249 citations · 289 across the 5 of their papers we have counts for
6 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…
Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
Moritz René Schäfer, Nico Segreto, Fabian Zills +2
We introduce Atomistic learned potentials in JAX (apax), a flexible and efficient open source software package for training and inference of machine-learned interatomic potentials.…
Zero Shot Molecular Generation via Similarity Kernels
Rokas Elijošius, Fabian Zills, Ilyes Batatia +4
Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative…
ZnTrack -- Data as Code
Fabian Zills, Moritz Schäfer, Samuel Tovey +2
The past decade has seen tremendous breakthroughs in computation and there is no indication that this will slow any time soon. Machine learning, large-scale computing resources, an…
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…
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
Viktor Zaverkin, Julia Netz, Fabian Zills +2
We propose a machine learning method to model molecular tensorial quantities, namely the magnetic anisotropy tensor, based on the Gaussian-moment neural-network approach. We demons…