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We present a complete set of chemo-structural descriptors to significantlyextend the applicability of machine-learning (ML) in material screening andmapping energy landscape for multicomponent systems. These new descriptorsallow differentiating between structural prototypes, which is not possibleusing the commonly used chemical-only descriptors. Specifically, we demonstratethat the combination of pairwise radial, nearest neighbor, bond-angle,dihedral-angle and core-charge distributions plays an important role inpredicting formation energies, bandgaps, static refractive indices, magneticproperties, and modulus of elasticity for three-dimensional (3D) materials aswell as exfoliation energies of two-dimensional (2D) layered materials. Thetraining data consists of 24549 bulk and 616 monolayer materials taken fromJARVIS-DFT database. We obtained very accurate ML models using gradientboosting algorithm. Then we use the trained models to discover exfoliable2D-layered materials satisfying specific property requirements. Additionally,we integrate our formation energy ML model with a genetic algorithm forstructure search to verify if the ML model reproduces the DFT convex hull. Thisverification establishes a more stringent evaluation metric for the ML modelthan what commonly used in data sciences. Our learnt model is publiclyavailable on the JARVIS-ML website (https://www.ctcms.nist.gov/jarvisml )property predictions of generalized materials.

Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape