MARVEL Junior Seminar — May 2025

May 02, 2025, from 12:15 until 13:15, Coviz2 (MED 2 1124), EPFL + Zoom

The MARVEL Junior Seminars aim to intensify interactions between the MARVEL Junior scientists belonging to different research groups – this is held in hybrid mode, in order to maintain in-person contacts and allow off-campus attendees to follow the seminars remotely! We are pleased to propose the 66th MARVEL Junior Seminar:  Anna Paulish (Mathematics for materials modeling - MATMAT, EPFL) and Alessandro Carbone (Lab. of theory and simulation of materials - THEOS, EPFL) will present their research. 

Each seminar consists of two presentations of 25 minutes each, allowing to present on a scientific question in depth, followed by time for discussion. The discussion is facilitated and timed by the chair.

Pizzas will be served after the seminars in order to facilitate discussions based on the talks just presented. 

Onsite participation

12:15 — Seminars take place in EPFL room Coviz2 (MED 2 1124)

~13:15 — Pizzas will be served in the MED building atrium, second floor

Online participation

Starting at 12:15:

https://epfl.zoom.us/j/68368776745
Password: 1923 

Abstracts

Talk 1 — Error-informed Gaussian process regression for predicting DFT quantities

Anna Paulish, Michael Herbst

Mathematics for materials modeling (MATMAT), EPFL

Nowadays, data-driven methods have gained popularity in computational materials science. However, most atomistic machine learning approaches assume that errors in the training data follow small Gaussian noise distributions, requiring all simulations to be of uniformly high quality. This means that numerical parameters -- such as discretisation basis, k-point sampling, and tolerance settings -- have to be accurate throughout the entire data generation process, leading to high computational costs.

This work aims to develop a heteroscedastic Gaussian process regression (GPR) framework for predicting Density Functional Theory (DFT) quantities by incorporating discretization error estimates, moving from the standard Gaussian noise assumption to a more quantitative description of the error. The proposed error-informed GPR approach will be able to better model datasets of different quality, giving a way to reduce data generation costs while maintaining high predictive accuracy. Furthermore, we will identify the key aspects necessary to implement this GPR model as a practical tool enabling accelerated and cost-efficient computational materials discovery.


Talk 2 —  Sum-over-pole framework to embed interacting many-body systems

Alessandro Carbone, Nicola Marzari

Laboratory of theory and simulation of materials (THEOS), EPFL

Abstract TBA




Check the list of the next MARVEL Junior Seminars here.

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