-
Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Ex...
This data set contains atomic structures of water clusters, bulk water and rock-salt Li8Mo2Ni7Ti7O32 in the XCrySDen [1] structure format (XSF), and total energies are included... -
Ultrahigh drive current and large selectivity in GeS selector
Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic... -
Electron transport through metal/MoS2 interfaces: edge- or area-dependent pro...
In ultra-thin two-dimensional (2-D) materials, the formation of ohmic contacts with top metallic layers is a challenging task that involves different processes than in bulk-like... -
Semi-local and hybrid functional DFT data for thermalised snapshots of polymo...
Structure prediction for molecular crystals is a longstanding challenge, as often minuscule free energy differences between polymorphs are sensitively affected by the... -
Origin of high strength in the CoCrFeNiPd high-entropy alloy
Recent experiments show that the CoCrFeNiPd high-entropy alloy (HEA) is significantly stronger than CoCrFeNi and with nanoscale composition fluctuations beyond those expected... -
Efficient, interpretable graph neural network representation for angle-depend...
Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding...