Despite the rapid commercialization of additive manufacturing technology such as selective laser melting, SLM, there are gaps in process modeling and material property prediction that contribute to slow and costly process qualification and product certification. To address these gaps, CFDRC and our partner Dr. Kevin Chou, University of Alabama, will develop multiple computationally efficient, high-fidelity simulation tools for the SLM process. During Phase I the team will apply adaptive meshing to enable efficient thermomechanical simulations for centimeter size test coupon builds, leverage existing multiphase flow models to analyze particle size effects on material variations, apply phase field models to predict microstructure evolution, and evaluate model predictions against experimental characterization. During Phase II, the modeling tools will be extended to improve computational efficiency and scalability to aerospace component dimensions by further leveraging parallel computing and other acceleration techniques. The fidelity of the models will be enhanced to better predict distortion, residual stress, microstructure and defects from process conditions; and additional process data will be used to validate the resulting codes. The resulting toolset will be capable of efficiently predicting these dimensional and microstructural properties of SLM components from process conditions, while addressing important design and build features such as overhanging sections and build supports. The high-fidelity, physics based nature of the codes will allow straightforward application to new materials, and to guiding development of and verifying analytical physics models for process control.