Computational Materials Science最新文献_第7页

Physically interpretable models for predicting lattice parameters, tunnel geometry, and symmetry in hollandite-type materials 预测荷兰石型材料中晶格参数、隧道几何形状和对称性的物理可解释模型

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-09 DOI: 10.1016/j.commatsci.2026.114494

Mingyang Zhao , Zhisen Feng , Bin Lei , Xiao Chen , Zhenfeng Tong

Hollandite-type materials are structurally versatile ceramics used in nuclear waste immobilization, ion transport, and energy storage, yet quantitative and physically interpretable composition-to-structure prediction remains limited. In this work, we develop a suite of interpretable multivariate regression models, benchmarked against machine learning approaches, to predict key crystallographic features of hollandite materials, including lattice constants a and c, tunnel bottleneck size (d-d), and crystallographic symmetry. Using physically motivated descriptors such as ionic-radius mismatch, oxidation states, electronegativity, and tunnel occupancy, we reveal a pronounced directional anisotropy in composition-structure relationships. Lattice constant a is accurately captured by a multivariate linear regression (MLR) model dominated by steric mismatch effects, whereas lattice constant c and d-d require compact multivariate polynomial regression (MPR) models to describe essential nonlinear and interaction effects arising from coupled steric and electronic contributions. A regression-based symmetry model further enables reliable classification of tetragonal and monoclinic phases using physically interpretable descriptors alone. Benchmarking against artificial neural networks (ANN) and support vector regression (SVG) confirms that the interpretable regression models achieve comparable accuracy while offering direct mechanistic insight. The validated regression models are subsequently applied to predict structural features of previously uncharacterized hollandite compositions, including actinide-substituted titanates and Mn- and Sn-based analogues. Overall, this work establishes a data-efficient, mechanism-aware framework for composition-driven structure prediction in tunnel-structured oxides, providing a transferable strategy for accelerated materials design.

荷兰石型材料是一种结构多样的陶瓷，用于核废料固定化、离子传输和能量储存，但定量和物理上可解释的成分-结构预测仍然有限。在这项工作中，我们开发了一套可解释的多元回归模型，以机器学习方法为基准，预测荷兰石材料的关键晶体特征，包括晶格常数a和c，隧道瓶颈尺寸（d-d）和晶体对称性。利用离子半径失配、氧化态、电负性和隧道占用等物理动机描述符，我们揭示了成分-结构关系中明显的方向各向异性。晶格常数a是由空间错配效应主导的多元线性回归（MLR）模型准确捕获的，而晶格常数c和d-d需要紧凑的多元多项式回归（MPR）模型来描述由耦合的空间和电子贡献引起的基本非线性和相互作用效应。基于回归的对称模型进一步实现了四方相和单斜相的可靠分类，仅使用物理可解释的描述符。针对人工神经网络（ANN）和支持向量回归（SVG）的基准测试证实，可解释的回归模型在提供直接机制洞察力的同时达到了相当的准确性。经过验证的回归模型随后被应用于预测以前未表征的荷兰酸盐成分的结构特征，包括锕系取代的钛酸盐和锰基和锡基类似物。总的来说，这项工作为隧道结构氧化物的成分驱动结构预测建立了一个数据高效、机制感知的框架，为加速材料设计提供了一种可转移的策略。

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引用次数: 0

A general LLM-powered text mining framework: Applied to extract high entropy alloys 一个通用的基于llm的文本挖掘框架：用于提取高熵合金

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-09 DOI: 10.1016/j.commatsci.2025.114476

Haolun Yuan , Jun Zeng , Jie Zuo , Xin Wang , Dingguo Xu

In this paper, we present a general framework for automating the information extraction process from materials science literature. Our aim is to meet the increasing demand for large-scale databases in both research and engineering. The text mining part consists of three continuous stages: labeling, extraction, and post-processing, which are all powered by large language models (LLMs). Through these successive stages, the framework enables the extraction of material data from both text and tables. It supports the generation of high-quality databases with only a moderate level of prior knowledge about the extraction targets and minimal coding effort, thereby facilitating the rapid development of data-driven models from the ground up. The Framework was applied to high entropy alloys (HEAs) research papers and constructed a comprehensive database of 5393 records encompassing mechanical properties, phase information, and processing histories. Such a database provides a valuable foundation for investigating process–structure–property relationships in alloys, which may support both mechanistic understanding and data-driven design. To assess the quality of the database, we also trained machine learning models to accurately predict phase and yield strength. Our database of HEAs provides a rich resource for future data-driven design of new alloy materials.

在本文中，我们提出了一个自动化材料科学文献信息提取过程的通用框架。我们的目标是满足研究和工程领域对大规模数据库日益增长的需求。文本挖掘部分包括三个连续的阶段：标记、提取和后处理，这些阶段都由大型语言模型（llm）提供支持。通过这些连续的阶段，框架可以从文本和表中提取重要数据。它支持生成高质量的数据库，只需要对提取目标有一定程度的先验知识和最少的编码工作，从而促进从头开始的数据驱动模型的快速开发。该框架应用于高熵合金（HEAs）的研究论文，构建了包含力学性能、相信息和加工历史的5393条记录的综合数据库。这样的数据库为研究合金的工艺-结构-性能关系提供了有价值的基础，可以支持机理理解和数据驱动设计。为了评估数据库的质量，我们还训练了机器学习模型来准确预测相位和屈服强度。我们的HEAs数据库为未来新型合金材料的数据驱动设计提供了丰富的资源。

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引用次数: 0

Point defect energetics in gallium arsenide, a comprehensive density functional theory study 砷化镓点缺陷能量学，密度泛函理论的综合研究

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-09 DOI: 10.1016/j.commatsci.2025.114474

Stephen P. Fluckey, Christopher N. Sterling, Blas P. Uberuaga, Xiang-Yang Liu

In materials, point defects often control or modify functional properties. To predict the performance of materials intended for application in optoelectronic devices, it is imperative to understand the properties of those point defects. For the first time, all six intrinsic defects of GaAs, a key optoelectronics material, and their charge transition levels are calculated using density functional theory with the HSE06 functional. For comparison, both PBE and

r^{2} SCAN

calculations are also carried out. The HSE06 results are found to be in better agreement with experimental data than previous calculations. The importance of using the exact electron exchange present in hybrid functionals and larger supercells to accurately determine defect levels and ground state defect configurations is demonstrated.

在材料中，点缺陷常常控制或改变功能特性。为了预测用于光电器件的材料的性能，必须了解这些点缺陷的性质。本文首次利用密度泛函理论和HSE06泛函计算了关键光电子材料GaAs的所有6个本征缺陷及其电荷跃迁能级。为了比较，还进行了PBE和r2SCAN计算。发现HSE06的结果比以前的计算更符合实验数据。使用精确的电子交换存在于混合功能和更大的超级电池的重要性，以准确地确定缺陷水平和基态缺陷配置被证明。

引用次数: 0

First-principles study of synergistic CeHf–VO complex defects on phase stability and ferroelectric polarization in HfO2 协同CeHf-VO配合物缺陷对HfO2中相稳定性和铁电极化的第一性原理研究

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-07 DOI: 10.1016/j.commatsci.2026.114487

Min Huang , Yan-Ping Jiang , Zhi-Liang Tong , Xin-Gui Tang , Zhen-Hua Tang , Xiao-Bin Guo , Wen-Hua Li , Yi-Chun Zhou

We use first-principles calculations to investigate the synergistic effects of cerium (Ce) substitution on Hf sites (Ce_Hf) and oxygen vacancies (V_O) on the structural stability, electronic structure, and ferroelectric properties of polymorphic HfO₂. The larger ionic radius of Ce⁴⁺/Ce³⁺ drives lattice expansion and distortion, which reduces the energy offset between the orthorhombic (O) and monoclinic (M) phases, thereby stabilizing the polar O-phase. The presence of Ce_Hf has been shown to reduce the formation energy of oxygen vacancies, promoting the formation of complex defects (Ce_Hf–V_O) with three-coordinated vacancies. Electronic-structure analysis reveals defect states with Ce-4f/O-2p/Hf-5d hybridization; their formation reduces the energy gap between the valence-band maximum and conduction-band minimum, thereby facilitating electron excitation. In terms of ferroelectric properties, while Ce_Hf slightly decreases the spontaneous polarization (P_S), the Ce_Hf–V_O complex partially restores P_S and lowers the polarization-switching barrier from 2.70 eV in pristine HfO₂ to 2.42 eV, a larger reduction than for either defect alone. These results identify a microscopic mechanism by which coupled point defects both stabilize the orthorhombic phase and ease polarization switching, providing guidance for defect-engineered ferroelectric HfO₂.

我们利用第一性原理计算研究了铈（Ce）取代Hf位点（CeHf）和氧空位（VO）对多晶HfO2结构稳定性、电子结构和铁电性能的协同效应。Ce4+/Ce3+较大的离子半径驱动晶格膨胀和畸变，减少了正交(O)相和单斜(M)相之间的能量偏移，从而稳定了极性O相。CeHf的存在降低了氧空位的形成能，促进了具有三配位空位的复合缺陷（CeHf - vo）的形成。电子结构分析揭示了Ce-4f/O-2p/Hf-5d杂化缺陷态；它们的形成减小了价带最大值和导带最小值之间的能隙，从而有利于电子激发。在铁电性能方面，CeHf略微降低了自发极化（PS）， CeHf - vo配合物部分恢复了自发极化（PS），并将极化开关势垒从原始HfO2中的2.70 eV降低到2.42 eV，比单独使用任何一种缺陷都要大。这些结果确定了耦合点缺陷既稳定正交相又易于极化开关的微观机制，为缺陷工程铁电HfO2提供了指导。

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引用次数: 0

Thermodynamic Modeling of plasticity-driven shifts in transformation temperatures of high-temperature shape memory alloys 高温形状记忆合金塑性驱动相变温度变化的热力学建模

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-07 DOI: 10.1016/j.commatsci.2025.114462

Adrien R. Cassagne , Dimitris C. Lagoudas , Jean-Briac le Graverend

Plasticity introduced by pre-straining in thermo-mechanical processes, like cold working, can result in significant changes in the transformation temperatures (TTs) of High-temperature shape memory alloys (HTSMAs). Modeling the shifts of the transformation temperatures is then a crucial stake for potential industrial applications. A continuum thermodynamics approach is proposed to model the shifts due to plasticity. The proposed model, derived from previous studies, estimates the new transformation temperatures of a HTSMA depending on the magnitude of accumulated plastic deformation. A backstress and plastic hardening energy terms are introduced within the expression of the Gibbs energy. These terms are directly expressed as a function of the accumulated plastic deformation. The model is calibrated using experimental data obtained with Differential Scanning Calorimetry (DSC) after compression of samples at room temperature. Assumptions are made regarding the volume fraction of retained martensite following deformation. An optimization of the hardening parameters is achieved to match experimental results. The developed model is able to describe the trends and shifts of TTs in the explored range of plastic deformations. This supports the fact that dissipative internal energies can explain the shifts of the transformation temperatures in severely deformed HTSMAs.

高温形状记忆合金（htsma）的相变温度（TTs）在冷加工等热机械加工过程中由预应变引入的塑性会导致相变温度（TTs）的显著变化。因此，对转化温度的变化进行建模是潜在工业应用的关键利害关系。提出了一种连续介质热力学方法来模拟塑性引起的位移。提出的模型源自先前的研究，根据累积塑性变形的大小估计HTSMA的新转变温度。在吉布斯能表达式中引入了背应力和塑性硬化能。这些项直接表示为累积塑性变形的函数。利用差示扫描量热法（DSC）在室温下压缩样品后获得的实验数据对模型进行校准。对变形后残余马氏体的体积分数作了假设。对硬化参数进行了优化，使其与实验结果相匹配。所建立的模型能够描述塑性变形范围内TTs的变化趋势和变化。这支持了耗散内能可以解释严重变形htsma相变温度变化的事实。

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引用次数: 0

Graph diameter as a topological descriptor for hyperbranched polymers: insights from stochastic simulation of ring-opening multibranching polymerization of glycidol 图直径作为超支化聚合物的拓扑描述符：来自开环多分支聚合的随机模拟的见解

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-07 DOI: 10.1016/j.commatsci.2025.114467

Ákos Szabó

This study investigates the ring-opening multibranching polymerization (ROMBP) of glycidol using stochastic simulation. We analyzed the graph diameter of virtually generated macromolecules and examined how this parameter, denoted as d^math_n, responds to variations in the initial composition of protected (monofunctional) and unprotected (bifunctional) monomers. The results uncover a distinct mathematical relationship between d^math_n and the average degree of branching (DBₐᵥ). It was demonstrated that d^math_n serves as a powerful indicator of the topological features of hyperbranched polymers obtained under different feed conditions. Unlike DBₐᵥ, d^math_n more accurately reflects changes in macromolecular size. These findings establish d^math_n as a reliable topological descriptor, offering new insights into the complex structure-property relationships of hyperbranched polymers.

采用随机模拟的方法研究了甘二醇开环多分支聚合反应。我们分析了虚拟生成的大分子的图直径，并检查了这个参数（表示为dmathn）如何响应受保护（单功能）和不受保护（双功能）单体的初始组成的变化。结果揭示了dmathn和平均分支度之间的独特数学关系（DBᵥ）。结果表明，dmathn是表征不同进料条件下超支化聚合物拓扑结构特征的有力指标。与DB ᵥ不同，dmathn更准确地反映了大分子大小的变化。这些发现确立了dmathn作为一种可靠的拓扑描述符，为超支化聚合物复杂的结构-性质关系提供了新的见解。

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引用次数: 0

Mapping and characterization of surface-dependent electronic properties and morphological changes in the cubic phase of crystalline perovskite CsPbBr3 晶体钙钛矿CsPbBr3立方相表面依赖电子性质和形态变化的制图和表征

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-06 DOI: 10.1016/j.commatsci.2025.114477

Marcos de C. Leite , Gabriel X. Pereira , Lucas M. Farigliano , Gustavo M. Dalpian , Juan Andrés , Amanda F. Gouveia

Lead halide perovskites are attracting considerable interest across a wide range of applications, from gas sensors to energy conversion and utilization. Here, the cubic phase of crystalline perovskite CsPbBr

_{3}

is proposed as a probe to shed light on subtle structural and electronic changes that control surface-dependent electronic properties and morphology, using a computational approach based on density functional theory calculations. We carried out first-principles density functional theory calculations to obtain the surface-dependent properties (band structures, density of states, and surface energies) of low Miller-index (001), (110), and (111) surfaces with different terminations of CsPbBr

_{3}

. Additionally, the atomic arrangements and stability of these surfaces were characterized to provide a close match between experimental field-emission scanning electron microscopy images and computational simulations. We demonstrate a practical application of the Wulff construction by leveraging computed surface energies to determine a complete map of available morphologies that are consistent with experimental observations. Our findings reveal how the exposed surfaces on the morphology influence the electronic properties, elucidating the atomic-level synergy between surface-dependent electronic properties and morphological changes in CsPbBr

_{3}

, and providing a theoretical foundation and design principles for enhancing perovskite stability through surface engineering.

卤化铅钙钛矿在从气体传感器到能量转换和利用的广泛应用中引起了相当大的兴趣。本文采用基于密度泛函理论计算的计算方法，提出了晶体钙钛矿CsPbBr3的立方相作为探针，以揭示控制表面依赖电子性质和形貌的细微结构和电子变化。我们通过第一性原理密度泛函理论计算得到了具有不同末端的CsPbBr3的低米勒指数（001）、（110）和（111）表面的表面依赖性质（能带结构、态密度和表面能）。此外，对这些表面的原子排列和稳定性进行了表征，以提供实验场发射扫描电子显微镜图像和计算模拟之间的密切匹配。我们展示了Wulff结构的实际应用，利用计算表面能来确定与实验观察一致的可用形态的完整地图。本研究揭示了CsPbBr3表面暴露对其电子性能的影响，阐明了CsPbBr3表面依赖的电子性能与形态变化之间的原子水平协同作用，为通过表面工程增强钙钛矿稳定性提供了理论基础和设计原则。

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引用次数: 0

Dendritic growth dynamics and morphology control in the Amplitude-expanded Phase Field Crystal Model with Gaussian colored noise 带高斯彩色噪声的扩幅相场晶体模型中枝晶生长动力学及形貌控制

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-05 DOI: 10.1016/j.commatsci.2026.114488

Chen Li , Wenjun Zhu , Kun Wang , Xiaoping Ouyang

The Amplitude-expanded Phase Field Crystal (APFC) model is employed to systematically investigate the dynamics of dendritic growth under different undercooling conditions and to explore in depth the role of Gaussian colored noise — a ubiquitous phenomenon in both natural and engineering environments — in regulating crystal growth morphology. The results show that the competition between interface energy anisotropy and interface kinetic anisotropy is the key driving force behind the formation of different dendritic morphologies. Gaussian colored noise can selectively amplify perturbations that match the system’s unstable spectrum and control the crystal growth direction, significantly enhancing interfacial instability and enabling control over the complexity of dendritic morphology. For the first time within the APFC framework, a quantitative mapping relationship among noise parameters (intensity, filter width, characteristic wavenumber) interfacial stability, and dendritic morphology has been established. This offers a new theoretical perspective on the interfacial evolution mechanism in non-equilibrium solidification processes and provides a new dimension to control morphology during crystal growth.

采用扩幅相场晶体（APFC）模型系统研究了不同过冷条件下枝晶的生长动力学，并深入探讨了自然和工程环境中普遍存在的高斯有色噪声在调节枝晶生长形态中的作用。结果表明，界面能量各向异性和界面动力学各向异性之间的竞争是形成不同枝晶形态的关键驱动力。高斯彩色噪声可以选择性地放大与系统不稳定谱相匹配的扰动，控制晶体生长方向，显著增强界面不稳定性，实现对枝晶形态复杂性的控制。在APFC框架内，首次建立了噪声参数（强度、滤波器宽度、特征波数）、界面稳定性和枝晶形貌之间的定量映射关系。这为研究非平衡凝固过程界面演化机制提供了新的理论视角，并为控制晶体生长过程中的形貌提供了新的视角。

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引用次数: 0

Tunable magnetic and topological phases in EuMnXBi2 (X=Mn, Fe, Co, Zn) pnictides EuMnXBi2 (X=Mn, Fe, Co, Zn) pnicies的可调磁相和拓扑相

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-05 DOI: 10.1016/j.commatsci.2025.114481

Deep Sagar , Abhishek Sharma , Arti Kashyap

We present a comprehensive density functional theory (DFT) study of the electronic, magnetic, and topological properties of the layered pnictides EuMnXBi₂ (X = Mn, Fe, Co, Zn), focusing in particular on the relatively unexplored Bi-based member of the EuMn₂X₂ family. Unlike the well-studied As-, Sb-, and P-based analogues, we show that EuMn₂Bi₂ stabilizes in a C-type antiferromagnetic ground state with a narrow-gap semiconducting character. Inclusion of spin–orbit coupling (SOC) drives a transition from this trivial antiferromagnetic semiconductor to a Weyl semimetal hosting four symmetry-related Weyl points and robust Fermi arc states. Systematic substitution of Mn with Fe, Co, and Zn further reveals a tunable sequence of magnetic ground states: Fe and Co induce ferrimagnetism with semimetallic behavior, while Zn stabilizes a ferromagnetic semimetal with a large net moment. These findings establish Bi-based EuMnXBi₂ pnictides as a versatile platform where magnetic exchange interactions and band topology can be engineered through SOC and chemical substitution. The complex interplay of magnetic interactions and topological effects in the proposed bulk and doped pnictides opens a promising avenue to explore a wide range of electronic and magnetic phenomena. In particular, this study demonstrates that EuMn₂Bi₂ hosts tunable magnetic and topological phases driven by electron correlations, chemical substitution, and spin–orbit coupling.

我们对层状化合物EuMn2X2 （X = Mn, Fe, Co, Zn）的电子，磁性和拓扑性质进行了全面的密度泛函理论（DFT）研究，特别关注了EuMn2X2家族中相对未被开发的基于bi的成员。与已被充分研究的As-， Sb-和p -基类似物不同，我们发现EuMn2Bi2稳定在具有窄间隙半导体特性的c型反铁磁基态。包含自旋轨道耦合（SOC）驱动从这种平凡的反铁磁半导体转变为具有四个对称相关Weyl点和鲁棒费米弧态的Weyl半金属。系统地用Fe、Co和Zn取代Mn进一步揭示了可调谐的磁性基态序列：Fe和Co诱导具有半金属行为的铁磁性，而Zn稳定具有大净矩的铁磁性半金属。这些发现建立了基于铋的EuMnXBi2 nictides作为一个通用平台，其中磁交换相互作用和能带拓扑可以通过SOC和化学取代来设计。磁性相互作用和拓扑效应的复杂相互作用为探索广泛的电子和磁性现象开辟了一条有前途的途径。特别地，本研究证明了EuMn2Bi2具有可调谐的磁相和拓扑相，由电子相关、化学取代和自旋轨道耦合驱动。

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引用次数: 0

Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning 用机器学习预测共聚物性质的描述符和基于图的分子表示

IF 3.3 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Computational Materials Science

Pub Date : 2026-01-05 DOI: 10.1016/j.commatsci.2025.114475

Elaheh Kazemi-Khasragh , Rocío Mercado , Carlos Gonzalez , Maciej Haranczyk

Copolymers are highly versatile materials with a vast range of possible chemical compositions. By using computational methods for property prediction, the design of copolymers can be accelerated, allowing for the prioritization of candidates with favorable properties. In this study, we utilized two distinct representations of molecular ensembles to predict the seven different physical polymer properties copolymers using machine learning: we used a random forest (RF) model to predict polymer properties from molecular descriptors, and a graph neural network (GNN) to predict the same properties from 2D polymer graphs under both a single- and multi-task setting. To train and evaluate the models, we constructed a data set from molecular dynamic simulations for 140 binary copolymers with varying monomer compositions and configurations. Our results demonstrate that descriptors-based RFs excel at predicting density and specific heat capacities at constant pressure (C_p) and volume (C_v) because these properties are strongly tied to specific molecular features captured by molecular descriptors. In contrast, graph representations better predict expansion coefficients (

γ

,

α

) and bulk modulus (K), which depend more on complex structural interactions better captured by graph-based models. This study underscores the importance of choosing appropriate representations for predicting molecular properties. Our findings demonstrate how machine learning models can expedite copolymer discovery with learnable structure–property relationships, streamlining polymer design and advancing the development of high-performance materials for diverse applications.

共聚物是高度通用的材料，具有广泛的可能的化学成分。通过使用性能预测的计算方法，可以加速共聚物的设计，从而优先考虑具有良好性能的候选材料。在这项研究中，我们利用两种不同的分子集合表示来使用机器学习预测七种不同的物理聚合物性质共聚物：我们使用随机森林（RF）模型来预测分子描述符中的聚合物性质，并使用图神经网络（GNN）来预测单任务和多任务设置下二维聚合物图中的相同性质。为了训练和评估模型，我们构建了140种二元共聚物的分子动力学模拟数据集，这些共聚物具有不同的单体组成和构型。我们的研究结果表明，基于描述符的RFs在预测密度和恒压（Cp）和体积（Cv）下的比热容方面表现出色，因为这些特性与分子描述符捕获的特定分子特征密切相关。相比之下，图表示可以更好地预测膨胀系数（γ, α）和体积模量(K)，这更多地依赖于基于图的模型更好地捕获的复杂结构相互作用。这项研究强调了选择合适的表征来预测分子性质的重要性。我们的研究结果证明了机器学习模型如何通过可学习的结构-性能关系来加速共聚物的发现，简化聚合物设计并推进高性能材料的开发，以适应各种应用。

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引用次数: 0