Upcoming Webinar
Polyvalent Machine-Learned Potential for Cobalt: from Bulk to Nanoparticles
In this webinar we describe the development of a highly accurate machine-learned potential (MLP) for Co, enabling simulations of large models of bulk material, surfaces, and nanoclusters over extended time scales across a wide range of temperatures and pressures. While non-magnetic itself, the MLP is trained on several thousand spin-polarized ab initio computations performed using MedeA VASP. The resulting MLP closely reproduces the phonon dispersions of hexagonal close-packed (hcp) and face-centered cubic (fcc) Co, Co surface energies, and the relative stabilities of Co nanoparticles of various shapes. The thermal expansion coefficient and the melting temperature of Co computed with this MLP are close to experimental values. Furthermore, this MLP captures nuanced material properties such as vacancy formation energies on nanoparticle vertices. This accuracy and versatility make the potential suitable for a wide array of applications, including modeling the geometry of Co catalytic surfaces.
Webinar Sessions
Tuesday, March 26th:
Live Q&A
10:00 AM PST (USA)
1:00 PM EST (USA)
18:00 CET (EUROPE)
Wednesday, March 27th:
Live Q&A
07:00 AM PST (USA)
10:00 AM EST (USA)
15:00 CET (EUROPE)
19:30 IST (INDIA)
Thursday, March 28th:
Live Q&A
07:00 CET (EUROPE)
11:30 IST (INDIA)
14:00 CST (CHINA)
15:00 JST (JAPAN)
*Recording and Slides
Registrations will also include a link to the recording and slides after the sessions end.
Presenters
Dr. Marthe Bideault
Marthe Bideault is a PhD student at Materials Design, in partnership with the University of Paris-Saclay. Her thesis topic focuses on modeling a new catalytic reaction for ammonia synthesis. This research project stems from a collaboration between Professor Ryoji Asahi from the University of Nagoya and Materials Design.
During her master's program last year, she completed an internship at Materials Design, where she worked on modeling the diffusion of oxygen in zirconium oxides.
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