We are now developing a special purpose machine for accelerating ab initio molecular orbital calculations, MOEngine, a parallel architecture with small local distributed memories. This machine enables low-cost and high-performance molecular orbital calculations. MOEngine has such small memories, several megabytes, for each processor that all the matrix elements cannot be put on each memory. Conventional Fock matrix construction algorithms cannot be applied for MOEngine, and a new parallel distributed algorithm is required in which matrix element data are transferred between a host machine and each processor whenever it is necessary. Then, we developed a novel algorithm for large-scale Fock matrix generation with small local distributed memory parallel architecture. In this paper, we give a detailed explanation of “cutoff", and descrive the relationship between the “cutoff" and a Fock matrix generation algorithm for small distributed memory. In such an algorithm, “matrix element cutoff" which cooperates with the integral cutoff is indispensable for the decrease of the amount of data transfer. We have also investigated how much integral cutoff is done in large molecules. The ratio of the cutoff-survival basis number to the original basis number is less than 10%, and the survival basis number becomes nearly constant. This means that, in large molecules, the number of effective electron repulsion integrals decreases remarkably and is proportional to the order of N2
Two interactive computer-assisted learning (C.A.L.) programmes dealing with basic chemistry concepts are presented. These deal respectively with oxidation numbers and inorganic chemical nomenclature. Both programmes were initially developed in the context of an introductory remedial or streamlining chemistry course taught in French at l'Université de Moncton, Moncton N.B., Canada. An English translation of the programme on Oxidation Numbers already exists, whereas the translation of the Nomenclature programme is intended. Both programmes are well suited for students at the High School and first year University levels.
The relationships between molecular structure and taste quality: sweet or bitter, or several perillartine derivatives were examined using a perceptron type neural network simulator for structure-activity correlation of molecules: Neco with reconstruction of weight matrix method. The reconstruction of weight matrix method was used to optimize the number of neurons in hidden layer. In the case of using six parameters: hydroforbic(log P) and the STERIMOL(L, Wl, Wu, Wr, and Wd) parameters as inputs, the number of neurons in hidden layer is minimized to one by the reconstruction learning method. Even in this case, there is no misclassified compound. The prediction rate by leave-one-out procedure was also 100%. The most important three parameters were the same as predicted by Fisher ratio. The number of input parameters was minimized by holding the number of neurons in hidden layer to one. Two parameters, namely Log P and Wr were found to describe the sweet/bitter activity of perillartine derivatives. Instead of STERIMOL parameters, atomic charges of common molecular skeleton, HOMO and LUMO energies, and HOMO-LUMO energy difference were selected as input data. MOPAC93/AM1 was used to evaluate these parameters. The optimum number of neurons in the hidden layer was also one. Atomic charges and LUMO energy are also important for sweet/bitter classification. This suggests that electronic structure around common molecular skeleton and electron elimination reaction are essential to sweet/ bitter activity of perillartine derivatives.
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