https://www.selleckchem.com/pr....oducts/Gefitinib.htm
The accuracy of some density functional (DF) models widely used in material science depends on empirical or free parameters that are commonly tuned using reference physical properties. Grid-search methods are the standard numerical approximations used to find the optimal values of the free parameters, making the computational complexity to scale with the number of points in the grid. In this report, we illustrate that Bayesian optimization (BO), a sample-efficient machine learning algorithm, can calibrate different density functional
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