Related papers: Machine learning Landau free energy potentials
Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active-learner, which is…
A key challenge towards reliable robotic control is devising computational models that can both learn policies and guarantee robustness when deployed in the field. Inspired by the free energy principle in computational neuroscience, to…
In this work we introduce the simplest, lowest-order Landau-like potential for BiFeO$_3$ and La-doped BiFeO$_3$ as an expansion around the paraelectric cubic phase in powers of polarization, FeO$_6$ octahedral rotations and strains. We…
We consider a multiferroic chain with a linear magnetoelectric coupling induced by the electrostatic screening at the ferroelectric/ferromagnet interface. We study theoretically the dynamic ferroelectric and magnetic response to external…
Several heavy fermion materials, including UPt$_3$, exhibit a rapid but gradual rise in the magnetization at a critical field, without an apparent phase transition at any temperature $T>0$, with the possibility of a first order transition…
Materials underpin modern technologies, from energy harvesting, storage, and conversion to information and communication technologies. Their functionality is often governed by the interplay between competing phases, as thermodynamic…
We develop a strategy that integrates machine learning and first-principles calculations to achieve technical accurate predictions of infrared spectra. Specifically, the methodology allows to predict infrared spectra for complex systems at…
We investigate the variation of the spontaneous ferroelectric polarization with epitaxial strain for BaTiO$_3$, PbTiO$_3$, LiNbO$_3$, and BiFeO$_3$ using first principles calculations. We find that while the strain dependence of the…
The effect of layer-by-layer heterostructuring and epitaxial strain on lattice instabilities and related ferroelectric properties is investigated from first principles for the [001]-(PbTiO$_3$)$_1$(PbZrO$_3$)$_1$ superlattice and pure…
A first-principles based effective Hamiltonian is used within a molecular dynamics simulation to study the elastocaloric effect in PbTiO3. It is found that the transition temperature is a linear function of uniaxial tensile stress. Negative…
We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures,…
The Verwey phase transition in magnetite is analyzed on the basis of the Landau theory. The free energy functional is expanded in a series of components belonging to the primary and secondary order parameters. A low-temperature phase with…
Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training…
Machine learning models are increasingly applied in materials science, yet their predictive power is often constrained by data scarcity. Here, we show that accurate predictions can be achieved, even with a limited number of training…
Estimating the free energy, as well as other thermodynamic observables, is a key task in lattice field theories. Recently, it has been pointed out that deep generative models can be used in this context [1]. Crucially, these models allow…
Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted reward function. However, existing approaches either assume access to a…
We discuss a simple phenomenological Landau theory of phase transitions with two coupled single-component order parameters and compare the results with available experimental data. The model corresponds to the case of a ferroic system, in…
Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust…
The discovery of ferroelectricity in HfO$_2$-based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density non-volatile memories. The functional performances…
The Landau theory of 180 degrees domain walls in BaTiO3 type ferroelectric particles is presented. Results of exact description of domain walls in bulk enabled us to formulate variational approach to theory of domain walls in corresponding…