超越理论驱动的发现：热随机搜索与数据衍生结构
Beyond theory driven discovery: hot random search and datum derived structures
AI总结：
该研究基于从头算随机结构搜索，提出两种机器学习加速方法：整合EDDP退火的热AIRSS可高效搜索大晶胞复杂低能结构，基于参考结构的主动学习EDDP生成法可直接衍生多样低能结构，还复原了石榴石结构，为材料结构发现提供新路径。
摘要：
数据驱动方法已彻底改变了计算化学科学的发展前景，其中机器学习原子间势（MLIPs）可将计算速度提升数个数量级。本文反思了与数据驱动发现相对的、基于从头算随机结构搜索（AIRSS，ab initio random structure searching）的理论驱动发现模式，随后介绍了两种利用机器学习加速的方法。本文展示了由临时数据衍生势（EDDPs，ephemeral data derived potentials）实现的、穿插在直接结构弛豫之间的长时长高通量退火，可被整合进AIRSS中，使高难度体系的采样向低能构型偏移。热AIRSS（hot-AIRSS）保留了随机搜索的并行优势，同时可应对复杂得多的体系，这一点通过在大晶胞中搜索复杂硼结构得到了验证。随后本文展示了如何从单一的实验测定金刚石结构直接生成低能碳结构。作为对合理随机结构生成方法的扩展，候选结构被随机生成，随后被优化以最小化EDDP环境向量与参考金刚石结构的环境向量之间的差异。基于距离的代价函数被纳入一个主动学习的EDDP中。使用该势进行的搜索得到了石墨、小纳米管、笼状富勒烯类结构，以及种类丰富的四面体骨架结构。采用相同方法，从一个在更小晶胞、不同化学组成下生成的低能AIRSS结构中，复原出了镁铝榴石Mg₃Al₂(SiO₄)₃的石榴石结构。本文还讨论了该方法与现代基于扩散模型的生成方法之间的关联。
英文摘要：
Data driven methods have transformed the prospects of the computational chemical sciences, with machine learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory driven, as opposed to data driven, discovery based on ab initio random structure searching (AIRSS), and then introduce two methods which exploit machine learning acceleration. I show how long high throughput anneals, between direct structural relaxation, enabled by ephemeral data derived potentials (EDDPs), can be incorporated into AIRSS to bias the sampling of challenging systems towards low energy configurations. Hot AIRSS (hot-AIRSS) preserves the parallel advantage of random search, while allowing much more complex systems to be tackled. This is demonstrated through searches for complex boron structures in large unit cells. I then show how low energy carbon structures can be directly generated from a single, experimentally determined, diamond structure. An extension to the generation of random sensible structures, candidates are stochastically generated and then optimised to minimise the difference between the EDDP environment vector and that of the reference diamond structure. The distance-based cost function is captured in an actively learned EDDP. Graphite, small nanotubes and caged, fullerene-like, structures emerge from searches using this potential, along with a rich variety of tetrahedral framework structures. Using the same approach, the pyrope, Mg$_3$Al$_2$(SiO$_4$)$_3$, garnet structure is recovered from a low energy AIRSS structure generated in a smaller unit cell with a different chemical composition. The relationship of this approach to modern diffusion model based generative methods is discussed.
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