In this work, we offered a machine-learning based predictive model that output the phonon density-of-states by inputting atomic structures. One feature is the prediction of alloy properties. Conventionally alloy prepdictions can be achieved using virtual crystal approximation (VCA), where for the an alloy A_xB_1-x, the virtual mass m_VCA and potential V_VCA are given by

m_VCA = x * m_A + (1-x) * m_B

V_VCA = x * V_A + (1-x) * V_B

In the machine-learning model, using one-hot encoding to represent one pure element with certain mass (i.e. H = [1,0,0,...], He=[0,4,0,0,...]), the feator vector for alloy A_xB_1-x can be written as

[0,0,...., x * m_A, 0,0,..., (1-x) * m_B,0..]

So, two VCA equations emerged into one for machine learning, but without generating additional computational cost when computing alloy properties.