Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows

Structure prediction of crystals, surfaces and nanoparticles

We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue 'Dynamic in situ microscopy relating structure and function'

페리어라이트 제올라이트에서 알루미늄 위치가 디메틸에테르 카르보닐화 반응에 미치는 영향에 대한 제일원리 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2021.8. 김준성.Computational catalysis is one of most fastest growing fields, fueled with the advance in machine learning method and rapid enhancement of computational power, thereby the automation of high throughput screening is achieved. However, this growth is limited by the human understanding level of the catalysis. Especially, Fundamental understanding for heterogeneous catalysis is still not enough to introduce such automation. In current dissertation, the contents consist of 4 parts. In Chapter 1, my motivation for the research is suggested. In Chapter 2, theoretical backgrounds were covered. The summary of density functional theory and theories for calculating catalytic properties are described. In Chapter 3, atomistic simulations for heterogenous catalysis model of dimethyl ether carbonylation reaction in ferrierite zeolite. Especially, the role of Al dopant in zeolite and configurations of adsorbate molecular were focused because zeolites are assemblies of some ring units, which results in the structural complexity and ability to molecular sieves. In Chapter 4, The reaction mechanism of dimethyl ether carbonylation on the active site is suggested and the validation of results is discussed. The whole reaction energies were calculated and the rate determining step was identified. Not only the main reaction paths, but also some side reaction paths were also considered. The results were compared with the literature and discussed.컴퓨터 시뮬레이션 분야는 하드웨어 연산 능력의 급속한 향상에 힘입어 빠르게 성장하고 있으며, 대량 스크리닝 방법을 이용한 높은 수준의 자동화는 새로운 촉매를 설계하는 아주 핵심적인 방법론으로 주목받고 있다. 그러나 이러한 컴퓨터 시뮬레이션을 통한 새로운 촉매 발견 및 설계 연구는 촉매에 대한 인간의 이해 수준에 의해 제한된다. 특히, 이러한 자동화를 완전히 도입하기에는 아직 불균일 촉매에 대한 기본적인 이해가 부족하다. 이러한 맥락에서, 본 논문에서는 제올라이트의 디메틸에테르로부터 메틸 아세테이트까지 이르는 촉매반응에 대한 제일원리 계산을 수행하고 그 결과를 토의하여 촉매 시스템에 대한 기본적인 이해의 폭을 넓히고자 시도하였다. 논문의 내용은 4부로 구성되어 있다. 1장에서는 연구 동기를 제시하며, 제올라이트의 전반적인 연구 동향을 살펴보고, 디메틸에테르의 카르보닐화 반응 및 제일원리 계산을 이용한 다양한 반응모델링의 연구 배경을 살펴본다. 2장에서는 이론적 배경을 다루었다. 밀도 함수 이론의 요약과 촉매 특성 계산 이론을 설명한다. 3장에서는 페리어라이트 제올라이트에서 디메틸 에테르 카르보닐화 반응의 불균일 촉매 모델에 대한 원자 시뮬레이션, 특히 제올라이트에서 알루미늄 도펀트의 역할과 흡착물 분자의 제올라이트 내에서의 형상을 다루었다. 제올라이트가 일부 고리 단위의 집합체이기 때문에 구조적 복잡성과 이 구조적인 복잡성으로 인해 야기되는 흡착 에너지의 변화를 다루었으며, 실험 결과들과 함께 이 내용의 타당성을 토의하였다. 4장에서는 제올라이트 내의 촉매 활성점에서 디메틸 에테르 카르보닐화 반응 메커니즘을 제안하고 결과 검증에 대해 논의하였다. 전체 반응 에너지를 계산하고 속도 결정 단계를 확인했고 타당성을 토의하였다. 주요 반응 경로뿐만 아니라 일부 부반응 경로도 고려했고, 그 결과를 문헌과 비교하고 논의하였다. 본 논문은 제올라이트의 촉매 반응에 대해서 원자수준에서 상세히 그 반응을 살펴봄으로써 가장 근본적인 수준에서 반응에 대한 이해도 뿐만 아니라 기존 실험으로 확인하기 힘들었던 알루미늄 분포에 따른 촉매 활성에 영향도 함께 토의했다는 점에서 그 의의가 있다 하겠다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Catalytic application of zeolites 7 1.3 Computational modeling of catalytic reaction in zeolite 10 Chapter 2 Theoretical backgrounds 15 2.1 Electronic structure calculations 15 2.2 Catalytic properties 17 Chapter 3 Gas-Phase Carbonylation of Dimethyl Ether on the stable Seed-Derived Ferrierite 21 3.1 Introduction 21 3.2 Calculation details 23 3.3 Result and Discussion 24 3.4 Conclusion 40 Chapter 4 Reaction mechanism of DME carbonylation over Ferrierite: First-principles Study 41 4.1 Introduction 41 4.2 Literature reviews 41 4.3 Calculation details 43 4.4 Results and discussion 45 4.5 Conclusion 56 Bibliography 57 국문초록 68박

BIODIESEL PRODUCTION USING SUPPORTED 12-TUNGSTOPHOSPHORIC ACID AS SOLID ACID CATALYSTS

Biodiesel has achieved worldwide recognition for many years due to its renewability, lubricating property, and environmental benefits. The abstract represents a summary of all the chapters of the thesis. The research chapters are defined as research phases in the abstract. The thesis starts with an introduction followed by literature review. In the literature review, all the necessary data were collected reviewing the literature. Then an artificial neural network model (ANN) was built based on the published research data to capture the general trends or to make predictions. Both catalyst properties and reaction conditions were trended and predicted using the network model. The review study revealed that esterification and transesterification required catalysts with slightly different properties. In the first phase of the study, biodiesel production using 12-Tungstophosphric acid (TPA) supported on SBA-15 as a solid acid catalyst was studied. In this phase of the study, a large number of 0-35% TPA on SBA-15 catalysts were synthesized by impregnation method and the effects of various operating conditions such as–catalyst wt.% and methanol to oil molar ratio on the transesterification of model feedstock Triolein were studied. A 25% TPA loading was found to be the optimum. A 4.15 wt.% catalysts (based on Triolein) and 39:1 methanol to Triolein molar ratio was found to be the optimum reaction parameter combination, when the reaction temperature was kept fixed at 200C, stirring speed of 600 rpm and 10 h reaction time. The biodiesel yield obtained using this condition was 97.2%. In the second phase of the study, a 12-Tungstophosphoric acid (TPA) was supported by using organic functional group (i.e. 3-aminopropyltriethoxysilane (APTES)) and was incorporated into the SBA-15 structure. A 45 wt.% TPA incorporated SBA-15 produced an ester with biodiesel yield of 97.3 wt.%, when 3 wt.% catalyst (based on the green seed canola (GSC) oil) and 25.8:1 methanol GSC oil molar ratio were used at 2000C for reaction time of 6.2 h. In the third phase, process sustainability (i.e. process economics, process safety, energy efficiency, environmental impact assessment) studies were conducted based on the results obtained in phase three. Based on the study, it was concluded that heterogeneous acid catalyzed process had higher profitability as compared to the homogeneous acid catalyzed process. Additionally, it was obtained that heterogeneous acid catalyzed process was safe, more energy efficient and more environment friendly than homogenous process. In the fourth phase, the catalytic activity of Tungsten oxide (WO3) and TPA supported (by impregnation) on H-Y, H-β and H-ZSM-5 zeolite catalysts were tested for biodiesel production from Green Seed Canola (GSC) oil. In this phase iii of the study, TPA/H-Y and TPA/H- zeolite were proved to be effective catalysts for esterification and transesterification, respectively. A 55% TPA/H- showed balanced catalytic activity for both esterification and transesterification. It yielded 99.3 wt.% ester, when 3.3 wt.% catalyst (based on GSC oil) and 21.3:1 methanol to GSC oil molar ratio were used at 200C, reaction pressure of 4.14 MPa and reaction time of 6.5 h. Additionally, this catalyst (55% TPA/H-) was experimented for etherification of pure glycerol, and maximum conversion of glycerol (100%) was achieved in 5 h at 120C, 1 MPa, 1:5 molar ratio (glycerol: (tert-butanol) TBA), 2.5% (w/v) catalyst loading. Later, these conditions were used to produce glycerol ether successfully from the glycerol derived after transesterification of green seed canola oil. A mixture of GSC derived biodiesel, and glycerol ether was defined as biofuels. In the fifth phase, catalytic activity of H-Y supported TPA (using different impregnation methods) was studied in details further for esterification of free fatty acid (FFA) of GSC oil. From the optimization study, 97.2% FFA (present in the GSC oil) conversion was achieved using 13.3 wt.% catalyst, 26:1 methanol to FFA molar ratio at 120C reaction temperature and 7.5 h of reaction time. In the sixth- and final phase, techno-economic and ecological impacts were compared between biodiesel and combined biofuel production processes based on the results obtained in phase four. Based on the study, it was concluded that, biodiesel production process had higher profitability as compared to that for combined biofuel production process. Additionally, biodiesel production process was more energy efficient than combined biofuel production process. However, combined biofuel production process was more environment-friendly as compared to that for biodiesel production process

Metal-Organic Frameworks in Germany: from Synthesis to Function

Metal-organic frameworks (MOFs) are constructed from a combination of inorganic and organic units to produce materials which display high porosity, among other unique and exciting properties. MOFs have shown promise in many wide-ranging applications, such as catalysis and gas separations. In this review, we highlight MOF research conducted by Germany-based research groups. Specifically, we feature approaches for the synthesis of new MOFs, high-throughput MOF production, advanced characterization methods and examples of advanced functions and properties

Fabrication, characterization of high-entropy alloys and deep learning-based inspection in metal additive manufacturing

Alloying has been used to confer desirable properties to materials. It typically involves the addition of small amounts of secondary elements to a primary element. In the past decade, however, a new alloying strategy that involves the combination of multiple principal elements in high concentrations to create new materials called high- entropy alloys (HEAs) has been in vogue. In the first part, the investigation focused on the fabrication process and property assessment of the additive manufactured HEA to broaden its engineering applications. Additive manufacturing (AM) is based on manufacturing philosophy through the layer-by-layer method and accomplish the near net-shaped components fabrication. Attempt was made to coat AlCoCrFeNi HEA on an AISI 304 stainless steel substrate to integrate their properties, however, it failed due to the cracks at the interface. The implementation of an intermediate layer improved the bond and eliminated the cracks. Next, an AlCoCrFeNiTi0.5 HEA coating was fabricated on the Ti6Al4V substrate, and its isothermal oxidation behavior was studied. The HEA coating effectively improved the Ti6Al4V substrate\u27s oxidation resistance due to the formation of continuous protective oxides. In the second part, research efforts were made on the deep learning-based quality inspection of additive manufactured products. The traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. A neural-network approach was developed toward robust real-world AM anomaly detection. The results indicate the promising application of the neural network in the AM industry --Abstract, page iv

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine