Award Abstract # 2213527
LEAPS-MPS: Spin-lattice interaction in paramagnetic cubic iron at high pressure

NSF Org: DMR
Division Of Materials Research
Recipient: THE UNIVERSITY OF TEXAS AT EL PASO
Initial Amendment Date: March 28, 2022
Latest Amendment Date: March 28, 2022
Award Number: 2213527
Award Instrument: Standard Grant
Program Manager: Alexios Klironomos
aklirono@nsf.gov
 (703)292-4920
DMR
 Division Of Materials Research
MPS
 Directorate for Mathematical and Physical Sciences
Start Date: June 1, 2022
End Date: May 31, 2025 (Estimated)
Total Intended Award Amount: $249,068.00
Total Awarded Amount to Date: $249,068.00
Funds Obligated to Date: FY 2022 = $249,068.00
History of Investigator:
  • Jorge Munoz (Principal Investigator)
    jamunoz@utep.edu
Recipient Sponsored Research Office: University of Texas at El Paso
500 W UNIVERSITY AVE
EL PASO
TX  US  79968-8900
(915)747-5680
Sponsor Congressional District: 16
Primary Place of Performance: University of Texas at El Paso
500 West University Ave.
El Paso
TX  US  79968-8900
Primary Place of Performance
Congressional District:
16
Unique Entity Identifier (UEI): C1DEGMMKC7W7
Parent UEI: C1DEGMMKC7W7
NSF Program(s): OFFICE OF MULTIDISCIPLINARY AC
Primary Program Source: 010V2122DB R&RA ARP Act DEFC V
Program Reference Code(s): 075Z, 7569, 8084, 095Z, 102Z
Program Element Code(s): 125300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

This award is funded in whole under the American Rescue Plan Act of 2021 (Public Law 117-2).

NONTECHNICAL SUMMARY

This LEAPS-MPS award supports computational and theoretical research on the effect of magnetism on the way iron atoms arrange themselves at high temperatures and pressures. As one of the principal constituents of the Earth, and being of paramount importance to our technological civilization, iron is one of the most intensely studied materials, but quantum mechanical simulations that simultaneously include magnetism and the displacement patterns of iron atoms from their equilibrium positions due to temperature are computationally prohibitive. These simulations are nevertheless important because many physical and chemical properties of materials depend on the particular arrangement of atoms and their vibrations around equilibrium positions, and the ability to accurately predict such patterns can enable the design of materials using only computer simulations.

The research team will approach this computational challenge by using a particular computer algorithm to efficiently search for general parameters that result in predictions that are consistent with experiments, and by using these parameters to guide quantum mechanical and other simulations. The team will also spearhead the use of a machine learning algorithm capable of learning two or more features of the material and transparently showing their interaction.

This award also supports the creation of educational resources that will be used to expose community college students to concepts of computational materials science and will facilitate formal and informal mentorship for community college students by faculty and undergraduate researchers at the University of Texas El Paso, and establishing research collaborations with researchers at the University of California Berkeley.

TECHNICAL SUMMARY

This LEAPS-MPS award supports computational and theoretical research on how magnetic spin excitations (magnons) in the paramagnetic states of face-centered cubic and body-centered cubic iron affect the relative thermodynamic stability of the two phases at temperatures up to the melting point and pressures up to 15 GPa through their interaction with phonons. Simulations of the lattice dynamics from first principles are computationally expensive, and the inclusion of spin degrees of freedom can make the direct computation of the interaction prohibitive. Nevertheless, with an estimated contribution to the free energy of up to 35 meV/atom, the spin-lattice coupling cannot be ignored in calculations of the thermodynamics and phase stability of this system.

The research team will approach the challenge by (i) empirically constraining the interatomic force constants, from which phonon dispersion relations can be computed via evolutionary computation, and (ii) developing machine learning models based on mathematical graphs capable of learning both atomic and spin configurations to be used in dynamics simulations.

This award also supports the creation of educational resources that will be used to expose community college students to concepts of computational materials science and will facilitate formal and informal mentorship for community college students by faculty and undergraduate researchers at the University of Texas El Paso, and establishing research collaborations with researcher at the University of California Berkeley.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.

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