  Intrinsically low lattice thermal conductivity ($\kappa_l$) is a desired
requirement in many crystalline solids such as thermal barrier coatings and
thermoelectrics. Here, we design an advanced machine-learning (ML) model based
on crystal graph convolutional neural network that is insensitive to volumes
(i.e., scale) of the input crystal structures to discover novel quaternary
chalcogenides, AMM'Q$_3$ (A/M/M'=alkali, alkaline-earth, post-transition
metals, lanthanides, Q=chalcogens). Upon screening the thermodynamic stability
of $\sim$ 1 million compounds using the ML model iteratively and performing
density functional theory (DFT) calculations for a small fraction of compounds,
we discover 99 compounds that are validated to be stable in DFT. Taking several
DFT-stable compounds, we calculate their $\kappa_l$ using phonon-Boltzmann
transport equation, which reveals ultralow-$\kappa_l$ ($<$ 2 Wm$^{-1}$K$^{-1}$
at room-temperature) due to their soft elasticity and strong phonon
anharmonicity. Our work demonstrates the high-efficiency of scale-invariant ML
model in predicting novel compounds and presents experimental research
opportunities with these new compounds.
