相关论文: Phase discovery with active learning: Application …

Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However, DFT is prohibitively expensive for large scale…

The corrosion property of Nickel-titanium (NiTi) shape memory alloy is investigated during forward martensitic transformation between 40C and 0C. The Differential Scanning Calorimetry technique is used to find the forward and reverse…

Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase…

An algorithm was made to study the lattice correspondence involved in phase transformation from cubic B2 to monoclinic B19'. The method is based on studying the orientation matrices generated from EBSD data. Starting from the very…

Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, Machine Learning Interatomic Potentials…

In the field of rehabilitation and neuroscience shape memory alloys play a crucial role as lightweight actuators. Devices are exploiting the shape memory effect by transforming heat into mechanical work. In rehabilitation applications,…

Metastable alloys, such as $\beta$-phase titanium (Ti) alloys with a body-centered cubic (BCC) lattice, can exhibit exceptional mechanical properties through the interplay of multiple deformation mechanisms -- diffusionless phase…

Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by…

Deep learning is revolutionizing many areas of science and technology, especially image, text and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn…

In recent yerars, Bismuth sodium titanate (BNT) and BNT-based piezoelectric materials have proved potential for lead-free piezoelectric technologies. Such materials' complicated phase structure is crucial to their thermo-electromechanical…

We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our…

We present a systematic benchmark of MACE potentials for iron-nickel alloys, focusing on structural, elastic, magnetic, and finite-temperature properties relevant to phase stability. The reference dataset comprises spin-polarized PBE…

Understanding the phase stability of elemental lithium (Li) is crucial for optimizing its performance in lithium-metal battery anodes, yet this seemingly simple metal exhibits complex polymorphism that requires proper accounting for quantum…

Silicon exhibits several metastable allotropes which recently attracted attention in the quest for materials with superior (e.g. optical) properties, compatible with Si technology. In this work we shed light on the atomic-scale mechanisms…

Two unique behaviors of superelasticity and shape memory effect have made shape memory alloy such as NiTi, an interesting alloy for different applications. Recently, additive manufacturing (AM) as a powerful tool for fabricating NiTi has…

Carbon solubility in face-centered cubic Ni-W alloys and the phase diagram of C-Ni-W are investigated by means of first principle calculations and semi-grand canonical Monte Carlo simulations. With density functional theory (DFT) total…

Shape-memory alloys exhibit a solid-to-solid phase transition that involves a temperature-driven rearrangement of their crystal structure and is responsible for their remarkable properties and numerous technological applications. Here, we…

Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in exploring the vast configurational space. The machine-learning…

Phase change materials are exploited in several enabling technologies such as storage class memories, neuromorphic devices and memories embedded in microcontrollers. A key functional property for these applications is the fast crystal…

Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but…