5 papers
TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials
Yong-Chao Wu, Xiaoya Chang, Tero Mäkinen +5
Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training s…
General-Purpose Machine-Learned Potential for CrCoNi Alloys Enabling Large-Scale Atomistic Simulations with First-Principles Accuracy
Yong-Chao Wu, Tero Mäkinen, Mikko Alava +1
CrCoNi medium-entropy alloys exhibit exceptional mechanical properties arising from pronounced chemical complexity, including short-range order (SRO), and low stacking fault energy…
Exploring the impact of Ti/Al on L12 nanoprecipitation and deformation behavior in CoNiFeAlTi multi-principal element alloys through atomistic simulations
Amin Esfandiarpour, Anshul D. S. Parmar, Silvia Bonfanti +3
Recent studies on CoNi-based multi-principal element alloys (MPEAs) have demonstrated high strength and ductility, attributed to the formation of stable L12 nanoscale precipitates.…
Nanoindentation of single crystalline Mo: Atomistic defect nucleation and thermomechanical stability
F. J. DomÃnguez-Gutiérrez, S. Papanikolaou, A. Esfandiarpour +2
The mechanical responses of single crystalline Body-Centered Cubic (BCC) metals, such as molybdenum (Mo), outperform other metals at high temperatures, so much so that they are con…
Comparative study on radiation resistance of WTaCrV high-entropy alloy and tungsten in helium-containing conditions
Amin Esfandiarpour, Damian Kalita, Zbigniew Koziol +1
W and W-based high-entropy alloys (HEAs) are promising candidates for plasma-facing materials in fusion reactors. While irradiation studies on W have revealed a tendency for helium…