Matthew Hayes

Chemical engineering

Hometown: Tempe, Arizona, United States

Graduation date: Spring 2023

Sustainability icon, disabled. A green leaf.

FURI | Spring 2022

Using Machine Learned Forces and Energies to Improve Quantum Mechanical Simulations

Density Functional Theory (DFT) calculations have been used for decades to predict the chemical properties of compounds and materials. While DFT calculations are highly chemically accurate, they are computationally costly to perform for systems containing many atoms. On the other hand, classical mechanics-based molecular dynamics (MD) simulations can be performed at a larger scale and can model thousands of atoms, albeit with less chemical accuracy. Using machine learning to predict atomic forces and map atomic positions to an energy density will improve the accuracy of molecular dynamics simulations and will allow for faster prediction of material properties.

Mentor:

View the poster
QR code for the current page

It’s hip to be square.

Students presenting projects at the symposium are encouraged to download this personal QR code and include it within your poster. This allows symposium attendees to explore more about your project and about you in the future. 

Right click the image to save it to your computer.

Additional projects from this student

Studying water solvent effects of alumina on selenate adsorption will advance knowledge in the removal of toxic anions from the environment.

Mentor:

  • FURI
  • Fall 2021

FURI Totals

TotalStudents

0

FacultyMentors

0

OnlineSymposia

0

FocusAreas

0

FURIProjects

0

MOREProjects

0

KEENProjects

0

GCSPProjects

0