From the 6 of 61 papers with an AI index.
14 citations
- Z. Wang8 profiles52 · h 13
- H. Li8 profiles51 · h 17
- Y. Zhang9 profiles49 · h 61
- Z. Li6 profiles38 · h 10
- Y. Huang6 profiles35 · h 61
- L. Zhang5 profiles31 · h 30
- Z. Zhao3 profiles30
- A. Gabrielli3 profiles29 · h 36
- S. Sinha2 profiles29
- G. Mokgatitswane2 profiles28
- J. Kroll3 profiles28 · h 3
- S. Roy-Garand2 profiles28
- Michigan State UniversityUS20 papers
- The Ohio State UniversityUS20 papers
- Argonne National LaboratoryUS19 papers
- Brookhaven National LaboratoryUS19 papers
- FZU ‒ Institute of Physics of the Academy of Sciences of the Czech RepublicCZ19 papers
- Heidelberg UniversityDE19 papers
- Lawrence Berkeley National LaboratoryUS19 papers
- Tsinghua UniversityCN19 papers
- University of Science and Technology of ChinaCN19 papers
- Charles UniversityCZ18 papers
- Jagiellonian UniversityPL18 papers
- University of CalabriaIT18 papers
12 papers · 1 filter
A high-dimensional neural network potential for finite-temperature phenomena in NiTi martensite
Petr Jaroš, Petr Sedlák, Petr Šesták +3
We present a high-dimensional neural network potential (HDNNP) for the martensitic phase of the NiTi shape-memory alloy trained to density functional theory (DFT) data. A central a…
Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science
Miroslav Lebeda, Jan Drahokoupil, Petr VeÅtát +1
High-throughput powder X-ray diffraction (XRD) simulations are a key prerequisite for generating large datasets used in the development of machine-learning models for XRD-based mat…
k-Means Clustering in Fingerprint-Based Configuration Selection for Fitting Interatomic Potentials
Miroslav Lebeda, Jan Drahokoupil, LudvÃk Löbel +1
In this study, we present a method for selecting an arbitrary number of distinct configurations from a larger data set by applying k-means clustering to atomistic configuration fin…
Lattice Parameters and Bulk Modulus of SrTiMnO Perovskites: A Comparison of Exchange-Correlation Functionals with Experimental Validation
Miroslav Lebeda, Jan Drahokoupil, Stanislav Kamba +4
We assessed four exchange-correlation functionals (LDA CA-PZ, GGA parametrized by PBE, PBEsol, and WC) in predicting the lattice parameters of SrTiMn$_{\mathit{x}}…
Revealing interstitial energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy via universal machine learning interatomic potentials
Miroslav Lebeda, Jan Drahokoupil, Veronika MazáÄová +1
Understanding the behavior of light interstitial elements in multicomponent alloys remains challenging due to the complexity of local chemical environments and the high computation…
SimplySQS: An Automated and Reproducible Workflow for Special Quasirandom Structure Generation with ATAT
Miroslav Lebeda, Jan Drahokoupil, Petr VlÄák +2
The special quasirandom structure (SQS) method is widely used for modeling disordered materials under periodic boundary conditions, with the ATAT mcsqs module being one of the most…