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The question on the dominant driving mechanism (displacive or order-disorder) at each structural phase transition of KNbO3 is investigated by means of molecular dynamics simulations. To this purpose, we first develop a shell model by…
Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable…
Ferroelectric solid solutions usually exhibit giant dielectric response and high piezoelectricity in the vicinity of the morphotropic phase boundary (MPB), where the structural phase transitions between the rhombohedral and the tetragonal…
Simulating finite temperature phase transitions from first-principles is computationally challenging. Recently, molecular dynamics (MD) simulations using machine-learned force fields (MLFFs) have opened a new avenue for finite-temperature…
Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips. Their performance and stability are governed by polarization dynamics and domain…
Machine learning force fields have emerged as promising tools for molecular dynamics (MD) simulations, potentially offering quantum-mechanical accuracy with the efficiency of classical MD. Inspired by foundational large language models,…
Strontium titanate (SrTiO3) is regarded as an essential material for oxide electronics. One of its many remarkable features is subtle structural phase transition, driven by antiferrodistortive lattice mode, from a high-temperature cubic…
Machine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here we develop and use an unsupervised machine learning workflow within a framework of first-principles-based…
Machine learning has proven to be a valuable tool to approximate functions in high-dimensional spaces. Unfortunately, analysis of these models to extract the relevant physics is never as easy as applying machine learning to a large dataset…
Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for…
Molecular-dynamics simulations of KNbO$_3$ reveal preformed dynamic chain-like structures, present even in the paraelectric phase, that are related to the softening of phonon branches over large regions of the Brillouin zone. The phase…
We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of $^{18}$O substitution on the properties of quantum paraelectric SrTiO$_3$ (STO). We find that calculations…
We develop and implement an automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function. Here the…
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic…
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and transformations. Here we show that a machine learning model,…
Composition-temperature phase diagrams are crucial for designing ferroelectric materials, however predicting them accurately remains challenging due to limited phase transformation data and the constraints of conventional methods. Here, we…
Though the electrical responses of the various polymorphs found in ferroelectric polycrystalline thin film HfO$_2$ are now well characterized, little is currently understood of this novel material's grain sub-structure. In particular, the…
Particulate composites underpin many solid-state chemical and electrochemical systems, where microstructural features such as multiphase boundaries and inter-particle connections strongly influence system performance. Advances in X-ray…
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated…
As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in…