There is, today, a strong expectation that future materials will be studied in huge numbers first on the computer, and the best candidates for synthesis in the laboratory will be identified computationally. In this way engineers can efficiently formulate new materials that are lighter, stronger, or otherwise more functionally effective. Such advances are needed across all fields of technology, from energy to medicine to transportation to manufacturing. Recent advances from the molecular modeling community toward quantifying atomic interactions are rapidly eliminating a key obstacle to realization of this vision. Yet, an important obstacle remains: the thermal properties of materials -- those that are important at all but the lowest temperatures -- are needed to predict crystal structures and properties at conditions of practical interest. These properties are too expensive to compute for many materials at once, as needed for a computation-based screening effort. The project team has developed an algorithm that significantly accelerates these calculations without any loss of accuracy, and therefore goes a long way toward removing this obstacle. The aim of this project is to make this breakthrough available to researchers who are using molecular simulation to understand and develop new materials. To this end, this project will refine and extend these methods, and then add computer code to widely-used molecular simulation packages so that they can perform calculations using these new techniques. The team is additionally making efforts to promote awareness and ensure ease-of-use of the methods and their implementation.
"Mapped averaging" is a recently published scheme for the reformulation of ensemble averages. The framework uses approximate results from statistical mechanical theory to derive new ensemble averages (mapped averages) that represent exactly the error in the theory. Well-conceived mapped averages can be computed by molecular simulation with remarkable precision and efficiency; in favorable cases the computational savings are many orders of magnitude. For crystalline systems, a harmonic approximation provides a suitable starting point, allowing simulation to compute precisely the anharmonic contribution to the properties. The result is a technique for computing crystalline properties with unprecedented, transformative efficiency. The aim of this project is to implement these methods on well-established and widely used software packages for simulation of crystalline systems, and to develop mapped averages for new applications of interest to the users of these systems. The theoretical basis for this project appeared in the literature very recently (2015), so the proposed work is completely novel. The techniques are not trivial to understand and are tedious implement, hence adoption by the larger community will require this targeted infrastructure development to make them more accessible to casual users. The full development team includes the computational scientists and software engineers who coded, maintain and distribute the packages where these elements will be introduced. This group assists the project investigators to interface with the simulation packages while ensuring that the new codes are written to the highest standards. The full development team works together also to ensure that the software elements are thoroughly validated for correctness and usability. In addition to the implementation, the project also aims to expand the scope of the mapped-averaging method to encompass properties and substances to which it was not previously applied. This project enables mapped averaging methods to be employed on several widely-used molecular simulation packages: viz, LAMMPS, HOOMD, Cassandra, and VASP, which altogether have a base encompassing thousands of users. Software elements implemented in this project are in many cases completely transparent to the users of these packages, and can be employed by them with no added complication, to speed up their calculations by orders of magnitude. Thus the efforts made in this project will produce an enabling technology, giving scientists and engineers new capabilities to formulate materials for practical applications. Development tools and scripts are constructed in this project, which will facilitate the extension of mapped-averaging methods by other developers to even more molecular simulation packages, material properties, and molecular model systems. Software developed for this project is distributed open-source. Knowledge developed in this project is consolidated to form course materials made available on the web, and used as part of a large component of a graduate molecular simulation course taught by the PI. Training of 1 PhD student and numerous MS and undergraduates occurs across the project period. A strong dissemination effort involving papers, documentation, presentations, and workshops ensure that these methods and tools are understood and adopted by the community. Finally, instructional, graphically-oriented molecular simulation modules are developed and made available on the web to convey concepts related to harmonic and anharmonic components of crystalline behavior, with unique capabilities made possible by the mapped averaging framework.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
