473 citations · 2.8k across the 34 of their papers we have counts for
34 papers
NEPMaker: Active learning of neuroevolution machine learning potential for large cells
Junjie Wang, Shuning Pan, Haoting Zhang +4
Machine learning potentials (MLPs) achieve near first-principles accuracy but often fail for atomic environments outside the training distribution. Active learning can mitigate thi…
Physically Intuitive Anisotropic Model of Hardness
Faridun N. Jalolov, Alexander G. Kvashnin
The hardness of materials plays an important role in material design. There are numerous experimental methods to measure the hardness of materials, but theoretical prediction of ha…
Mechanical properties of single and polycrystalline solids from machine learning
Faridun N. Jalolov, Evgeny V. Podryabinkin, Artem R. Oganov +2
Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of methods and low accuracy of empirical…
Generating and grading 34 Optimized Norm-Conserving Vanderbilt Pseudopotentials for Actinides and Super Heavy Elements in the PseudoDojo
Christian Tantardini, Miroslav Iliaš, Matteo Giantomassi +3
In the last decades, material discovery has been a very active research field driven by the need to find new materials for many different applications. This has also included mater…
Evidence for Pseudogap Phase in Cerium Superhydrides: CeH and CeH
Dmitrii Semenok, Jianning Guo, Di Zhou +9
Polyhydride superconductors have been shown to possess metallic properties with a Bardeen-Cooper-Schrieffer-type superconducting ground state. Here, we provide evidence for unconve…
Material Hardness Descriptor Derived by Symbolic Regression
Christian Tantardini, Hayk A. Zakaryan, Zhong-Kang Han +4
Hardness is a materials' property with implications in several industrial fields, including oil and gas, manufacturing, and others. However, the relationship between this macroscal…