Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials

IF 13.1 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Xin Liu,Anirudh Raju Natarajan
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Abstract

Abstract Multicomponent transition metal carbides are promising for extreme-environment applications, but identifying compositions that are both synthesizable and hard remains challenging. We fine-tune the MACE machine-learned interatomic potential on approximately 28,000 density functional theory calculations spanning the composition space of groups 4–6 transition metals and carbon to predict the thermodynamic stability and elastic properties of multicomponent carbides. The fine-tuned model achieves formation energy errors of ~10 meV/atom for thermodynamically relevant structures with only 20% of the training data. We screen over 1500 equiatomic compositions across rocksalt, hexagonal, and hcp prototypes, combining free energy models with elasticity-based hardness surrogates. Synthesizability predictions at 1500 °C agree well with experimental reports for both single-phase and multiphase carbides. The group number of the constituent metals governs both stability and hardness. Short-range order contributes only a few meV/atom to the free energy, so the disordered phase can be treated as a random solid solution when screening rocksalt carbides at high temperature. For compositions mixing group 4/5 and group 6 metals, we identify a new family of stacking-ordered phases with formation energies well below those of disordered rocksalt or hexagonal structures. DFT calculations corroborate these predictions and suggest that stacking-ordered phases should be experimentally accessible in multicomponent carbides. This study provides a framework for screening synthesizable multicomponent materials with target properties, identifies promising carbide compositions across the full nine-component space, and reveals a new class of stacking-ordered carbides accessible only in multicomponent compositions.
基于机器学习势的多组分过渡金属碳化物的可合成性、硬度和堆叠顺序
多组分过渡金属碳化物在极端环境下的应用前景广阔,但确定既可合成又坚硬的组合物仍然具有挑战性。我们对MACE机器学习的原子间势进行了微调,对大约28000个密度泛函理论计算跨越4-6族过渡金属和碳的组成空间,以预测多组分碳化物的热力学稳定性和弹性性能。该模型仅使用20%的训练数据,就能获得与热力学相关结构的~10 meV/原子的地层能量误差。我们在岩盐、六边形和hcp原型中筛选了超过1500种等原子成分,结合了自由能模型和基于弹性的硬度替代品。在1500°C下的合成能力预测与单相和多相碳化物的实验报告一致。组成金属的族数决定了稳定性和硬度。短程有序对自由能的贡献仅为几个meV/原子,因此在高温筛选岩盐碳化物时,无序相可以作为随机固溶体处理。对于混合4/5族和6族金属的组合物,我们发现了一个新的堆叠有序相族,其地层能量远低于无序岩盐或六边形结构。DFT计算证实了这些预测,并表明在多组分碳化物中应该可以通过实验获得堆叠有序相。该研究为筛选具有目标性能的可合成多组分材料提供了一个框架,在整个九组分空间中确定了有前途的碳化物成分,并揭示了一类新的仅在多组分成分中可获得的堆叠有序碳化物。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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