Combined synthesis and computational search for new correlated electronic materials

全世界都在努力开发新的过渡金属氧化物，这是由于它们具有广泛的令人感兴趣的行为，例如超导性、磁阻、铁电性、光致发光、热电性等。更具体地说，钙钛矿氧化物具有一般的ABO 3结构，其中大的B阳离子位于A阳离子和氧离子的骨架内，具有适应各种元素的能力和灵活性，因此被广泛研究，以仔细设计新的功能材料。这些氧化物的库通常通过阳离子的取代而建立。然而，阴离子或氧化物化学可以通过阴离子晶格的变化而极大地扩展，这通过常规热力学合成难以实现，因为仅形成最稳定的构型或构型的混合物。可以通过局部化学反应的动力学控制来探索阴离子的化学计量的变化，所述局部化学反应是被晶格局部限制的反应。例如，LaSrNiRuO6还原为LaSrNiRuO4导致形成新的Ru 2+中心，从而可以在扩展的氧化物框架中研究其电子结构。然而，仅仅通过实验探索钙钛矿氧化物的化学空间是耗时的。最近，机器学习已经在研究各种功能材料，从而可以高效地探索广阔的配置景观。例如，机器学习已被应用于辅助材料发现，如超导材料的临界温度，铁磁体的居里温度，光子学的电子结构特征等的预测，而计算方法已被用于研究拓扑化学改性结构，其模拟涉及解决复杂的量子力学方程，需要指数增加计算能力与系统大小。因此，用传统方法模拟具有大量原子的众多系统以阐明材料行为是极其困难的。该项目旨在将包括机器学习和合成在内的联合收割机计算建模相结合，以加快发现这些新的局部化学改变的钙钛矿氧化物。最终，这将提供一个机会来创建一个反馈循环，在这个循环中，通过机器学习从现有数据库中预测新材料，合成材料并将信息反馈到学习模型，从而可以探索无数的材料结构。该项目属于EPSRC功能陶瓷和无机物研究领域的福尔斯。

Worldwide efforts are being made to develop new transition metal oxides due to their wide array of interesting behaviours such as superconductivity, magnetoresistance, ferroelectricity, photoluminescence, thermoelectricity, etc. More specifically, perovskites oxides, having the general ABO3 structure with large B cations sitting inside a framework of A cations and oxygen ions, have the ability and flexibility to accommodate a large variety of elements and are thus widely studied in order to carefully design new functional materials. A library of these oxides is built up generally by the substitution of cations. However, the anion or oxide chemistry can be vastly expanded through changes in the anionic lattice which is difficult to accomplish through conventional thermodynamic synthesis as only the most stable configuration or a mixture of configurations is formed. The varying stoichiometries of anions can be explored through kinetic control with topochemical reactions, which are reactions that are locally confined with crystal lattices. For example, the reduction of LaSrNiRuO6 to LaSrNiRuO4 leads to the formation of novel Ru2+ centers, allowing its electronic structure to be studied in extended oxide frameworks. It is, however, time-consuming to explore the chemical space of perovskite oxides through experimentation only.Recently, a wide range of functional materials have been under study by machine learning, allowing the exploration of vast configuration landscapes with high efficiency. For instance, machine learning has been applied to aid materials discovery such as the prediction of the critical temperature of superconducting materials, Curie temperature of ferromagnets, electronic structure features of photovoltaics, etc. While computational methods have been used to study topochemically modified structures, their simulations involve solving complex quantum mechanical equations, requiring exponentially increasing computational power with system size. Therefore, simulating numerous systems with a large number of atoms to elucidate material behaviour is extremely difficult with conventional methods. This project aims to combine computational modelling including machine learning and synthesis to expedite the discovery of these new topochemically altered perovskite oxides. Ultimately, this will provide an opportunity to create a feedback loop in which a new material is predicted by machine learning from existing databases, synthesising the material and feeding the information back to the learning model allowing the exploration of a myriad of material structures.This project falls within the EPSRC Functional Ceramics and Inorganics research area.