrons contained in electron shells of M³⁺ and M²⁺ ions and redox-potentials of the pairs M(4)-M(3) and M(3)-M(2) of actinides (E₄⁰ and E₂⁰, respectively). Standard redox potentials of these pairs of actinides are related linearly to the effective quantity of 5f-electrons (nsub(eff)xnsub(eff)=nsub(f)+Δ, where nsub(f) is the quantity of 5f-electrons in shells of the M(3) and M(2) ions; Δ is an integral correction identical for the ions with the same orbital quantum number of L of the main state. The calculated values of E₄⁰ are: for Th - 3.41, Pa - 1.96, Am+2.31, Cm+3.12, Cf+3.12, Es+4.57, Fm+5.3, Md+6.02, No+6.75, Lr+8.92 V; E₂⁰ for Pu - 3.28, Am - 2.75, Cm - 3.8, Bk-2.75, Cf -1.7, Es -1.18, Fm -0.65 V. Standard enthalpy values of the formation of the following ions have been calculated: M⁴⁺(Pa, Cm, Md-Lr), M³⁺(Th, Pa,Md-Lr) as well as standard isobaric potentials of the formation of M⁴⁺(Pa,Cm,Cf), M³⁺(Th, Pa, Cf), and M²⁺(Pu-Cf)

[en] G3(MP2)//B3 theory was modified to incorporate compact effective potential (CEP) pseudopotentials, providing a theoretical alternative referred to as G3(MP2)//B3-CEP for calculations involving first-, second-, and third-row representative elements. The G3/05 test set was used as a standard to evaluate the accuracy of the calculated properties. G3(MP2)//B3-CEP theory was applied to the study of 247 standard enthalpies of formation, 104 ionization energies, 63 electron affinities, 10 proton affinities, and 22 atomization energies, comprising 446 experimental energies. The mean absolute deviations compared with the experimental data for all thermochemical results presented an accuracy of 1.4 kcal mol⁻¹ for G3(MP2)//B3 and 1.6 kcal mol⁻¹ for G3(MP2)//B3-CEP. Approximately 75% and 70% of the calculated properties are found with accuracy between ±2 kcal mol⁻¹ for G3(MP2)//B3 and G3(MP2)//B3-CEP, respectively. Considering a confidence interval of 95%, the results may oscillate between ±4.2 kcal mol⁻¹ and ±4.6 kcal mol⁻¹, respectively. The overall statistical behavior indicates that the calculations using pseudopotential present similar behavior with the all-electron theory. Of equal importance to the accuracy is the CPU time, which was reduced by between 10% and 40%

[en] Highlights: • ΔH°_f is predicted from the molecular structure of the compounds alone. • ANN-SGC model predicts ΔH°_f with a correlation coefficient of 0.99. • ANN-MNLR model predicts ΔH°_f with a correlation coefficient of 0.90. • Better definition of the atom-type molecular groups is presented. • The method is better than others in terms of combined simplicity, accuracy and generality. - Abstract: A theoretical method for predicting the standard enthalpy of formation of pure compounds from various chemical families is presented. Back propagation artificial neural networks were used to investigate several structural group contribution (SGC) methods available in literature. The networks were used to probe the structural groups that have significant contribution to the overall enthalpy of formation property of pure compounds and arrive at the set of groups that can best represent the enthalpy of formation for about 584 substances. The 51 atom-type structural groups listed provide better definitions of group contributions than others in the literature. The proposed method can predict the standard enthalpy of formation of pure compounds with an AAD of 11.38 kJ/mol and a correlation coefficient of 0.9934 from only their molecular structure. The results are further compared with those of the traditional SGC method based on MNLR as well as other methods in the literature

[en] Standard heats of formation (ΔH₂₉₈⁰) of theMeO.nH₂0 hydroxides were computed from the formula ΔH₂₉₈⁰ = ΔHsub(298)sup(0(Meo) 71.n. Heats of formation of the Me(OH)₂ and Me(OH)₃ hydroxides were estimated, where Me is the iron group transition metal. Satisfactory agreement of the experimenta and theoretical data enabled to compute the values ΔH₂₉₈⁰ of Co₂O₃ and Ni₂O₃ from the heats of formation of Co(OH)₃ and Ni(OH)₃. Using the values ΔHsub(298)sup(0(Me₂O₃)) heats of formation of the MeOOH hydroxides are computed. The values ΔH₂₉₈⁰ for FeOOH and β=NiOOH agree with experimental data available

[en] Highlights: ► Predictive models were developed for standard heats of formation. ► Models consisted of quantitative structure–property relationship models. ► Database comprised of 1765 compounds involving 82 chemical classes. ► Predictions for the linear model were 138 kJ/mol root-mean-square error (RMSE). ► Predictions for the non-linear model were 97 kJ/mol RMSE. - Abstract: The standard heat of formation is a basic thermophysical property required in determining enthalpies of reaction and in thermodynamic stability analyses. Further, the enthalpies of formation are important in investigating bond energies, resonance energies and the nature of chemical bonds. Therefore, the development of accurate structure-based estimation methods for large varieties of chemical species is greatly beneficial in enhancing capability in process and product development. In this work, quantitative structure–property relationship (QSPR) models were developed for a structurally diverse DIPPR dataset of standard heats of formation comprising 1765 pure compounds involving 82 chemical classes. We have employed both linear and nonlinear QSPR modeling techniques. The linear approach involves the use of constricted binary particle swarm optimization (BPSO) for feature selection and multiple-linear regression. In the nonlinear approach, the optimum network architecture and its associated inputs are identified using a wrapper-based feature selection algorithm combining differential evolution and artificial neural networks. Model predictions for the root-mean-square error of the BPSO and nonlinear approaches were 138 and 97 kJ/mol, respectively.

[en] A mathematical model is developed for generalizing database information on the thermodynamic properties of pure substances. A way of estimating the values of thermodynamic functions (enthalpies of formation, entropies, and heat capacities) of chemical compounds from their structure is described. It is based on a linear regression that reflects the connection between a chosen set of compound characteristics and properties. A way of batch-processing of database information for preparing source samples is described. A way of obtaining model parameters is provided, along with the corresponding numerical values. Trial calculations are made that show good correlation with reference data.