c is the ratio of infeasible measurements to total measurements, and t ∈ R + is a parameter (specified with the feas param argument) which controls risk when it comes to selecting infeasible parameters. Here, smaller values of t de-emphasize the feasibility classifier's contribution (second term in Equation ) and thus indicate more risk, while larger values do the opposite and represent less risk. objective functions f = {f i } n i=1 : X → Y to be optimized concurrently. ASF S transforms a vector of objective measurements to a scalar merit value, S : Y → [0, 1] which is processed by the optimizer. Available ASF types are Chimera, Hypervolume, Chebyshev, and Weighted Sum. As an illustrative example of multi-objective optimization in Atlas, we use the dye lasers dataset from Olympus, which reports computed photophysical properties for 3458 organic molecules synthesized from three groups of molecular building blocks -A, B, and C (resulting in A-B-C-B-A pentamers shown in Figure ). Each molecule was subjected to a computational protocol consisting of cheminformatic, semi-empirical and ab initio quantum chemical steps to compute absorption and emission spectra, as well as fluorescence rates. The objectives for this dataset are i) the peak score, a dimensionless quantity given by the fraction of the fluorescence power spectral density that falls within the 400 -460 nm region (to be maximized), ii) the spectral overlap of the absorption and emission spectra (to be minimized), and iii) the fluorescence rate (to be maximized).