37 citations · 71 across the 8 of their papers we have counts for
8 papers
Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry
Marvin Alberts, Oliver Schilter, Federico Zipoli +2
Spectroscopic techniques are essential tools for determining the structure of molecules. Different spectroscopic techniques, such as Nuclear magnetic resonance (NMR), Infrared spec…
Learning a reactive potential for silica-water through uncertainty attribution
Swagata Roy, Johannes P. Dürholt, Thomas S. Asche +2
The reactivity of silicates in an aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite ca…
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
Matteo Manica, Jannis Born, Joris Cadow +21
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful represe…
On the Quality of Uncertainty Estimates from Neural Network Potential Ensembles
Leonid Kahle, Federico Zipoli
Neural network potentials (NNPs) combine the computational efficiency of classical interatomic potentials with the high accuracy and flexibility of the ab initio methods used to cr…
The solid-state Li-ion conductor LiTaO: A combined computational and experimental study
Leonid Kahle, Xi Cheng, Tobias Binninger +8
We study the oxo-hexametallate LiTaO with first-principles and classical molecular dynamics simulations, obtaining a low activation barrier for diffusion of 0.29 eV a…
Simulating diffusion properties of solid-state electrolytes via a neural network potential: Performance and training scheme
Aris Marcolongo, Tobias Binninger, Federico Zipoli +1
The recently published DeePMD model (https://github.com/deepmodeling/deepmd-kit), based on a deep neural network architecture, brings the hope of solving the time-scale issue which…