820 citations · 12k across the 115 of their papers we have counts for
122 papers
Path Integral Methods in Atomistic Modelling: An Introduction
Michele Ceriotti, David E. Manolopoulos, Thomas E. Markland +1
This book provides an introduction to path integral methods and their application to modeling atomistic processes. The book covers both the foundational theory and recently develop…
Uncertainty quantification by direct propagation of shallow ensembles
Matthias Kellner, Michele Ceriotti
Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further so…
Electronic excited states from physically-constrained machine learning
Edoardo Cignoni, Divya Suman, Jigyasa Nigam +3
Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be…
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…
Physics-inspired Equivariant Descriptors of Non-bonded Interactions
Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran +1
One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size sca…
Robustness of Local Predictions in Atomistic Machine Learning Models
Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud +3
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is co…