85 citations · 372 across the 13 of their papers we have counts for
13 papers
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
Jacob W. Toney, Samir Darouich, Yiran Wang +3
Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined t…
In-Depth Exploration of Catalytic Sites on Amorphous Solid Water: I. The Astrosynthesis of Aminomethanol
Giulia M. Bovolenta, Gabriela Silva-Vera, Stefano Bovino +3
Chemical processes taking place on ice-grain mantles are pivotal to the complex chemistry of interstellar environments. In this study, we conducted a comprehensive analysis of the…
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…
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…
Processing of hydroxylamine, NH2OH, an important prebiotic precursor, on interstellar ices
Gerán Molpeceres, Víctor. M. Rivilla, Kenji Furuya +3
Hydroxylamine, NH2OH, is one of the already detected interstellar molecules with the highest prebiotic potential. Yet, the abundance of this molecule found by astronomical observat…
Transfer learning for chemically accurate interatomic neural network potentials
Viktor Zaverkin, David Holzmüller, Luca Bonfirraro +1
Developing machine learning-based interatomic potentials from ab-initio electronic structure methods remains a challenging task for computational chemistry and materials science. T…