Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.Comment: 11 pages, 5 figures, 2 table

Pressure-induced Superconductivity and Structure Phase Transition in SnAs-based Zintl Compound SrSn2As2

Layered SnAs-based Zintl compounds exhibit a distinctive electronic structure, igniting extensive research efforts in areas of superconductivity, topological insulators and quantum magnetism. In this paper, we systematically investigate the crystal structures and electronic properties of the Zintl compound SrSn2As2 under high-pressure. At approximately 20.8 GPa, pressure-induced superconductivity is observed in SrSn2As2 with a characteristic dome-like evolution of Tc. Theoretical calculations together with high pressure synchrotron X-ray diffraction and Raman spectroscopy have identified that SrSn2As2 undergoes a structural transformation from a trigonal to a monoclinic structure. Beyond 28.3 GPa, the superconducting transition temperature is suppressed due to a reduction of the density of state at the Fermi level. The discovery of pressure-induced superconductivity, accompanied by structural transitions in SrSn2As2, greatly expands the physical properties of layered SnAs-based compounds and provides a new ground states upon compression.Comment: 15 pages, 6 figures. arXiv admin note: text overlap with arXiv:2307.1562

Chemical Synthesis and Materials Discovery

Functional materials impact every area of our lives ranging from electronic and computing devices to transportation and health. In this Perspective, we examine the relationship between synthetic discoveries and the scientific breakthroughs that they have enabled. By tracing the development of some important examples, we explore how and why the materials were initially synthesized and how their utility was subsequently recognised. Three common pathways to materials breakthroughs are identified. In a small number of cases, such as the aluminosilicate zeolite catalyst ZSM-5, an important advance is made by using design principles based upon earlier work. There are also rare cases of breakthroughs that are serendipitous, such as the buckyball and Teflon(R). Most commonly, however, the breakthrough repurposes a compound that is already known and was often made out of curiosity or for a different application. Typically, the synthetic discovery precedes the discovery of functionality by many decades; key examples include conducting polymers, topological insulators and electrodes for lithium-ion batteries.Comment: 15 pages, two figure

Design, Synthesis and Characterization of New Superconductors

Design and synthesis of new materials are a long-standing goal for chemistry, physics and material science, especially those with intriguing properties such as magnetism and superconductivity. With consideration and incorporation of the highlights in some existing design rules, we successfully designed and discovered the superconductivity in BaPt2Bi2, SrSnP and YbxPt5P. With the help of valence electron counting method, we synthesized a new intermetallic compound, BaIr2Ge2, which was then found to be non-superconducting above 1.8 K. Thus, we considered both valence electron counting and chemical pressure adjustment to reach the superconductivity of BaPt2Bi2 (Tc = 2.0 K) which crystallizes in a structure highly related to the parent compound of one of the high-temperature superconductors, BaFe2As2. According to the bonding analysis, Pt-Pt and Pt-Bi antibonding interactions are believed to be responsible to superconductivity in such system. In order to find more superconductors with Pt-Bi critical charge transfer pair, with the help from adaptive genetic algorithm, we synthesized SrPtBi2 for the first time. Theoretical calculation reveals that Pt-Bi antibonding interaction exists in SrPtBi2 but does not induce superconductivity while few Pt-Pt interaction can be found. Guided by the famous bismuthate superconductor, Ba1-xKxBiO3, we successfully observed the superconductivity in a known compound, SrSnP, at Tc = 2.3 K. The bonding analysis indicates that the Sn-P antibonding interaction and Sr-P bonding interaction are essential in releasing more electrons from Sn atom and, thus, provide more possibilities for electrons to form Cooper pair which is significant for superconductivity based on BCS theory. Due to the fact that there exist many superconducting Pt-rich materials, the ones with Pt-P charge transfer pair were also tested with the success in synthesizing APt8P2 (A = Ca and La) and YbxPt5P. APt8P2 compounds were determined to be non-superconducting above 1.8 K. The bonding analyses for them provided the evidence for structural stability. However, YbxPt5P was observed to be superconducting below Tc = 0.6 K while large heat capacity anomaly attributed to magnetism can be found below Tc which implies the possible coexistence of superconductivity and magnetism

기계학습 퍼텐셜을 이용한 결정구조예측과 그 응용

학위논문(박사) -- 서울대학교대학원 : 공과대학 재료공학부, 2022.2. 한승우.결정구조예측은 주어진 조성에서 가장 안정한 결정구조 상태를 찾는 방법이다. 결정구조예측 방법론을 이용한다면 원리적으로는 물질에 대한 합성 실험 이전에 합성 가능한 물질들의 라이브러리를 모두 수립할 수 있기 때문에 최근 결정구조예측 방법론은 많은 각광을 받고 있다. 하지만, 결정구조예측 방법의 한계는 알고리즘의 효율이 느리다는 것이다. 이는 결정구조예측 알고리즘이 많은 수의 제일원리계산을 동반하기 때문이다. 따라서, 제일원리계산 기반의 결정구조예측 방법은 복잡한 삼성분계 이상의 재료를 대량으로 스크리닝하는 연구들에 거의 사용되지 않고 있다. 이러한 결정구조예측 방법론의 속도를 높이기 위해서 기계학습 퍼텐셜을 제일원리계산의 대체 모델로 사용하려는 시도들이 있다. 하지만, 기계학습 퍼텐셜을 학습하기 위한 학습 데이터셋을 지정하는 것이 어려운데, 그 이유는 결정구조예측의 문제 특성상 시뮬레이션을 하기 전에 어떤 구조가 나올지 미리 알 수 없기 때문이다. 이러한 한계를 극복하기 위해 기존 연구들에서는 무작위 샘플링 방식과 실시간 학습 방식이 사용되어왔다. 하지만 기존의 이러한 방법론들은 삼성분계 이상의 시스템에 적용될 만큼 높은 정확도와 속도를 보이지는 않았다. 본 학위 논문에서는 인공신경망 퍼텐셜을 기반으로 한 결정구조예측 프로그램을 만드는 것을 목표로 하였다. 핵심 아이디어는 분자동역학 계산을 통해 만든 비정질 구조들을 인공신경망 퍼텐셜의 학습 데이터셋으로 사용하는 것이다. 이 학위논문에서는 이렇게 학습된 퍼텐셜로 계산된 에너지는 제일원리계산으로 얻어진 에너지와 높은 상관관계에 있다는 것을 밝혔다. 이는 인공 신경망 모델이 제일원리계산의 대체모델로서 사용될 수 있다는 의미이다. 이러한 인공 신경망 퍼텐셜을 기반으로 결정구조예측 프로그램인 SPINNER를 개발하였다. 프로그램은 실험 구조 데이터베이스와 이론적으로 예측된 구조들에 대하여 테스트 되었으며, 테스트 결과 개발된 방법론은 가장 안정한 결정구조를 합리적인 계산 시간 안에 찾아낼 수 있다는 것을 확인하였다. 개발된 방법론을 사용하여 진행하고 있는 삼성분계의 산화물들과 리튬 고체 전해질에 대한 탐색 연구에 대해 소개하였으며 개발된 프로그램의 한계와 발전 방향에 대하여 논하였다. 본 학위논문에서 개발된 결정구조예측 알고리즘은 우수한 미래 재료 발견으로 이어질 수 있을 것으로 기대된다.Crystal structure prediction aims to find the ground-state structure in a given composition. This is of great interest as it can establish a list of all synthesizable materials prior to experiments. However, the main challenge in predicting crystal structure comes from the efficiency of the algorithm: the NP-hardness of the problem and the high cost of density functional theory, which is employed as a structure optimizer and an energy evaluator, limit the widespread use of the algorithm in searching complex multinary systems. To accelerate the speed of crystal structure prediction, there have been several attempts to employ machine learning potentials as a surrogate model of density functional theory calculations. However, constructing the training set is not straightforward because prior knowledge of the configurations is not available before making predictions. Previous researches employed random sampling and on-the-fly sampling methods to train machine learning potentials but did not achieve enough efficiency and accuracy to be utilized in multinary systems. In this dissertation, we develop the crystal structure prediction program using neural network potentials as the surrogate model of density functional theory calculations. Our main idea is to construct the training set with the disordered structures sampled from molecular dynamics simulations. The energies calculated by trained potentials show a good correlation with the energies calculated by density functional theory calculations, which indicates that the neural network potential can be a hi-fidelity surrogate model for crystal structure prediction. Then, we develop the crystal structure prediction method by optimizing algorithms for constructing training sets, training neural network potentials, and searching structures with evolutionary algorithms. The developed program is tested on the experimental database and theoretical structures predicted by other crystal structure prediction methods. The tests show that the developed method can identify the global minimum in most cases at a reasonable computational cost. Using the developed method, we are now discovering the missing ternary metal oxides and Li superionic conducting oxide materials. By harnessing the accuracy and efficiency of neural network potentials, this dissertation will pave the way to the wide material discoveries in various research fields.Abstract Contents List of Tables List of Figures 1 Introduction 1.1 Overview of crystal structure prediction (CSP) 1.2 Goal of the dissertation 1.3 Organization of the dissertation 2 Theoretical background 2.1 Density functional theory 2.1.1 Born-Oppenheimer approximation 2.1.2 Hohenberg-Kohn theorem 2.1.3 Kohn-Sham equation 2.1.4 Exchange-correlation functional 2.2 Neural network potential (NNP) 2.2.1 Model 2.2.2 Descriptor 2.2.3 Training of NNP 2.3 Crystal structure prediction 2.3.1 Data-mining approaches 2.3.2 Heuristic approaches 2.3.3 Local optimization and energy evaluation 2.3.4 Structure similarity 2.3.5 Advanced techniques on genetic algorithm 3 CSP with machine learning potential 3.1 Training machine learning potential 3.1.1 Melt-quench-annealing simulation 3.1.2 Training NNP 3.1.3 Evaluation of the quality of NNP 3.1.4 Structure searching with NNP 3.2 Developing and optimizing CSP algorithm 3.2.1 Optimization of training procedure 3.2.2 Optimization of global optimization 3.3 Performance test 3.3.1 Blind tests on experimental database 3.3.2 Benchmark test on other CSP methods 3.3.3 Computational cost 3.4 Transfer learning over compositions 4 Applications of CSP 4.1 Synthesizability of missing ternary oxides 4.2 Li superionic solid electrolyte 4.3 Challenges and perspectives 5 Conclusion Bibliography 103 Abstract (In Korean) 108 Acknowlegement 110박