衬底感知的二维材料计算设计
Substrate-aware computational design of two-dimensional materials
AI总结：
针对二维材料计算研究常忽略衬底作用的问题，本研究结合进化算法、机器学习势等提出衬底感知的二维材料计算设计方法，在蓝宝石衬底Mo-S体系中发现新结构并预测其合成条件，为衬底工程与实验合成提供支撑。
摘要：
二维材料因其卓越的电子、力学和光学特性受到广泛关注，是下一代电子与光电子应用的首选候选材料。尽管在实际应用（包括制备工艺和最终器件组装）中，二维材料常与衬底结合使用，但为简化计算，相关计算研究往往忽略衬底相互作用的影响。本研究提出一种预测任意衬底上二维材料原子结构的新方法，该方法结合了进化算法、晶格匹配技术、自动化机器学习原子间势训练流程，以及用于预测所获二维结构实验合成可行条件的从头算热力学方法。以c切面蓝宝石衬底上的Mo-S体系为案例研究，我们发现了多种新的稳定与亚稳结构，包括已知的1H-MoS₂，以及新发现的Pmma Mo₃S₂、P-1 Mo₂S、P2₁m Mo₅S₃和P4mm Mo₄S，其中Mo₄S结构因与衬底的相互作用而得以稳定。对Mo₃S₂、Mo₂S、Mo₅S₃和Mo₄S的电子能带结构计算显示它们具有金属性，而声子特性计算表明其具备动力学稳定性，且衬底会调制声子态密度。最后，我们利用从头算热力学方法，在常用的CVD（化学气相沉积）技术参数空间内预测了这些新发现结构的合成条件。这些结果为计算衬底工程提供了见解，有助于研究衬底对二维材料热力学与动力学稳定性的影响，调控其电子和声子特性以支撑未来应用，同时为其实验合成提供指引图谱。
英文摘要：
Two-dimensional materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generation electronic and optoelectronic applications. Despite their widespread use in combination with substrates in practical applications, including the fabrication process and final device assembly, computational studies often neglect the effects of substrate interactions for simplicity. This study presents a novel method for predicting the atomic structure of 2D materials on arbitrary substrates by combining an evolutionary algorithm, a lattice-matching technique, an automated machine learning interatomic potentials training protocol, and the ab initio thermodynamics approach for predicting the possible conditions of experimental synthesis of the predicted 2D structures. Using the Mo-S system on a c-cut sapphire substrate as a case study, we reveal several new stable and metastable structures, including previously known 1H-MoS2 and newly found Pmma Mo3S2, P-1 Mo2S, P21m Mo5S3, and P4mm Mo4S, where the Mo4S structure is specifically stabilized by interaction with the substrate. Electronic band structure calculations of Mo3S2, Mo2S, Mo5S3, and Mo4S showed their metallic behavior, while phonon properties calculations indicated their dynamic stability and substrate-induced modulation of the phonon density of states. Finally, we use the ab initio thermodynamics approach to predict the synthesis conditions of the discovered structures in the parameter space of the commonly used CVD technique. These results provide insights into computational substrate engineering, allowing one to study the substrate effect on the thermodynamic and dynamical stability of 2D materials and to modulate their electronic and phonon properties for their future applications, as well as to provide guide maps for their experimental synthesis.
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