Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential
Jacob T. Sivak, Saeed S. I. Almishal, Mary K. Caucci, Yueze Tan, Dhiya Srikanth, Matthew Furst, Long-Quin Chen, Christina M. Rost, Jon-Paul Maria, Susan B. Sinnott
High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rock salt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rock salt compositions are correctly resolved, with dozens more compositions awaiting discovery.
Development of a magnetic interatomic potential for cubic anti-ferromagnets: the case of NiO
IT4Innovations VŠB - Technical University of Ostrava（IT4创新与卓越中心 俄斯特拉发理工大学）；Departamento de Física, Universidad de Oviedo（奥维耶多大学物理系）；ASturias RAw Materials Institute (ASRAM), Universidad de Oviedo（奥维耶多大学阿斯特里亚斯原材料研究所）；Department of Condensed Matter Physics Faculty of Mathematics and Physics Charles University（查理大学数学与物理学院凝聚态物理系）
Using a fast and accurate neural network potential we are able to systematically explore the energy landscape of large unit cells of bulk magnesium oxide with the minima hopping method. The potential is trained with a focus on the near-stoichiometric compositions, in particular on suboxides, i.e., Mg$_x$O$_{1-x}$ with $0.50<x<0.60$. Our extensive exploration demonstrates that for bulk stoichiometric compounds, there are several new low-energy rocksalt-like structures in which Mg atoms are octahedrally six--coordinated and form trigonal prismatic motifs with different stacking sequences. Furthe…
Probing Elastic Isotropy in Entropy Stabilized Transition Metal Oxides: Experimental Estimation of Single Crystal Elastic Constants from Polycrystalline Materials
Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
Stewart Blusson Quantum Matter Institute, University of British Columbia（不列颠哥伦比亚大学斯图尔特·布卢森量子物质研究所）；Department of Physics & Astronomy, University of British Columbia（不列颠哥伦比亚大学物理与天文学系）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究院）
Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and, thus, shape their technological applications. Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zi…
Silicon carbide (SiC) is an attractive semiconductor material for applications in power electronic devices. However, fabrication of a high-quality SiC/SiO${}_2$ interface has been a challenge. It is well-known that there is a great difference in oxidation rate between the Si-face and C-face, and that the quality of oxide on the Si-face is greater than that on the C-face. However, the atomistic mechanism of the thermal oxidation of SiC remains to be solved. In this paper, a new Si-C-O interatomic potential was developed to reproduce the kinetics of the thermal oxidation of SiC. Using this newly…
Compositionally complex ceramics (CCCs), including high-entropy ceramics (HECs) as a subclass, offer new opportunities of materials discovery beyond the traditional methodology of searching new stoichiometric compounds. Herein, we establish new strategies of tailoring CCCs via a seamless combination of (1) non-equimolar compositional designs and (2) controlling microstructures and interfaces. Using oxide solid electrolytes for all-solid-state batteries as an exemplar, we validate these new strategies via discovering a new class of compositionally complex perovskite oxides (CCPOs) to show the p…
Development of an Atomic Cluster Expansion potential for iron and its oxides
Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；Interdisciplinary Centre for Advanced Materials Simulations, Ruhr Universität Bochum（波鸿鲁尔大学跨学科高级材料模拟中心）
In order to design next-generation ferroelectrics, a microscopic understanding of their macroscopic properties is critical. One means to achieving an atomistic description of ferroelectric and dielectric phenomena is classical molecular dynamics simulations. Previously, we have shown that interatomic potentials based on the bond valence molecular dynamics (BVMD) method can be used to study structural phase transitions, ferroelectric domain nucleation, and domain wall migration in several perovskite oxides and fixed-composition binary and ternary alloys. Most modern devices, however, use variab…
Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
University of South Carolina（南卡罗来纳大学）；National Laboratory of the Rockies（落基山国家实验室）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Cambridge（剑桥大学）；Max Planck Institute for Polymer Research（马克斯·普朗克聚合物研究所）；University of Toronto（多伦多大学）
The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate, …
Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces
钽、钽氧化物及其异质界面在电子、热管理、催化和生物化学等多个应用领域具有巨大的技术价值。例如，包括金属（Ta）和绝缘氧化物（Ta 2 O 5）的TaOx忆阻器中的局部氧化学计量变化已经被示出导致在十亿个周期内在亚纳秒时间尺度上的快速切换，这与神经形态计算相关。尽管其广泛的重要性，原子尺度的理解的氧化学计量变化跨Ta/TaOx异质界面，如在氧化和氧化物生长的早期阶段，没有得到很好的理解。这主要是由于缺乏一个可变的电荷原子间势模型的钽氧化物，可以准确地描述在金属（Ta）和氧化物（TaOx）的环境，以及在他们的界面的离子相互作用。为了解决这一挑战，我们引入了一个电荷转移离子势（CTIP）模型Ta/Ta氧化物系统的晶格参数，结合能，状态方程，和各种实验观察到的Ta 2 O 5多晶型物的弹性性能的训练。采用遗传算法驱动的单目标全局优化方案，其次是局部单纯形优化，确定最佳的一组CTIP参数。我们新开发的CTIP潜力准确地预测结构，热力学，充满活力的多晶型物，以及弹性和表面性质的Ta和Ta 2 O 5，在良好的协议与DFT计算和实验。我们采用我们的新参数化的CTIP潜力，研究在不同的温度和原子/分子性质的氧化物种的钽氧化的早期阶段。
Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. Accurate prediction of band gaps using firstprinciples methods is time consuming, requiring hybrid functionals, quasi-particle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in …
Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling of complex materials with near first-principles accuracy. For molecular dynamics simulations, accurate energies and interatomic forces are a prerequisite, but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding. Here, we introduce an efficient alternative method for the training of ANN potentials on energy and force information based on an extrapolation of the total energy via a Taylor expansion. By translating the force in…
Observing non-classical nucleation pathways remains challenging in simulations of complex materials with technological interests. This is because it requires very accurate force fields that can capture the whole complexity of their underlying interatomic interactions and an advanced structural analysis. Here, we first report the construction of a machine-learning force field for zinc oxide interactions using the Physical LassoLars Interaction Potentials approach which allows us to be predictive even for untrained structures. Then, we carried out freezing simulations from a liquid and observed …
Conceptual 2D group III nitrides and oxides (e.g., 2D InN and 2D InO) in heterostructures with graphene have been realized by metalorganic chemical vapor deposition (MOCVD). MOCVD is credited with being central to fabrication of established semiconductor materials and by purpose for an advance in emergent semiconductor materials at the 2D limit. A defining characteristic of MOCVD is the employment of metalorganic precursors such as trimethyl-indium, -gallium, and -aluminum, which contain (strong) metal-carbon bonds. Mechanisms that regulate MOCVD processes at the atomic scale are largely unkno…
Machine Learning Guided Discovery of Corundum High Entropy Oxides
Stewart Blusson Quantum Matter Institute, University of British Columbia（不列颠哥伦比亚大学斯图尔特·布森量子物质研究所）；Department of Physics & Astronomy, University of British Columbia（不列颠哥伦比亚大学物理与天文学系）；School of Physical and Chemical Sciences, Queen Mary University of London（伦敦大学玛丽女王学院物理与化学科学学院）；Laboratoire de Physique de la Matière Condensée, CNRS, École polytechnique, Institut Polytechnique de Paris（巴黎理工学院综合理工学院CNRS凝聚态物理实验室）；Department of Chemistry, University of British Columbia（不列颠哥伦比亚大学化学系）
Transparent oxide materials, such as $CuAlO_{2}$, a p-type transparent conducting oxide (TCO), have recently been studied for high temperature thermoelectric power generators and coolers for waste heat. TCO materials are generally low cost and non-toxic. The potential to engineer them through strain and nano-structuring are two promising avenues toward continuously tuning the electronic and thermal properties to achieve high zT values and low cost/kW-hr devices. In this work, the strain-dependent lattice thermal conductivity of 2H $CuAlO_{2}$ is computed by solving the phonon Boltzmann transpo…
