NSF Org:	DMR Division Of Materials Research
Recipient:	REGENTS OF THE UNIVERSITY OF MICHIGAN
Initial Amendment Date:	July 26, 2022
Latest Amendment Date:	July 26, 2022
Award Number:	2213806
Award Instrument:	Standard Grant
Program Manager:	Daryl Hess dhess@nsf.gov  (703)292-4942 DMR  Division Of Materials Research MPS  Directorate for Mathematical and Physical Sciences
Start Date:	September 1, 2022
End Date:	August 31, 2026 (Estimated)
Total Intended Award Amount:	$380,000.00
Total Awarded Amount to Date:	$380,000.00
Funds Obligated to Date:	FY 2022 = $380,000.00
History of Investigator:	Katsuyo Thornton (Principal Investigator) kthorn@umich.edu Liang Qi (Co-Principal Investigator) Wenhao Sun (Co-Principal Investigator)
Recipient Sponsored Research Office:	Regents of the University of Michigan - Ann Arbor 1109 GEDDES AVE STE 3300 ANN ARBOR MI  US  48109-1015 (734)763-6438
Sponsor Congressional District:	06
Primary Place of Performance:	Regents of the University of Michigan - Ann Arbor 3003 South State St. Room 1062 Ann Arbor MI  US  48109-1274
Primary Place of Performance Congressional District:	06
Unique Entity Identifier (UEI):	GNJ7BBP73WE9
Parent UEI:
NSF Program(s):	CONDENSED MATTER & MAT THEORY
Primary Program Source:	01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	1711
Program Element Code(s):	176500
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.049

ABSTRACT

NONTECHNICAL SUMMARY
This award provides support for the Summer School for Integrated Computational Materials Education that is held on the campus of the University of Michigan, Ann Arbor. Established in 2011, this summer school aims to expand the educational opportunities in computational materials science by training and assisting graduate students, postdoctoral fellows, and professors who intend to incorporate computational and data science tools into university-level materials science and engineering education. The participants who complete the program become ambassadors of computational materials science and engineering - they return to their home universities/colleges and implement the modules and approaches presented in the summer school. This in turn stimulates the advancement of computational materials science, and thus will ripple through the broader field of materials science and engineering. Graduate student participants are trained in computational and data science approaches for both research and teaching, providing them the first step towards more in-depth learning of the computational techniques and future career path in higher education.


TECHNICAL SUMMARY
This award provides support for the Summer School for Integrated Computational Materials Education. While computational approaches have transformed materials research, the education and training in materials science and engineering have not fully reflected the evolution of the field. In response to the needs and challenges identified by the community, this two-week summer school was established to ?educate the educator? by providing training through an intensive course on computational materials science and engineering (CMSE) and focus sessions on educational modules that can be adopted into existing materials science and engineering core courses. This program aims to equip current and future educators with abilities and materials to introduce CMSE to undergraduate students. Graduate students, postdoctoral fellows, and faculty who intend to incorporate computational and data science tools into university-level materials science and engineering education are invited to participate in the program. The modules that have been developed incorporate concepts from thermodynamics, kinetics, electronic properties, and mechanics. By training the educators, the Summer School benefits not only those who attend, but also the undergraduates who would be taught by the participants. It also provides a stepping-stone towards more intensive CMSE research training for researchers who are not necessarily familiar with computational approaches. This in turn broadens the application of computational and data science tools in materials research and development and thus stimulates the advance of computational materials science education, leading to ripple effects in the broader field of materials science and engineering.

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.

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