相关论文: Phase discovery with active learning: Application …
Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at…
This paper reviews the status of molecular dynamics as a method in describing solid-solid phase transitions, and its relationship to continuum approaches. Simulation work done in NiTi and Zr using first principles and semi-empirical…
Nanoporous metals or nanofoams are a promising material class that is considered for sensing, actuation, and catalysis. To date, they mostly based on simple noble metals such as nanoporous gold, which exhibit peculiar stress-strain response…
Additive manufacturing has been vastly applied to fabricate various structures of nickel-titanium (NiTi) shape memory alloys due to its flexibility to create complex structures with minimal defects. However, the microstructure heterogeneity…
We construct a fast, transferable, general purpose, machine-learning interatomic potential suitable for large-scale simulations of $N_2$. The potential is trained only on high quality quantum chemical molecule-molecule interactions, no…
Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significant limitations. Classical force fields are efficient but…
Nitinol (NiTi), the most widely used shape-memory alloy, exhibits an austenite phase that has yet to be identified. The usually assumed austenite structure is cubic B2, which has imaginary phonon modes, hence it is unstable. We suggest a…
Ab initio simulations are capable of providing detailed information of material behavior at the nanoscale. Simulating experimentally relevant situations is, however, often computationally intense. Using hybrid approaches between ab initio…
The shape memory behavior of a NiTi nanoparticle is analyzed by molecular dynamics simulations. After a detailed description of the equilibrium structures of the used model potential, the multi variant martensitic ground state, which…
Nickel-titanium that has an austenite to martensite phase transition has been studied extensively in the past as a shape memory alloy, but a lot remains to be learned from such phase transitions. However, single crystals are needed for a…
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated…
Equiatomic nickle-titanium (NiTi) is investigated to determine the consequences of point defects on the Martensitic phase transformation. Using molecular dynamics (MD) simulations, NiTi with 0.1%, 0.5%, 1%, 2%, and 3% of Schottky-type…
Changes in temperature or stress state may induce reversible B2$\leftrightarrow$(R)$\leftrightarrow$ B19' martensitic transformations and associated shape memory effects in close-to-stoichiometric nickel-titanium (NiTi) alloys. Recent…
The controlled design of martensitic transformation (MT) to achieve specific properties is crucial for the innovative application of shape memory alloys (SMAs) in advanced technologies. Herein, we explore and design the MT behaviors and the…
Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level accuracy. They can be trained based on high-fidelity…
The identification and use of reversible Martensitic transformations, typically described as shape memory transformations, as a new class of solid-solid phase change material is experimentally demonstrated here for the first time. To prove…
The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is…
Lithium niobate-tantalate mixtures have garnered considerable interest for their ability to merge the desirable properties of both end members, enabling diverse high-value applications, such as high-performance faradaic capacitors,…
Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to…
The work presents a thermomechanical model for polycrystalline NiTi-based shape memory alloys developed within the framework of generalized standard solids, which is able to cover loading-mode dependent localization of the martensitic…