Entropy computing

An asterisk is appended to the symbol AaSm as a reminder that it represents only the configurational entropy computed by considering the... 

The configurational entropy per occupied lattice site (i.e., per unit mass rather than per unit volume) is by definition a monotonic function of temperature, and, of course, the fluid entropy deduced from calorimetric measurements also has this monotonic property. Figure 5a compares the mass and site configurational entropies computed from the LCT as functions of temperature T for the F-F and... 

Batista, Godden and Bajorath have developed the MolBlaster method, in which molecular similarity is assessed by Differential Shannon Entropy computed from populations of randomly generated fragments. For the range 0.64 < T < 0.99, this similarity measure provides with the same ranking as the... 

Considering these equations it becomes clear how elaborate the analysis is, all terms, except x depend on T. However, as, in principle, y(T,x) is measurable, so is r " at each T, and hence, r " (T,x). As p,= p° + RTlnJ,x, differentiation gives -S + Rln/jX + RTdhif /dT. Only if all these terms are properly accounted for can S be obtained. This procedure requires extremely accurate data over wide ranges of T and x. Although surface entropy computations have been published in the literature the author is not aware of studies executed with such detailed scrutiny. Certainly much remains to be done. 

Table 3.2 presents a selection of the most used thermodynamic options for phase equilibrium with suitable enthalpy and entropy methods. The accuracy of both phase equilibrium and enthalpy/entropy computation must be examined when using EOS models. For example, often a cubic EOS underestimates the enthalpy of vaporisation. In this case other methods are more accurate, as those based on three-parameters corresponding states law (Lee-Kesler, Curl-Pitzer, etc.). Mixtures rich in components with particular behaviour, as or CH, need special methods for accurate simulation. When binary interaction parameters for liquid activity models are absent, the UNI FAC predictive method may be employed. It is worth to note that UNIFAC is suitable only for exploratory purposes, but not for final design. When high non-ideal mixtures are involved at higher pressure then the combination of EOS with liquid activity models is recommended (see Chapter 6). 

The improvement in AT-values does not automatically ensure accuracy of other properties. In this example the estimation of liquid volume is poor for SRK, but acceptable for PR. Without interaction coefficients the prediction of the liquid volume is even better Note that when the volumetric properties are important, as in reservoir engineering, special equation of state or mixing rules should be applied, as Teja-Sandler EOS given in Chapter 5. The same observation holds for the enthalpy of vaporisation, which could be in serious error. Another method for enthalpy/entropy computation should be used, as for example based on the principle of corresponding states. 

Entropies calculated in this manner were compared with absolute entropies computed backward from measurable equilibrium constants and were found to check quite closely. 

It must be pointed out that ASm is the combinatorial entropy computed by considering the possible arrangements of the molecules on the lattice in Figure 3.3. Furthermore the number of ways that the system can be rearranged in space is reduced when one or both of the species exist as long chains. In the equations above, the subscript 1 usually represents the solvent, and the subscript 2 the polymer. 

In the systematic approach, the contaminated signal was processed using transients with parameters selected from a uniformly sampled grid in the parameter space. For each parameter value, the quality of the processed signal was computed. An example result is presented in Figure 2 which shows the performance as a function of the two parameters and / p. The parameter values /, and which yielded the lowest entropy were selected for processing. 

The classical computer tomography (CT), including the medical one, has already been demonstrated its efficiency in many practical applications. At the same time, the request of the all-round survey of the object, which is usually unattainable, makes it important to find alternative approaches with less rigid restrictions to the number of projections and accessible views for observation. In the last time, it was understood that one effective way to withstand the extreme lack of data is to introduce a priori knowledge based upon classical inverse theory (including Maximum Entropy Method (MEM)) of the solution of ill-posed problems [1-6]. As shown in [6] for objects with binary structure, the necessary number of projections to get the quality of image restoration compared to that of CT using multistep reconstruction (MSR) method did not exceed seven and eould be reduced even further. 

Hoover W G and Ree F H 1967 Use of computer experiments to locate the melting transition and calculate the entropy in the solid phase J. Chem. Phys. 47 4873-8... 

What has been developed within the last 20 years is the computation of thermodynamic properties including free energy and entropy [12, 13, 14]. But the ground work for free energy perturbation was done by Valleau and Torrie in 1977 [15], for particle insertion by Widom in 1963 and 1982 [16, 17] and for umbrella sampling by Torrie and Valleau in 1974 and 1977 [18, 19]. These methods were primarily developed for use with Monte Carlo simulations continuous thermodynamic integration in MD was first described in 1986 [20]. 

D. E. Smith and A. D. J. Haymet. Free energy, entropy and internal energy of hydrophobic interactions computer simulations. J. Chem. P/iys., 98 6445-6454,... 

Computational results can be related to thermodynamics. The result of computations might be internal energies, free energies, and so on, depending on the computation done. Likewise, it is possible to compute various contributions to the entropy. One frustration is that computational software does not always make it obvious which energy is being listed due to the dilferences in terminology between computational chemistry and thermodynamics. Some of these differences will be noted at the appropriate point in this book. 

Molecular enthalpies and entropies can be broken down into the contributions from translational, vibrational, and rotational motions as well as the electronic energies. These values are often printed out along with the results of vibrational frequency calculations. Once the vibrational frequencies are known, a relatively trivial amount of computer time is needed to compute these. The values that are printed out are usually based on ideal gas assumptions. 

The thermodynamic ceiling temperature (26) T for a polymerization is computed by dividing the AfTp by the standard entropy of polymerization, The T is the temperature at which monomer and polymer are in equHibrium in their standard states at 25°C (298.15 K) and 101.3... 

Various equations of state have been developed to treat association ia supercritical fluids. Two of the most often used are the statistical association fluid theory (SAET) (60,61) and the lattice fluid hydrogen bonding model (LEHB) (62). These models iaclude parameters that describe the enthalpy and entropy of association. The most detailed description of association ia supercritical water has been obtained usiag molecular dynamics and Monte Carlo computer simulations (63), but this requires much larger amounts of computer time (64—66). 

A4) Bond angle bending makes a nonnegligible contribution to conformational entropy and can affect computed equilibrium populations [11]. 

Whether AH for a projected reaction is based on bond-energy data, tabulated thermochemical data, or MO computations, there remain some fundamental problems which prevent reaching a final conclusion about a reaction s feasibility. In the first place, most reactions of interest occur in solution, and the enthalpy, entropy, and fiee energy associated with any reaction depend strongly on the solvent medium. There is only a limited amount of tabulated thermochemical data that are directly suitable for treatment of reactions in organic solvents. Thermodynamic data usually pertain to the pure compound. MO calculations usually refer to the isolated (gas phase) molecule. Estimates of solvation effects must be made in order to apply either experimental or computational data to reactions occurring in solution. 

A successful method to obtain dynamical information from computer simulations of quantum systems has recently been proposed by Gubernatis and coworkers [167-169]. It uses concepts from probability theory and Bayesian logic to solve the analytic continuation problem in order to obtain real-time dynamical information from imaginary-time computer simulation data. The method has become known under the name maximum entropy (MaxEnt), and has a wide range of applications in other fields apart from physics. Here we review some of the main ideas of this method and an application [175] to the model fluid described in the previous section. 

To compute zero-point vibration and thermal energy corrections to total energies as well as other thermodynamic quantities of interest such and the enthalpy and entropy of the system. 

AH and AS to various notional subprocesses such as bond dissociation energies, ionization energies, electron affinities, heats and entropies of hydration, etc., which themselves have empirically observed values that are difficult to compute ab initio. 

The second method can be applied to mixtures as well as pure components. In this method the procedure is to find the final temperature by trial, assuming a final temperature and checking by entropy balance (correct when ASp t, = 0). As reduced conditions are required for reading the tables or charts of generalized thermodynamic properties, the pseudo critical temperature and pressure are used for the mixture. Entropy is computed by the relation. See reference 61 for details. ... 

---