AIchemy’s Monthly Webinar Series – February 2025

We are delighted to welcome you to our AIchemy Hub’s monthly webinar series.

This month’s talks:

Dr. Fadwa El Mellouhi – Data-driven Materials Discovery

The webinar focuses on the prospects of Data-driven approaches in accelerating materials discovery. It is motivated by the upsurge of machine learning (ML) applications, big data and the adoption of computer science tools in materials science. Fadwa will give an overview of the latest advances in this field with focus on advances made in the last few years applied to energy and environmental materials. Fadwa will show how the combination of high throughput density functional theory (DFT) calculation with machine learning can be useful to perform a systematic analysis of the structure-to- property relation enabling to explore different classes of materials. Fadwa will highlight how the approach offers an interesting guideline to engineer novel materials for light absorption, green hydrogen production and CO2 reduction while enabling to reduce the huge space of experimental trial and error.

Ross Urqurhart – Machine Learning Potentials: Beyond Potential Energy

Machine learning potentials (MLPs) represent a transformative approach in computational chemistry, combining the efficiency of molecular dynamics with the precision of quantum mechanical methods like density functional theory. While traditionally employed for predicting potential energy surfaces, MLPs have the potential to go far beyond. In this talk, I will explore how MLPs can be adapted to predict complex properties such as pKa values and reaction pathways, highlighting their versatility in addressing challenges beyond conventional energy predictions.

Following the presentations, there will be time for questions from the audience.

We look forward to having you attend the event!