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Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.

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mWe describe the design philosophy, structure, and supporting tool kits of the NWChem computational chemistry package. The primary purpose of this effort was to develop efficient parallel algorithms for a broad range of methods commonly used in computational chemistry. To facilitate this, we developed a shared nonuniform access memory model which simplifies parallel programming while at the same time providing for portability across both distributed-and shared-memory machines. In addition to this specific focus on parallelization, a substantial effort has been made to simplify the general problem of large-scale software development, which is common to many research groups. We find that this simplification can be achieved through judicious use of ideas from the computer science field of software engineering in the design and implementation modeling software applications that provide 10-100 times more computing capability than has been available with conventional supercomputers. While increases in raw computing power alone will greatly expand the range of problems that can be treated by theoretical chemistry methods, a significant investment in new algorithms is needed