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Surface segregation in high-entropy alloys from alchemical machine learning
Arslan Mazitov, Maximilian A. Springer, Nataliya Lopanitsyna +3
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, th…
Modeling high-entropy transition-metal alloys with alchemical compression
Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer +2
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mec…
Beyond potentials: integrated machine-learning models for materials
Michele Ceriotti
Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energ…
Predicting hot-electron free energies from ground-state data
Chiheb Ben Mahmoud, Federico Grasselli, Michele Ceriotti
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation te…
Thermodynamics and dielectric response of by data-driven modeling
Lorenzo Gigli, Max Veit, Michele Kotiuga +3
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate descri…
Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat
Benjamin A. Helfrecht, Giovanni Pireddu, Rocio Semino +2
Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of distinct siliceous networks can be generated by computer-aided…