arXiv subjects

Fengbo Yuan

Publications and source records attributed to Fengbo Yuan.

4 recordsLinked to original sources

arXiv2026

dpti: An Automated Thermodynamic Integration Workflow for Phase Diagram Calculations with Machine Learning Interatomic Potentials

Thermodynamic integration (TI) is a widely used approach for computing free energies and phase diagrams. However, TI calculations driven by machine learning interatomic potentials (MLIPs) remain technically challenging because they require careful design of reversible integration paths and many closely related molecular dynamics (MD) tasks for each phase and state point. To address these challenges, we present dpti, an open-source Python package that automates TI workflows for phase diagram calculations with MLIPs. dpti connects reference systems with analytically known free energies to MLIP-described atomic and molecular solids and liquids through reversible integration paths. Given JSON input files, dpti generates and runs the required MD tasks, computes free energy contributions, estimates errors, and propagates coexistence points into phase boundaries. We demonstrate the usage of dpti with two examples driven by Deep Potential models: a silica phase diagram involving beta-quartz, coesite, and melt, and the ice Ih-liquid water phase boundary. dpti provides a useful tool for automated phase diagram calculations of materials modeled by MLIPs.

physics.comp-ph↗

arXiv2026

Atomic-scale theory of robust out-of-plane ferroelectricity in ultrathin films

Ferroelectricity in ultrathin films, characterized by robust switchable out-of-plane polarization, is key to next-generation nanoelectronics. Although the macroscopic theory of ferroelectricity suggests that ferroelectricity is inevitably suppressed as the film thickness decreases, recent studies have demonstrated robust ultra thin-film ferroelectricity, for certain ferroelectric materials, specifically HfO$_2$-based oxides and bismuth-based oxides. In this work, we develop an atomic-scale theoretical framework for understanding ferroelectricity in this limiting regime. By considering the work function of the termination layers of the film, we find that robust ferroelectricity arises from ``self-polarizing'' and ``switchable role of the termination layer'' effects strongly correlated to the ``characteristic structure.'' This theory also provides further insights on the importance of top electrodes in stabilizing ferroelectricity for this class of materials in the ultrathin limit. This work aims to develop a comprehensive theoretical framework for thin-film ferroelectricity, providing fundamental insights that can guide the design of next-generation nanoscale devices.

cond-mat.mtrl-sci↗

arXiv2023

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.

physics.chem-ph↗

arXiv2023

Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy

Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we find the deep potential, which is obtained by training the ab initio data from density functional theory calculations with the state-of-the-art SCAN exchange-correlation functional, is suitable to characterize high-pressure phases of Sn. We systematically validate several structural and elastic properties of the α (diamond structure), β, bct, and bcc structures of Sn, as well as the structural and dynamic properties of liquid Sn. The thermodynamics integration method is further utilized to compute the free energies of the α, β, bct, and liquid phases, from which the deep potential successfully predicts the phase diagram of Sn including the existence of the triple-point that qualitatively agrees with the experiment.

cond-mat.mtrl-sci↗