Combined synthesis and computational search for new correlated electronic materials

新型相关电子材料的联合合成和计算搜索

• 批准号: 2713498
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2713498
• 关键词: Combined; synthesis; computational; search; new

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.

由于其广泛的有趣行为，例如超导性，磁性，铁电位，铁电位，光致发光，热电学等。更具体地说，更具体地，perovskites氧化物，具有较大的ABO3结构，可以在较大的B阳离子中固定较大的能力，并且具有较大的能力，并且可以将其固定在较大的框架中，并且具有较大的能力，并且可以使e Emions的框架和氧化物的框架差异，否因此，经过广泛的研究以仔细设计新的功能材料。这些氧化物的库通常是由阳离子的替代而建立的。但是，通过阴离子晶格的变化可以大大扩展阴离子或氧化物化学，这很难通过传统的热力学合成而完成，因为仅形成了最稳定的构型或构型混合物。可以通过动力学控制探索带有拓扑化学反应的阴离子的变化岩石学，这些反应是局部限制在晶格的局部反应。例如，将lasrniruo6还原为lasrniruo4导致形成新颖的Ru2+中心，从而可以在扩展的氧化物框架中研究其电子结构。但是，仅通过实验探索钙钛矿氧化物的化学空间是耗时的。实际上，通过机器学习，已经在研究了广泛的功能材料，从而使以很高的效率探索了巨大的构型景观。例如，机器学习已应用于辅助材料发现，例如预测超导材料的临界温度，铁磁体的居里温度，光伏的电子结构特征等。虽然计算方法已用于研究拓位修改的结构，它们的模拟涉及求解复杂的量子机械方程，需要实现实用的计算量。因此，使用常规方法模拟许多具有大量原子以阐明材料行为的系统非常困难。该项目旨在结合计算建模，包括机器学习和合成，以加快发现这些新的拓扑替代改变的钙钛矿氧化物的发现。最终，这将为创建一个反馈循环提供一个机会，在该循环中，通过从现有数据库中进行机器学习，合成材料并将信息馈回到学习模型中，可以探索无数材料结构。该项目属于EPSRC功能性陶瓷和Inorganics研究领域。