3 papers
physics.chem-ph2025
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
physics.comp-ph2024
An automated framework for exploring and learning potential-energy surfaces
Yuanbin Liu, Joe D. Morrow, Christina Ertural +6
Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing mach…
cond-mat.mtrl-sci2024
SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
Sasan Amariamir, Janine George, Philipp Benner
Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a c…