64 citations · 144 across the 6 of their papers we have counts for
6 papers
A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning
Daniel Willimetz, Lukáš Grajciar
Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the represent…
A reactive neural network framework for water-loaded acidic zeolites
Andreas Erlebach, Martin Šípka, Indranil Saha +3
Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydro…
Differentiable Simulations for Enhanced Sampling of Rare Events
Martin Šípka, Johannes C. B. Dietschreit, Lukáš Grajciar +1
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically ch…
Accurate large-scale simulations of siliceous zeolites by neural network potentials
Andreas Erlebach, Petr Nachtigall, Lukáš Grajciar
The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computationally feasible protocol for identification of…
Doping isolated one-dimensional antiferro-magnetic semiconductor Vanadium tetrasulfide () nanowires with carriers induces half-metallicity
Shuo Li, Junjie He, Petr Nachtigall +2
Quasi one-dimensional (1D) vanadium tetrasulfide () nanowires (NWs) are synthetic semiconductors which combine with each other through Van der Waals interactions to form bulk…
Control of spintronic and electronic properties of bimetallic and vacancy-ordered vanadium carbide MXenes via surface functionalization
Shuo Li, Junjie He, Petr Nachtigall +2
MXenes are 2D transition metal carbides with high potential for overcoming limitations of conventional two-dimensional electronics. In this context, various MXenes have shown magne…