6 papers
The Degeneracy Distillery
T. Lucas Makinen, Deaglan J. Bartlett, Niall Jeffrey +1
When two or more parameters or labels produce similar data, they are degenerate, or hard to distinguish. Degeneracies render both label prediction and inverse problems difficult, s…
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…
Elastic softening and fracture in randomly perforated solids
Tero Mäkinen, Alessandro Taloni, Giulio Costantini +3
We study the mechanical response of quasi-brittle polymethyl methacrylate (PMMA) specimens containing controlled random distributions of laser-cut holes. Tensile tests combined wit…
Early Prediction of Creep Failure via Bayesian Inference of Evolving Barriers
Juan Carlos Verano-Espitia, Tero Mäkinen, Mikko J. Alava +1
Creep under a sustained load can persist for long times yet culminate in abrupt yielding or rupture, implying a finite lifetime even when the material appears solid. Here, we formu…
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…
pyRheo: An open-source Python package for complex rheology
Isaac Y. Miranda-Valdez, Aaro Niinistö, Tero Mäkinen +3
Mathematical modeling is a powerful tool in rheology, and we present pyRheo, an open-source package for Python designed to streamline the analysis of creep, stress relaxation, osci…