  We present a complete set of chemo-structural descriptors to significantly
extend the applicability of machine-learning (ML) in material screening and
mapping energy landscape for multicomponent systems. These new descriptors
allow differentiating between structural prototypes, which is not possible
using the commonly used chemical-only descriptors. Specifically, we demonstrate
that the combination of pairwise radial, nearest neighbor, bond-angle,
dihedral-angle and core-charge distributions plays an important role in
predicting formation energies, bandgaps, static refractive indices, magnetic
properties, and modulus of elasticity for three-dimensional (3D) materials as
well as exfoliation energies of two-dimensional (2D) layered materials. The
training data consists of 24549 bulk and 616 monolayer materials taken from
JARVIS-DFT database. We obtained very accurate ML models using gradient
boosting algorithm. Then we use the trained models to discover exfoliable
2D-layered materials satisfying specific property requirements. Additionally,
we integrate our formation energy ML model with a genetic algorithm for
structure search to verify if the ML model reproduces the DFT convex hull. This
verification establishes a more stringent evaluation metric for the ML model
than what commonly used in data sciences. Our learnt model is publicly
available on the JARVIS-ML website (https://www.ctcms.nist.gov/jarvisml )
property predictions of generalized materials.
