相关论文: Point defect formation at finite temperatures with…

This paper proposes an information theory approach to estimate the number of changepoints and their locations in a climatic time series. A model is introduced that has an unknown number of changepoints and allows for series…

Equilibrium phase transitions may be defined as nonanalytic points of thermodynamic functions, e.g., of the canonical free energy. Given a certain physical system, it is of interest to understand which properties of the system account for…

The calculation of free energies from first principles in materials is a formidable task which enables the prediction of phase stability with high accuracy; these calculations are complicated in magnetic materials by the interplay of…

This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 1011 solid-state compounds. The ML model uses a set of 25…

Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences. In this manuscript, we demonstrate how ML can be used to predict several properties in solid-state chemistry, in…

Machine learning materials properties measured by experiments is valuable yet difficult due to the limited amount of experimental data. In this work, we use a multi-fidelity random forest model to learn the experimental formation enthalpy…

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On…

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…

We present a comprehensive approach to characterizing labyrinthine structures that often emerge as a final steady state in pattern forming systems. We employ advanced machine learning based pattern recognition techniques to identify the…

We report the machine learning (ML)-based approach allowing thermoelectric generator (TEG) efficiency evaluation directly from 5 parameters: 2 physical properties - carriers density and energy gap, and 3 engineering parameters - external…

Phase transitions are ubiquitous across life, yet hard to quantify and describe accurately. In this work, we develop an approach for characterizing generic attributes of phase transitions from very limited observations made deep within…

As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for the accurate and efficient calculation of sublimation…

Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A…

The usefulness of semi-analytical thermal models for predicting the connection between process, microstructure and properties in powder bed fusion has been well illustrated in recent years. Such an approach provides the promise of accuracy…

The calculation of caloric properties such as heat capacity, Joule-Thomson coefficients and the speed of sound by classical force-field-based molecular simulation methodology has received scant attention in the literature, particularly for…

In this work, we present the first extension of the Many-Body Forces (MBF) Model to finite temperature. The MBF Model describes nuclear matter in a relativistic quantum hadrodynamics formalism that takes many-body forces into account…

The prediction of critical heat flux (CHF) using machine learning (ML) approaches has become a highly active research activity in recent years, the goal of which is to build models more accurate than current conventional approaches such as…

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…

We develop here a nonperturbative framework to study quantum chromodynamics (QCD) at finite temperatures using the thermofield dynamics (TFD) method of Umezawa. The methodology considered here is self-consistent and variational. There is a…

We investigate the stability of MnPb$_{2}$Bi$_{2}$Te$_{6}$ (MPBT), which is predicted to be a magnetic topological insulator (TI), using density functional theory calculations. Our analysis includes various measures such as enthalpies of…