Science Success Story
MuST Program Simplifies Predictions of Material Properties
XSEDE ECSS scientist a leader in development of opensource tool for engineering new substances
By Ken Chiacchia, Pittsburgh Supercomputing Center
Using the KKRCPA method, the MuST software converts the complex surroundings of an atom in a random alloy (brass, an alloy of copper and zinc, in this case) to an "effective medium" that averages the properties of the surrounding atoms. 
Materials science gives us substances with novel properties that enable new technologies. But engineering these materials can take tremendous computing power. An international collaboration in which a scientist in XSEDE's Extended Collaborative Support Service (ECSS) played a leading role has developed MuST, a new, opensource supercomputing code that radically reduces the complexity of the calculations. MuST—named for the "multiple scattering theory" on which it's based—makes it possible to simulate samples of material large enough for realworld relevance in much less time.
Why It's Important
Materials science is important for making our world run better, and at less expense. Doping a silicon wafer with a small quantity of impurity atoms can change it from being an electrical conductor to an insulator to a semiconductor—one of those miracle substances we take for granted but which make our computeraided modern lives possible. Altering the ratio of atoms in a glass mixture can produce a nearly unbreakable smartphone screen. Changing the composition of a metal can make it stronger, lighter, or easier to manufacture and form to shape.
"It's a manybody problem, so it is impossible to solve…[Instead] we need to solve the distributedequationeffective potential—it's called ‘the swamp.' By solving this equation, [we] solve the electron density, which answers the question of the energy of the original manybody problem."—Yang Wang, Pittsburgh Supercomputing Center (PSC)
The problem is, materials science is complicated. Particularly when a material has many different elements in it, understanding its properties—and how it can be engineered to do what we want—involves taking into consideration the interactions between each atom, its neighbors, their neighbors, and so on. This manybody problem, which is impossible to solve exactly, can be approximated to a high level of confidence with computers. But with the complicated rules of quantum chemistry that govern materials at the atomic scale, the complexity ramps up when you try to simulate more than a few atoms of a material. This is a problem, as small numbers of atoms may have different properties than the same material in realworld bulk. Particularly for disordered materials—some of the most promising and interesting materials for development—this complexity quickly pushes the problem beyond the point at which even the world's largest supercomputers can crunch it.
How XSEDE Helped
Yang Wang, computational scientist at PSC and an XSEDE ECSS consultant. 
Enter Yang Wang, senior computational scientist at PSC and ECSS consultant, and an international collaboration with the Oak Ridge National Laboratory, Universität Augsburg, University of the Chinese Academy of Sciences, Louisiana State University, and Middle Tennessee State University. They developed MuST, a software package that uses density functional theory (DFT) for ab initio investigation of disordered materials—that is, predicting materials' properties from first principles.
Ab initio quantum chemistry methods is a timetested way to accurately predict the properties of a substance. It also helps lab scientists focus expensive realworld experiments on the most promising candidates, speeding development. But the computational cost of DFT calculations typically scales with the third power of the number of electrons—in other words, if computing the behavior of a given number of electrons in a material takes a certain amount of time and power to calculate, twice as many will take eight times as much, three will take 27 times as much and four times as many 64 times as much. This puts a tight limit on how many atoms can be simulated, since more atoms means more electrons.
MuST takes advantage of locally selfconsistent multiple scattering theory (LSMS) to simplify the problem. Instead of, say, calculating the interactions of an aluminum atom in an AlCrFeCoNi alloy with each aluminum, chromium, iron, cobalt, and nickel atom nearby, it calculates those other atoms as a kind of average "soup" in which the aluminum atom sits.
"The soup reproduces the total behavior of these other atoms in their proportions in that alloy instead of accounting for each atom individually … Each domain has its own potential, which you then add together to get an effective potential for the whole space. You can treat it as singlesite scattering potential."—Yang Wang, PSC
MuST's simplification, using a combination of the KorringaKohnRostoker and coherent potential approximation methods (KKRCPA), reduces the complexity of the computation enormously. Instead of scaling to the third power, it scales with the number of electrons. Instead of 64 times the computing power enabling you to calculate four times as many electrons, it enables you to simulate 64 times as many.
Initial work with MuST, which has been available to the general scientific community since December 2019, has produced results as good as or better than those of goldstandard methods such as the coupled cluster singledouble and triple or quantum monte carlo techniques, which require much more computing power. It also goes beyond the reach of those methods when large numbers of atoms (thousands or more) are involved.
Goals for the future include incorporating the LSMS method with typical medium embedding, a different type of "soup" than used in the CPA method and which is designed to include physics that are not addressed by CPA. This would allow scientists to capture the metalinsulator transition phenomena driven by disorder in quantum materials, and integrate the KuboGreenwood formula into the package. This will, in turn, enable the investigation of electronic transport in disordered structures. The movement of electrons through a material underlies the phenomenon of electrical flow in pure metals but isn't nearly as well understood in materials with disorder caused by impurities or by alloying different metals.
Scientists can download the program—and join the team to develop MuST—here.
At a Glance:

Materials science gives us substances with novel properties that enable new technologies.

Engineering these materials can take tremendous computing power.

An international collaboration in which a scientist in XSEDE's Extended Collaborative Support Service (ECSS) played a leading role has developed MuST, a new, opensource supercomputing code that radically reduces the complexity of the calculations.

The new software makes it possible to simulate samples of material large enough for realworld relevance in much less time.