The discrete element method (DEM) is a time-driven simulation technique based on a Lagrangian description of particle motion that predicts the flow of granular matter and fine powders in conveying, mixing, drying, and heterogeneous gas-(liquid)-solids reactors. Powders flowing out of bins form bridges, they segregate in suboptimal pharmaceutical v-blenders, and a stream may split into large gulf streams as they enter fluidized bed reactors from standpipes and diplegs. To reduce the uncertainty in scaling up these and other powder process unit operations, researchers apply DEM. It integrates Newton's second law (acceleration equals the sum of the forces) for each particle and models contacts between the particles with springs and dashpots (dampers). It is computationally intensive since it calculates the trajectory of all particles. The availability of open source codes, commercial software, and parallel computer architectures has accelerated its adoption in pharmaceutical, agro-industrial, and mineral processes, and geophysics. The accuracy of DEM models depends on how well researchers calibrate the contact model expressions and their parameters: friction coefficients and the coefficient of restitution. Systems exceeding 1 x 10(8) particles can require weeks of computational time on large computer clusters. Current research targets non-spherical particle interactions and multiphysics problems including heat transfer, mass transfer, and chemical reactions within the particles. The field has grown to 750 indexed articles in WoS in 2017. A bibliographic analysis recognized four research clusters: granular materials, behaviour, particle shape, and deformation; flows, fluidized beds, and computational fluid dynamics; particles, impact, and validation; and granular flow, dynamics, and segregation.