ChemNeuro
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Draw molecule at cyan panel. How to use animation1, animation2
Cis-, Trans- compounds return average
of both. Critical temperature ( Tc ), critical pressure ( Pc ), and critical volume ( Vc ) represent three widely used pure component constants. These critical constants are very important properties in chemical engineering field because almost all other thermo chemical properties are predictable from boiling point and critical constants with using corresponding state theory. So precise prediction of critical constants are very needed. There are several methods to predict critical constants.
Every these methods need normal boiling point to predict critical temperature and the accuracy of Tc estimated value is strongly depend on inputted normal boiling point. So for completely unknown molecule case, it needs to estimate of boiling point, then to calculate Tc, this means multiply two errors. On the contrary, our neural network method predict critical temperature directly from molecular structure and need not boiling point. For critical pressure estimation, summation of group contribution factors with molecular weight (or number of heavy atom) lead to good correlation. But it is said that both Tc and Pc estimation with group contribution method, it can not introduce bi-functional interaction, so multifunctional compounds case, the result error become approximately 5%. Our neural network method takes bi-functional interaction and/or hydrogen bonding effect into account, so accuracy of estimation is much higher than conventional method. Experimental Critical volume or Critical density ( molecular weight / critical volume ) data can not be available so much compare to Tc and Pc. Some data book listed not experimental values but estimated values. Actually, this property is not so sensitive to its structure, but uncertainty of experimental/estimated problem is so serious when applying Vc to estimate liquid density. Our neural network method introduce correction factor from absolute molecular volume calculated from optimized structure by Molecular Orbital. Related Properties Vapor
Pressure and Boiling
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