249 citations · 260 across the 10 of their papers we have counts for
13 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
A fast summation method for the DFT-D3 dispersion correction
Victoria Valeeva, Cheuk Hin Ho, Mario Geiger +4
The DFT-D3 dispersion correction is routinely added to machine learning force fields (MLFFs) trained on dispersion-deficient functionals such as PBE. Its environment-dependent pair…
Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
Cheuk Hin Ho, Christoph Ortner, Yangshuai Wang
Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) i…
An Atomic Cluster Expansion Potential for Twisted Multilayer Graphene
Yangshuai Wang, Drake Clark, Sambit Das +5
Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machin…
Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion
Yangshuai Wang, Gabor Csanyi, Christoph Ortner
Molecular dynamics (MD) simulations provide detailed insight into atomic-scale mechanisms but are inherently restricted to small spatio-temporal scales. Coarse-grained molecular dy…
Analysis of local structure of mechanical and thermal rearrangements in glasses with the atomic cluster expansion
Joerg Rottler, Christoph Ortner
We explore the structural signatures of excitations in amorphous materials with the atomic cluster expansion (ACE), a universal and complete linear basis of descriptors of the atom…
Self-consistent Coulomb interactions for machine learning interatomic potentials
Jack Thomas, William J. Baldwin, Gábor Csányi +1
A ubiquitous approach to obtain transferable machine learning-based models of potential energy surfaces for atomistic systems is to decompose the total energy into a sum of local a…
Surrogate models for vibrational entropy based on a spatial decomposition
Tina Torabi, Yangshuai Wang, Christoph Ortner
The temperature-dependent behavior of defect densities within a crystalline structure is intricately linked to the phenomenon of vibrational entropy. Traditional methods for evalua…
Atomic Cluster Expansion without Self-Interaction
Cheuk Hin Ho, Timon S. Gutleb, Christoph Ortner
The Atomic Cluster Expansion (ACE) (Drautz, Phys. Rev. B 99, 2019) has been widely applied in high energy physics, quantum mechanics and atomistic modeling to construct many-body i…
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…
A Theoretical Case Study of the Generalisation of Machine-learned Potentials
Yangshuai Wang, Shashwat Patel, Christoph Ortner
Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challen…
Equivariant Matrix Function Neural Networks
Ilyes Batatia, Lars L. Schaaf, Huajie Chen +3
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MP…
ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion
William C. Witt, Cas van der Oord, Elena Gelžinytė +10
We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion (D…