Predict and design molecular and materials properties with controllable accuracy from first principles, using the fundamental laws of quantum mechanics. In order to transform the quantum simulations techniques developed in the last several decades into predictive design and discovery tools, we will address key issues related to improving accuracy, robustness, efficiency, and software performance and scalability:

• Accuracy: we focus on ab initio molecular dynamics (AIMD) and quantum Monte Carlo (QMC) methods and we plan to develop coupled AIMD/QMC approaches capable of describing materials in the presence of external perturbations.
• Robustness: we plan to develop algorithms and codes for data analysis and validation.
• Efficiency: we plan to work at reducing scaling and complexity of AIMD codes.
• Software performance and scalability: we plan to develop specialized linear algebra algorithms and codes for both next-generation high performance platforms and commodity clusters.