High-Throughput Computational Techniques for Discovery of Application-Specific Two-Dimensional Materials

Abstract

Two-dimensional (2D) materials have revolutionized the field of materials science since the successful exfoliation of graphene in 2004. Consequently, the advances in computational science have resulted in massive generic databases for 2D materials, where the structure and the basic properties are predicted using density functional theory (DFT). However, discovering material for a given application from these vast databases is a challenging feat. In this thesis, we have developed various automated high-throughput computational pipelines combining DFT and machine learning (ML) to assess the suitability of 2D materials for specific applications. Methods have also been developed to draw valuable insights into what makes these materials suitable for these applications. The assessed properties include suitability for energy storage in the form of Li-ion battery (LIB) and supercapacitor electrodes, along with high-temperature ferromagnetism and the presence of exotic charge density waves (CDW). The ultra-large surface-to-mass ratio of 2D materials has made them an ideal choice for electrodes of compact LIBs and supercapacitors. We combine explicit-ion and implicit-solvent formalisms to develop high-throughput pipelines and define four descriptors to map “computationally soft” single-Li-ion adsorption to “computationally hard” multiple-Li-ion-adsorbed configuration located at global minima for insight finding and rapid screening. Leveraging this large dataset, we also develop crystal-graph-based ML models for the accelerated discovery of potential candidates. A reactivity test with commercial electrolytes is further performed for wet experiments. Our unique approach, which predicts both Li-ion storage and supercapacitive properties and hence identifies various important electrode materials common to both devices, may pave the way for next-generation energy storage systems. Although there are numerous studies computationally exploring 2D materials as Li-ion battery electrodes, these studies are mostly material-specific, i.e., only a few materials are explored in each of these studies. In our work, however, using the novel descriptor-based technique, we explore thousands of 2D materials for LIB electrode applications. Moreover, to the best of our knowledge, no study has explored these thousands of 2D materials for supercapacitor electrodes yet, which we also achieve. The discovery of 2D ferromagnets with high Curie temperature is challenging since its calculation involves a manually intensive complex process. We develop a Metropolis Monte-Carlo-based pipeline and conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end ML model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. CDW materials are an important subclass of two-dimensional materials exhibiting significant resistivity switching with the application of external energy. We combine a first-principles-based structure-searching technique and unsupervised machine learning to develop a high-throughput pipeline, which identifies CDW phases from a unit cell with an inherited Kohn anomaly. The proposed methodology not only rediscovers the known CDW phases but also predicts a host of easily exfoliable CDW materials (30 materials and 114 phases) along with associated electronic structures. Apart from these, we have also investigated Li-ion storage in distorted rhenium disulfide crystal, polymorphism-driven Li-ion storage of monoelemental 2D materials, and cation intercalation-driven reversible magnetism in ferrous dioxide using global-energy-minima search technique. Our findings could provide useful guidelines for future experimental efforts. All the data, ML models, and computer codes are available freely for community usage. We stress that the automated methodologies/workflows developed in this thesis are as important as the results obtained and generalized enough to be applicable to any 2D materials. The available 2D materials databases are ever-growing, and the workflows introduced by us can aid in the discovery of even better application-specific 2D materials in the future.