Approaches to Materials Discovery in Inorganic Solid State Chemistry

无机固体是当今许多技术应用的基础。 金属和准金属是如何结合联合收割机形成这些新的固体（称为“金属间化合物”）的？ 令人惊讶的是，仍然不容易预测元素（特别是金属）如何相互反应，它们采用什么结构，以及它们拥有什么物理性质。 对于某些特定的固体，化学家已经成功地应用策略来“设计”新的化合物，例如操纵构件或识别相关系列中缺失的成员。 因为这些策略依赖于先例，根据定义，他们不能预见令人惊讶或违反直觉的结果。 探索性工作对于扩大我们的知识基础至关重要。 实验方法包括高温反应（高达1000 ℃），X射线衍射，光谱学，物理性质测量和量子计算。 学生在这些不同的技能，这在于化学，物理和材料科学的接口培训，非常适合在先进的材料研究的职业生涯。 随着成分、结构和性质之间的关系变得越来越复杂，最终模式识别技能变得太难处理。 为了补充这些传统方法，我们建议机器学习方法可以作为一种替代方法来发现新材料，更可靠，更快。 使用这两种方法，我们将合成各种金属和边缘金属（如锗，砷和硒）的新化合物。 基于我们过去对开发热电材料的兴趣，这种材料可以将废热转化为电能，我们考虑了几种令人兴奋的新应用。 首先，我们想评估各种砷化物作为潜在的主体结构，其中可以添加少量的磁性原子来产生铁磁半导体，这些铁磁半导体可以用于自旋电子器件，以构建更强大的计算机。 其次，我们正在设计具有特殊结构的硒化物和相关化合物，以便它们可以用作非线性光学材料，用于制造红外激光器，用于传感爆炸物或进行选择性外科手术。 第三，我们希望发现新的有色金属间化合物，这是非常罕见的，但由于其美丽而受到高度重视。 所有这些应用都有可能对许多部门产生重大影响，如能源、信息技术、环境可持续性和国防，这些部门对加拿大至关重要。 我们更广泛的愿景是，这些方法可以推广到其他类型的材料，以便科学家能够将人类的智慧和机器的速度联合收割机结合起来，以更深入地了解自然，解开结构和性能之间隐藏的关系，并加速材料发现。

Inorganic solids form the basis of many of today's technological applications. How do metals and metalloids combine to form these new solids (called “intermetallic compounds”)? Surprisingly, it is still not easy to predict how elements (especially metals) react with each other, what structures they adopt, and what physical properties they possess. For some specific solids, chemists have been successful in applying strategies to “design” new compounds, such as manipulating building blocks or identifying missing members within related series. Because these strategies rely on precedent, by definition, they cannot foresee surprising or counterintuitive results. Exploratory work is essential for expanding our knowledge base. Experimental methods include high-temperature reactions (up to 1000 C), X-ray diffraction, spectroscopy, physical property measurements, and quantum calculations. Students trained in these diverse skills, which lie at the interface of chemistry, physics, and materials science, are well suited for careers in advanced materials research. As relationships between compositions, structures, and properties grow more complex, eventually the pattern recognition skills become too difficult for humans to handle. To complement these traditional approaches, we propose that machine-learning methods may serve as an alternative approach to discover new materials, more reliably and much faster. Using both these approaches, we will synthesize new compounds of various metals and borderline metalloids (such as germanium, arsenic, and selenium). Building on our past interest in developing thermoelectric materials, which can convert waste heat to electricity, we have several exciting new applications in mind for compounds to be targeted. First, we want to evaluate various arsenides as potential host structures in which small amounts of magnetic atoms can be added to give rise to ferromagnetic semiconductors, which could be used in spintronic devices to build much more powerful computers. Second, we are designing selenides and related compounds with special structures so that they can be used as nonlinear optical materials to fabricate infrared lasers for sensing explosives or performing selective surgical procedures. Third, we want to discover new coloured intermetallics, which are exceedingly rare but are highly valued because of their beauty. All of these applications have the potential to significantly impact many sectors, such as energy, information technology, environmental sustainability, and defense, which are of vital interest to Canada. Our broader vision is that these approaches can be generalized to other types of materials so that scientists can combine the wisdom of humans and the speed of machines to gain a deeper understanding of nature, to unravel the hidden relationships between structures and properties, and to accelerate materials discovery.