Abstract

Computer simulations are becoming useful in providing insight in the physical and chemical processes taking places in nature. Simulations yield molecular level understanding, which is often complementary information to the understanding provided by experimental investigations. Yet, they are only useful when when they can accurately model the physical system. High accuracy is often only obtained by resorting to first principles, and by modelling the quantum mechanics features of the system of interest at the atomic level. Thriving nanotechnologies and exciting experiments pose big challenges to computational approaches. On the one hand, the systems to be simulated are large and computationally expensive, and their physical and thermal properties require sampling of a large phase space (using molecular dynamics or other techniques). On the other hand, the high accuracy required to evaluate inter-atomic interactions often means using very accurate and expensive approaches to solve the Schrodinger equation. We discuss here some of the most accurate approaches available to assess the ground state electronic states and their properties in molecular systems, solids and surfaces, namely quantum Monte Carlo (QMC) methods. QMC simulations are computationally expensive and often demands the employment of high performance computers. However, recent developments have drastically reduced the overall cost of QMC , especially in the evaluation of interaction energies. QMC methods can be used to benchmark cheaper but less accurate approaches (such as density functional theory, or empirical force fields) promoting their further developments. The combination of this hierarchy of methods, coupled with machine learning techniques, then provides high accuracy for systems whose size would preclude a full quantum mechanics approach.