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Spherically-symmetric atom-centered descriptors of atomic environments havebeen widely used for constructing potential or free energy surfaces ofatomistic and colloidal systems and to characterize local structures usingmachine learning techniques. However, when particle shapes are non-spherical,as in the case of rods and ellipsoids, standard spherically-symmetric structurefunctions alone produce imprecise descriptions of local environments. In orderto account for the effects of orientation, we introduce two- and three-bodyorientation-dependent particle-centered descriptors for systems composed ofrod-like particles. To demonstrate the suitability of the proposed functions,we use an efficient feature selection scheme and simple linear regression toconstruct coarse-grained many-body interaction potentials forcomputationally-efficient simulations of model systems consisting of colloidalparticles with anisotropic shape: mixtures of colloidal rods and nonadsorbingpolymer, hard rods enclosed by an elastic microgel shell, and ligand-stabilizednanorods. We validate the machine-learning (ML) effective many-body potentialsbased on orientation-dependent symmetry functions by using them in directcoexistence simulations to map out the phase behavior of colloidal rods andnon-adsorbing polymer. We find good agreement with results obtained fromsimulations of the true binary mixture, demonstrating that the effectiveinteractions are well-described by the orientation-dependent ML potentials.

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions