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

Substantia nigral dopamine transporter uptake in dementia with Lewy bodies

Nigrostriatal dopaminergic degeneration is a pathological hallmark of dementia with Lewy bodies (DLB). To identify the subregional dopamine transporter (DAT) uptake patterns that improve the diagnostic accuracy of DLB, we analyzed N-(3-[18F] fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl)-nortropane (FP-CIT) PET in 51 patients with DLB, in 36 patients with mild cognitive impairment with Lewy body (MCI-LB), and in 40 healthy controls (HCs). In addition to a high affinity for DAT, FP-CIT show a modest affinity to serotonin or norepinephrine transporters. Specific binding ratios (SBRs) of the nigrostriatal subregions were transformed to age-adjusted z-scores (zSBR) based on HCs. The diagnostic accuracy of subregional zSBRs were tested using receiver operating characteristic (ROC) curve analyses separately for MCI-LB and DLB versus HCs. Then, the effect of subregional zSBRs on the presence of clinical features and gray matter (GM) density were evaluated in all patients with MCI-LB or DLB as a group. ROC curve analyses showed that the diagnostic accuracy of DLB based on the zSBR of substantia nigra (area under the curve [AUC], 0.90) or those for MCI-LB (AUC, 0.87) were significantly higher than that based on the zSBR of posterior putamen for DLB (AUC, 0.72) or MCI-LB (AUC, 0.65). Lower zSBRs in nigrostriatal regions were associated with visual hallucination, severe parkinsonism, and cognitive dysfunction, while lower zSBR of substantia nigra was associated with widespread GM atrophy in DLB and MCI-LB patients. Taken together, our results suggest that evaluation of nigral DAT uptake may increase the diagnostic accuracy of DLB and MCI-LB than other striatal regions. © 2023, The Author(s).ope

불연속 Galerkin 기법을 이용한 압축성 점성 유동의 고차 해석

Thesis(doctor`s)--서울대학교 대학원 :기계항공공학부,2007.Docto

블록암호알고리즘 DICE 설계

Thesis (master`s)--서울대학교 대학원 :수리과학부,2001.Maste

제올라이트계촉매상에서 나프탈렌류의 선택적인 알킬화반응 강성우.

Maste

Implication of Small Vessel Disease MRI Markers in Alzheimer's Disease and Lewy Body Disease

Background: Small vessel disease (SVD) magnetic resonance imaging (MRI) markers including deep and periventricular white matter hyperintensities (PWMH), lacunes, and microbleeds are frequently observed in Alzheimer's disease (AD) and Lewy body disease (LBD), but their implication has not been clearly elucidated. Objective: To investigate the implication of SVD MRI markers in cognitively impaired patients with AD and/or LBD. Methods: We consecutively recruited 57 patients with pure AD-related cognitive impairment (ADCI), 49 with pure LBD-related cognitive impairment (LBCI), 45 with mixed ADCI/LBCI, and 34 controls. All participants underwent neuropsychological tests, brain MRI, and amyloid positron emission tomography. SVD MRI markers including the severity of deep and PWMH and the number of lacunes and microbleeds were visually rated. The relationships among vascular risk factors, SVD MRI markers, ADCI, LBCI, and cognitive scores were investigated after controlling for appropriate covariates. Results: LBCI was associated with more severe PWMH, which was conversely associated with an increased risk of LBCI independently of vascular risk factors and ADCI. PWMH was associated with attention and visuospatial dysfunction independently of vascular risk factors, ADCI, and LBCI. Both ADCI and LBCI were associated with more lobar microbleeds, but not with deep microbleeds. Conclusion: Our findings suggest that PWMH could reflect degenerative process related with LBD, and both AD and LBD independently increase lobar microbleeds.restrictio