From Noise to Knowledge - Stochastic Machine Learning for Materials Design

Explore stochastic machine learning approaches for materials design in this 54-minute virtual seminar presented by Dr. Dinis Abranches from the University of Aveiro, Portugal. Learn how to transform noisy data into valuable knowledge for advancing materials science through probabilistic machine learning methods. Discover techniques for handling uncertainty in materials property prediction and design optimization. Understand the application of stochastic algorithms in identifying promising material candidates and navigating the complex landscape of materials discovery. Gain insights into how probabilistic approaches can improve the reliability and interpretability of machine learning models in materials research, enabling more informed decision-making in the development of new materials with desired properties.