Seamless acceleration of ab initio materials modeling with machine learning

Machine-learning interatomic potentials are a promising tool combining the efficiency of empirical interatomic potentials and accuracy of quantum-mechanical models such as the density functional theory (DFT). Combined with active-learning algorithms, machine-learning potentials can be constructed automatically for a given problem thus seamlessly accelerating DFT. Machine-learning potentials commit a small error compared to DFT, but this error is often smaller than the error of DFT as compared to the experimental data. However, in some applications machine-learning potentials can be used as a screening tool thus completely eliminating their approximation error in the final answer. In my presentation I will introduce these algorithms and demonstrate how these algorithms can yield a speedup of several orders of magnitude in a number of applications, including construction of convex hulls of stable alloy structures, computing vibrational and configurational free energy of alloys, and diffusion of point defects.