AI in CHEM Seminar Series: Erin Makara (VTT)

This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments.

Date and location

• Wednesday 12 March 2025 @ 13:15-14:00
• A304 Ke2 lecture hall in the main building of the School of Chemical Engineering, Kemistintie 1, 02150 Espoo.

Agenda

• 13:00-13:15. Setup and brief info for the registered students.
• 13:15-14:00. Seminar by Erin Makara, lecture hall A304.
• 14:00-onwards. Coffee, netwoking and mingling in the lobby adjacent to the lecture hall.

Seminar info

ML-Assisted Material Simulation and Exploration: Moving Past Symmetry

Erin Makara, VTT

Simulating asymmetrical and amorphous materials with diverse elemental compositions presents significant challenges due to their large system sizes and the inability to leverage symmetry-based simplifications. Conventional computational methods struggle to efficiently model these high-entropy materials, however advances in machine-learned force fields (MLFFs) have provided a promising avenue for accelerating simulations while maintaining accuracy. This talk will discuss how ML-accelerated electronic and dynamic calculations enable the exploration of amorphous materials. It will discuss considerations and workflows for generating training data for the ML models, as well as discussion on validity of the approach through the lens of accuracy and time.

About the speaker

Erin Makara has found themselves at the crossroads of physics, chemistry, mathematics, and computer science, working towards advancing computational methods beyond their current limitations. They are currently a Research Scientist at Technical Research Centre of Finland VTT and a PhD student at Aalto University under the guidance of Dr. Anssi Laukkanen and Prof. Antti Karttunen. Erin holds a Master’s degree in Chemistry and is working alongside the PhD on a second Master’s degree in Machine Learning, Data Science and Artifical Intellignece. Alongside research and studies, they work to simulate various molecules and materials and predict their properties to aid in their team’s multiscale simulation effort.