The formulation of chemical potentials and free energy changes in biochemical reactions.

In 1994, an IUBMB-IUPAC joint committee recommended a revised formulation for standard chemical potentials and reaction free energies motivated by the fact that, in biochemistry, the reactants and products often exist in multiple charge states depending on the pH and pMg of the solution environment. The recommendation involved both the use of (1) a mathematical transform with the intent to hold the pH constant, and (2) the formulation of reference chemical potentials of ionized isomeric species based on the log sum of the individual standard chemical potentials of each isomeric species. Recently, several reports including a 2020 IUPAC report have appeared that challenged the need for such summary formulations, arguing that the standard chemical potentials were sufficient with full accounting of each of the different charge state isomers involved in a biochemical reaction.

Chemical and biochemical thermodynamics: Is it time for a reunification?

The thermodynamics of chemical reactions in which all species are explicitly considered with atoms and charge balanced is compared with the transformed thermodynamics generally used to treat biochemical reactions where atoms and charges are not balanced. The transformed thermodynamic quantities suggested by Alberty are obtained by execution of Legendre transformation of the usual thermodynamic potentials. The present analysis demonstrates that the transformed values for ΔG' and ΔH'can be obtained directly without performing Legendre transformations by simply writing the chemical reactions with all the pseudoisomers explicitly included and charges balanced.

Practical training framework for fitting a function and its derivatives.

This paper describes a practical framework for using multilayer feedforward neural networks to simultaneously fit both a function and its first derivatives. This framework involves two steps. The first step is to train the network to optimize a performance index, which includes both the error in fitting the function and the error in fitting the derivatives.

Molecular dynamics investigation of the bimolecular reaction BeH + H(2) --> BeH(2) + H on an ab initio potential-energy surface obtained using neural network methods with both potential and gradient accuracy determination.

The classical reaction dynamics of a four-body, bimolecular reaction on a neural network (NN) potential-energy surface (PES) fitted to a database obtained solely from ab initio MP2/6-311G(d,p) calculations are reported. The present work represents the first reported application of ab initio NN methods to a four-body, bimolecular, gas-phase reaction where bond extensions reach 8.1 A for the BeH + H(2) --> BeH(2) + H reaction.

Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting.

The O-O bond dissociation of HOOH is investigated on an analytic ab initio potential-energy surface obtained by fitting the energies of 25,608 configurations using neural network (NN) methods. The electronic structure calculations are executed using MP2 calculations with the 6-31G* basis set. A new data-sampling technique is introduced to collect HOOH configurations in the six-dimensional hyperspace.

Cis-->trans, trans-->cis isomerizations and N-O bond dissociation of nitrous acid (HONO) on an ab initio potential surface obtained by novelty sampling and feed-forward neural network fitting.

The isomerization and dissociation dynamics of HONO are investigated on an ab initio potential surface obtained by fitting the results of electronic structure calculations at 21 584 configurations by using previously described novelty sampling and feed-forward neural network (NN) methods. The electronic structure calculations are executed by using GAUSSIAN 98 with a 6-311G(d) basis set at the MP4(SDQ) level of accuracy. The average absolute error of the NN fits varies from 0.

Classification of atrial fibrillation episodes from sparse electrocardiogram data.

Background: Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. This paper presents the application of the Classification and Regression Tree (CART) technique for detecting spontaneous termination or sustenance of AF with sparse data.

Method: Electrocardiogram (ECG) recordings were obtained from the PhysioNet (AF Termination Challenge Database 2004) Web site.

Molecular dynamics investigations of the dissociation of SiO2 on an ab initio potential energy surface obtained using neural network methods.

The neural network (NN) procedure to interpolate ab initio data for the purpose of molecular dynamics (MD) simulations has been tested on the SiO(2) system. Unlike other similar NN studies, here, we studied the dissociation of SiO(2) without the initial use of any empirical potential. During the dissociation of SiO(2) into Si+O or Si+O(2), the spin multiplicity of the system changes from singlet to triplet in the first reaction and from singlet to pentet in the second.

Prediction of molecular-dynamics simulation results using feedforward neural networks: reaction of a C2 dimer with an activated diamond (100) surface.

A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered.