It stands to reason that the amount and the quality of big data is of key
importance for setting up accurate AI-driven models. Nonetheless, we believe
there are still critical roadblocks in the inherent generation of databases,
that are often underestimated and poorly discussed in the literature. In our
view, such issues can seriously hinder the AI-based discovery process, even
when high quality, sufficiently large and highly reputable data sources are
available. Here, considering superconducting and thermoelectric materials as
two representative case studies, we specifically discuss three aspects, namely
intrinsically biased sample selection, possible hidden variables, disparate
data age. Importantly, to our knowledge, we suggest and test a first strategy
capable of detecting and quantifying the presence of the intrinsic data bias.

大数据和 AI 驱动模型的准确性和质量对于建立准确的人工智能模型至关重要。然而，在根本性数据库生成中存在一些关键障碍，这些常常在文献中被低估并且得到很少的讨论。在本文中，我们特别针对超导和热电材料进行了两个代表性案例研究，讨论了三个方面，即内在有偏采样选择，可能的隐藏变量，以及不同的数据年龄。值得注意的是，据我们所知，我们提出并测试了第一个能够检测和量化内在数据偏差的策略。

数据库中一些难以捉摸的方面对基于人工智能的发现的阻碍：以超导材料为例的案例研究

On some elusive aspects of databases hindering AI based discovery: A  case study on superconducting materials

Data-driven approaches for material discovery and design have been
accelerated by emerging efforts in machine learning. While there is enormous
progress towards learning the structure to property relationship of materials,
methods that allow for general representations of crystals to effectively
explore the vast material search space and identify high-performance candidates
remain limited. In this work, we introduce a material discovery framework that
uses natural language embeddings derived from material science-specific
language models as representations of compositional and structural features.
The discovery framework consists of a joint scheme that, given a query
material, first recalls candidates based on representational similarity, and
ranks the candidates based on target properties through multi-task learning.
The contextual knowledge encoded in language representations is found to convey
information about material properties and structures, enabling both similarity
analysis for recall, and multi-task learning to share information for related
properties. By applying the discovery framework to thermoelectric materials, we
demonstrate diversified recommendations of prototype structures and identify
under-studied high-performance material spaces, including halide perovskite,
delafossite-like, and spinel-like structures. By leveraging material language
representations, our framework provides a generalized means for effective
material recommendation, which is task-agnostic and can be applied to various
material systems.

通过使用来自材料科学特定语言模型的自然语言嵌入作为成分和结构特征的表示方法，我们介绍了一种材料发现框架，并将其应用于热电材料，展示了原型结构的多样化推荐和识别出了未充分研究的高性能材料空间，其中包括卤化物钙钛矿、德拉福石和尖晶石结构。