Fully Automated Molecular Design with Atomic Resolution for Desired Thermophysical Properties

The value of fine and specialty chemicals is often determined by the specific requirements in their physical and chemical properties. Therefore, it is most desirable to design the structure of chemicals to meet some targeted material properties. In the past, the design of specialty chemicals has been based largely on experience and trial-and-error. However, recent advances in computational chemistry and machine learning can offer a new path to this problem. In this presentation, we demonstrate a successful example where the structure of a chemical of specified value of octanol–water partition coefficient (<i>K</i>_ow) can be predicted by computers. This method consists of two parts, the first being a robust method, the COSMO-SAC activity coefficient model, that predicts the activity coefficient with input of only the molecular structure. The second component of this method is a derivative-free optimization algorithm that searches in the multidimensional structure space for the desired value of <i>K</i>_ow. In particular, the genetic algorithm (GA), based on the Darwinian theory of evolution and natural selection, combined with simulated annealing (SA) is adopted for this purpose. Compared to other optimization algorithms, GA can overcome the problem of being trapped in local minima and SA can help improve the convergence. Therefore, the GA–SA combination has been found to be very suitable for molecular design. We show that the value of <i>K</i>_ow can be achieved within 1% of the target in 30 generations with a proper set of evolution parameters (including the size of the population, the probability of selection, the rate of temperature annealing, etc.). The same method can be applied to the search for chemicals with other desired properties, such as vapor pressure and solubility