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The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so…
We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with…
Lithium fluoride (LiF) is a critical component for stabilizing lithium metal anode and high-voltage cathodes towards the next-generation high-energy-density lithium batteries. Recent modeling study reported the formation of wurtzite LiF…
We show how to construct Landau-like free energy potentials using a machine-learning approach. For concreteness, we focus on perovskite oxide PbTiO$_{3}$. We work with a training set obtained from Monte Carlo simulations based on an…
We study the finite temperature (FT) phase transitions of two-dimensional (2D) $q$-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We…
Machine-learned (ML) coarse-grained (CG) models are a promising tool for significantly enhancing the efficiency of molecular simulations by systematically removing degrees of freedom while retaining fidelity to the underlying fine-grained…
We investigate Machine-Learned Force Fields (MLFFs) trained on approximate Density Functional Theory (DFT) and Coupled Cluster (CC) level potential energy surfaces for the carbon diamond and lithium hydride solids. We assess the accuracy…
Currently used methods for the description of thermodynamics of ferroelectric thin films (Landau theory or ab initio based Monte-Carlo simulations) are based on an energy expansion in terms of internal degrees of freedom. It was shown that…
Using an effective Hamiltonian parametrized from first principles, Monte Carlo simulations are performed in order to study the piezoelectric response of BaTiO3 in the ferroelectric tetragonal phase as a function of temperature. The effect…
BaTiO3 (BTO) is one of the most interesting classes of perovskite materials. The present study has been complied to explore some physical properties such as mechanical, vibrational, thermo-physical, and temperature dependent thermodynamic…
The rise of machine learning has greatly influenced the field of computational chemistry, and that of atomistic molecular dynamics simulations in particular. One of its most exciting prospects is the development of accurate,…
Machine-learned force fields (MLFFs), especially pre-trained foundation models, are transforming computational materials science by enabling ab initio-level accuracy at molecular dynamics scales. Yet their rapid rise raises a key question:…
An efficient and robust on-the-fly machine learning force field method is developed and integrated into an electronic-structure code. This method realizes automatic generation of machine learning force fields on the basis of Bayesian…
Measurements of the structure of hexagonal RMnO3 (R=rare earths (Ho) and Y) for temperatures significantly above the ferroelectric transition temperature (TFE) were conducted to determine the nature of the transition. The local and long…
With the rapid advancement of machine learning techniques for materials simulations, machine-learned force fields (MLFFs) have become a powerful tool that complements first-principles calculations by enabling high-accuracy molecular…
The majority of magnetoelectric crystals show complex temperature-magnetic field or temperature-pressure phase diagrams with alternating antiferromagnetic incommensurate, magnetoelectric, and commensurate phases. Such phase diagrams occur…
The atomic structure of the most stable reconstructed surface of cuprous oxide (Cu$_{2}$O)(111) surface has been a longstanding topic of debate. In this study, we develop on-the-fly machine-learned force fields (MLFFs) to systematically…
We study a generalization performance of the machine learning (ML) model to predict the atomic forces within the density functional theory (DFT). The targets are the Si and Ge single component systems in the liquid state. To train the…
A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent…
We study the thermodynamics of clean, layered superconductor/ferromagnet nanostructures using fully self consistent methods to solve the microscopic Bogoliubov-deGennes equations. From these self-consistent solutions the condensation free…