TY - JOUR
T1 - Ensemble Methods in Materials AI — Why Models Disagree and What It Means Scientifically
AU - Claire Martin
AU - Julien Robert
AU - Sophie Bernard
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2023
VL - 2
IS - 2
SP - 30
N2 - The integration of artificial intelligence (AI) into materials science has revolutionized how we predict, design, and discover new materials. Among various AI techniques, ensemble methods have emerged as powerful tools that leverage the collective intelligence of multiple models to enhance prediction accuracy and reliability. This review explores the application of ensemble methods in materials AI, focusing on why individual models disagree and the scientific implications of such disagreements. By analyzing recent advancements, we highlight how ensemble approaches address uncertainties in material property prediction, phase stability, and electronic structure calculations. The review synthesizes insights from peer-reviewed literature published, emphasizing the role of ensemble methods in providing robust predictions and uncovering underlying physical principles. Ultimately, understanding model disagreement not only improves computational efficiency but also deepens our scientific understanding of material behavior.
UR - https://iamrp.net/c147839608
ER -