Related papers: Machine learning Landau free energy potentials
Pb-based perovskites are considered to be the most efficient materials for energy harvest. However, real-time application is limited because of their toxicity. As a result, lead-free perovskites that offer similar advantages are potential…
The perovskite oxide EuTiO3 (ETO) has attracted increased scientific interest due to its potential multiferroic properties and magnetic activity above and below its structural phase transition at TS=282K. Various experiments have indirectly…
The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore…
Titanium dioxide has been extensively studied in the rutile or anatase phases, while its high-pressure phases are less well understood, despite that many are thought to have interesting optical, mechanical and electrochemical properties.…
The multifaceted physics of oxides is shaped by their composition and the presence of defects, which are often accompanied by the formation of polarons. The simultaneous presence of polarons and defects, and their complex interactions, pose…
Using data obtained from first-principles calculations, we show that the position of the morphotropic phase boundary (MPB) and transition temperature at MPB in ferroelectric perovskite solutions can be predicted with quantitative accuracy…
In this PhD thesis, we explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience. The free energy principle is a general mathematical theory of the necessary…
We propose a Ginzburg-Landau theory for the elastic properties of shape memory polycrystals. A single crystal elastic free energy for a system that undergoes a square-to-rectangle transformation is generalized to a polycrystal by…
Combining effective Hamiltonian and direct $ab$ $initio$ computation, we obtain the phase diagram of SnTiO$_{3}$ with respect to epitaxial strain and temperature, demonstrating the complex features of the phase diagram and providing insight…
We calculate from first principles the nonlinear piezoelectric response of ferroelectric PbTiO3 for the case of a polarization-enhancing electric field applied along the tetragonal axis. We focus mainly on the case of fixed in-plane lattice…
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…
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…
Quantitative description of finite-temperature properties of displacive ferroelectrics, and in particular the critical behavior, is of fundamental importance to both theory and device design, going beyond the Landau-Ginzburg approach, which…
First-principles based modeling on phonon dynamics and transport using density functional theory and Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for…
Perovskite solid solutions PbHfO3-PbSnO3 offer valuable opportunities for studying the formation mechsnisms of incommensurate phases, owing to the presence of an intermediate (between cubic and incommensurate) phase, which is stabilized in…
Raman spectroscopy is a powerful and nondestructive method that is widely used to study the vibrational properties of solids or molecules. Simulations of finite-temperature Raman spectra rely on obtaining polarizabilities along molecular…
We apply preference learning to the task of language model-guided design of novel structural alloys. In contrast to prior work that focuses on generating stable inorganic crystals, our approach targets the synthesizeability of a specific…
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any…
We study the possibility to apply phenomenological approach to the description of magnetic transitions in UGe$_2$ at ambient pressure with the help of Landau free energy expanded to 8th order in magnetisation. The analysis shows that for…
Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods but do not…