jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuki K. Wakabayashi | Takuma Otsuka | Yoshiharu Krockenberger | Hiroshi Sawada | Yoshitaka Taniyasu | Hideki Yamamoto
Source: npj Computational Materials (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Bayesian optimization with experimental failure for high-throughput materials growth Abstract A crucial problem in achieving innovative high-throughput materials growth with machine learning, such as Bayesian optimization (BO), and automation techniques has been a lack of an appropriate way to handle missing data due to experimental failures. Here, we propose a BO algorithm that complements the missing data in optimizing materials growth parameters. The proposed method provides a flexible optimization algorithm that searches a wide multi-dimensional parameter space. We demonstrate the effectiveness of the method with simulated data as well as in its implementation for actual materials growth, namely machine-learning-assisted molecular beam epitaxy (ML-MBE) of SrRuO 3 , which is widely used as a metallic electrode in oxide electronics. Through the exploitation and exploration in a wide three-dimensional parameter space, while complementing the missing data, we attained tensile-strained SrRuO 3 film with a high residual resistivity ratio of 80.1, the highest among tensile-strained SrRuO 3 films ever reported, in only 35 MBE growth runs. Bayesian optimization ; Throughput ; Molecular beam epitaxy ; Electronics ; Materials science ; Computer science ; Ultimate tensile strength ; Bayesian probability ; Artificial intelligence ; Parameter space ; Automation ; Algorithm ; Nanotechnology ; Epitaxy ; Mechanical engineering ; Composite material ; Engineering ; Mathematics ; Electrical engineering ; Layer (electronics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Takuma Kikutsuji | Yusuke Mori | Kei-ichi Okazaki | Toshifumi Mori | Kang Kim | Nobuyuki Matubayasi
Source: The Journal of Chemical Physics (article, 2022-03-31)
Field: Materials Science — Materials Chemistry

Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI) A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing the product and reactant in complex molecular systems. Recently, abundant research has been devoted to obtaining reaction coordinates using artificial neural networks from deep learning literature, where many collective variables are typically utilized in the input layer. However, it is difficult to explain the details of which collective variables contribute to the predicted reaction coordinates owing to the complexity of the nonlinear functions in deep neural networks. To overcome this limitation, we used Explainable Artificial Intelligence (XAI) methods of the Local Interpretable Model-agnostic Explanation (LIME) and the game theory-based framework known as Shapley Additive exPlanations (SHAP). We demonstrated that XAI enables us to obtain the degree of contribution of each collective variable to reaction coordinates that is determined by nonlinear regressions with deep learning for the committor of the alanine dipeptide isomerization in vacuum. In particular, both LIME and SHAP provide important features to the predicted reaction coordinates, which are characterized by appropriate dihedral angles consistent with those previously reported from the committor test analysis. The present study offers an AI-aided framework to explain the appropriate reaction coordinates, which acquires considerable significance when the number of degrees of freedom increases. Artificial neural network ; Artificial intelligence ; Computer science ; Dipeptide ; Variable (mathematics) ; Isomerization ; Chemistry ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Topic Modeling ; Machine Learning in Bioinformatics
Authors: Qingyang Dong | Jacqueline M. Cole
Source: Scientific Data (article, 2022-05-03)
Field: Materials Science — Materials Chemistry

Auto-generated database of semiconductor band gaps using ChemDataExtractor Large-scale databases of band gap information about semiconductors that are curated from the scientific literature have significant usefulness for computational databases and general semiconductor materials research. This work presents an auto-generated database of 100,236 semiconductor band gap records, extracted from 128,776 journal articles with their associated temperature information. The database was produced using ChemDataExtractor version 2.0, a 'chemistry-aware' software toolkit that uses Natural Language Processing (NLP) and machine-learning methods to extract chemical data from scientific documents. The modified Snowball algorithm of ChemDataExtractor has been extended to incorporate nested models, optimized by hyperparameter analysis, and used together with the default NLP parsers to achieve optimal quality of the database. Evaluation of the database shows a weighted precision of 84% and a weighted recall of 65%. To the best of our knowledge, this is the largest open-source non-computational band gap database to date. Database records are available in CSV, JSON, and MongoDB formats, which are machine readable and can assist data mining and semiconductor materials discovery. Computer science ; Database ; Parsing ; Information retrieval ; JSON ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Li Zhu | Jing Tang | Baichang Li | Tianyu Hou | Yong Zhu | Jiadong Zhou | Zhi Wang | Xiaorong Zhu | Zhenpeng Yao | Xu Cui | Kenji Watanabe | Takashi Taniguchi | Yafei Li | Zheng Han | Wu Zhou | Yuan Huang | Zheng Liu | James Hone | Yufeng Hao
Source: ACS Nano (article, 2022-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures Two-dimensional (2D) materials and their in-plane and out-of-plane (<i>i</i>.<i>e</i>., van der Waals, vdW) heterostructures are promising building blocks for next-generation electronic and optoelectronic devices. Since the performance of the devices is strongly dependent on the crystalline quality of the materials and the interface characteristics of the heterostructures, a fast and nondestructive method for distinguishing and characterizing various 2D building blocks is desirable to promote the device integrations. In this work, based on the color space information on 2D materials' optical microscopy images, an artificial neural network-based deep learning algorithm is developed and applied to identify eight kinds of 2D materials with accuracy well above 90% and a mean value of 96%. More importantly, this data-driven method enables two interesting functionalities: (1) resolving the interface distribution of chemical vapor deposition (CVD) grown in-plane and vdW heterostructures and (2) identifying defect concentrations of CVD-grown 2D semiconductors. The two functionalities can be utilized to quickly identify sample quality and optimize synthesis parameters in the future. Our work improves the characterization efficiency of atomically thin materials and is therefore valuable for their research and applications. Heterojunction ; van der Waals force ; Materials science ; Characterization (materials science) ; Semiconductor ; Interface (matter) ; Chemical vapor deposition ; Artificial neural network ; Nanotechnology ; Optoelectronics ; Computer science ; Artificial intelligence ; Chemistry ; Molecule Ga2O3 and related materials ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Joydeep Munshi | Alexander Rakowski | Benjamin H. Savitzky | Steven E. Zeltmann | Jim Ciston | Matthew Henderson | Shreyas Cholia | Andrew M. Minor | Maria K. Y. Chan | Colin Ophus
Source: npj Computational Materials (article, 2022-12-14)
Field: Materials Science — Materials Chemistry

Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns Abstract A fast, robust pipeline for strain mapping of crystalline materials is important for many technological applications. Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions, but this technique is limited when the electron beam undergoes multiple scattering. Deep-learning methods have the potential to invert these complex signals, but require a large number of training examples. We implement a Fourier space, complex-valued deep-neural network, FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures, orientations, thicknesses, and microscope parameters, which are augmented with experimental artifacts. We evaluated FCU-Net against simulated and experimental datasets, where it substantially outperforms conventional analysis methods. Our code, models, and training library are open-source and may be adapted to different diffraction measurement problems. Diffraction ; Pipeline (software) ; Computer science ; Optics ; Scattering ; Electron diffraction ; Fourier transform ; Artificial intelligence ; Nonlinear system ; Deep learning ; Electron ; Artificial neural network ; Materials science ; Algorithm ; Computational physics ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Mengyang Li | Ruisheng Zhao | Jingshuang Dang | Xiang Zhao
Source: Coordination Chemistry Reviews (article, 2022-08-12)
Field: Chemistry — Organic Chemistry

Theoretical study on the stabilities, electronic structures, and reaction and formation mechanisms of fullerenes and endohedral metallofullerenes Chemistry ; Fullerene ; Computational chemistry ; Nanotechnology ; Chemical physics ; Organic chemistry Fullerene Chemistry and Applications ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Aditya Nandy | Husain Adamji | David W. Kastner | Vyshnavi Vennelakanti | Azadeh Nazemi | Mingjie Liu | Heather J. Kulik
Source: ACS Catalysis (article, 2022-07-15)
Field: Chemistry — Inorganic Chemistry

Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation The challenge of activating inert C–H bonds motivates a study of catalysts that draws from what can be accomplished by natural enzymes and translates these advantageous features into transition-metal complex (TMC) and material mimics. Inert C–H bond activation reactivity has been observed in a diverse number of predominantly iron-containing enzymes from the heme-P450s to nonheme iron α-ketoglutarate-dependent enzymes and methane monooxygenases. Computational studies have played a key role in correlating active-site variables, such as the primary coordination sphere, oxidation state, and spin state, to reactivity. TMCs, zeolites, metal–organic frameworks (MOFs), and single-atom catalysts (SACs) are synthetic inorganic materials that have been designed to incorporate Fe active sites in analogy to single sites in enzymes. In these systems, computational studies have been essential in supporting spectroscopic assignments and quantifying the effects of the metal-local environment on C–H bond reactivity. High-throughput virtual screening tools that have been widely used for bulk metal catalysis do not readily extend to the single-site inorganic catalysts where metal–ligand bonding and localized d-electrons govern reaction energetics. These localized d-electrons can also necessitate wave function theory calculations when density functional theory (DFT) is not sufficiently accurate. Where sufficient computational or experimental data can be gathered, machine learning has helped uncover more general design rules for reactivity or stability. As we continue to investigate metalloprotein active sites, we gain insights that enable us to design stable, active, and selective single-site catalysts. Reactivity (psychology) ; Chemistry ; Catalysis ; Active site ; Density functional theory ; Transition metal ; Ligand (biochemistry) ; Methane monooxygenase ; Oxidation state ; Enzyme catalysis ; Computational chemistry ; Combinatorial chemistry ; Organic chemistry Metal-Catalyzed Oxygenation Mechanisms ; Catalytic C–H Functionalization Methods ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Madushanka Manathunga | Andreas W. Götz | Kenneth M. Merz
Source: Current Opinion in Structural Biology (review, 2022-06-29)
Field: Materials Science — Materials Chemistry

Computer-aided drug design, quantum-mechanical methods for biological problems Computer science ; Quantum ; Field (mathematics) ; Drug discovery ; Quantum chemical ; Popularity ; QM/MM ; Computational science ; Computer engineering ; Nanotechnology ; Molecular dynamics ; Bioinformatics ; Chemistry ; Computational chemistry ; Materials science ; Physics ; Molecule ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Felix Conrad | Mauritz Mälzer | Michael Schwarzenberger | Hajo Wiemer | Steffen Ihlenfeldt
Source: Scientific Reports (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

Benchmarking AutoML for regression tasks on small tabular data in materials design Machine Learning has become more important for materials engineering in the last decade. Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions. Yet, it is not frequently used for small tabular data. Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples. This benchmark addresses these conditions and draws special attention to the overall competitiveness with manual data analysis. Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering. Performance, robustness and usability are discussed in particular. The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. Second, the data sampling for train and test data is of crucial importance for reliable results. Benchmarking ; Computer science ; Robustness (evolution) ; Popularity ; Data science ; Benchmark (surveying) ; Usability ; Machine learning ; Field (mathematics) ; Artificial intelligence ; Data mining ; Biology ; Human–computer interaction ; Cartography Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning and Algorithms
Authors: Wei Chen | Akshay Iyer | Ramin Bostanabad
Source: Engineering (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Data Centric Design: A New Approach to Design of Microstructural Material Systems Building processing, structure, and property (PSP) relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science. Recent technological advancements in data acquisition and storage, microstructure characterization and reconstruction (MCR), machine learning (ML), materials modeling and simulation, data processing, manufacturing, and experimentation have significantly advanced researchers’ abilities in building PSP relations and inverse material design. In this article, we examine these advancements from the perspective of design research. In particular, we introduce a data-centric approach whose fundamental aspects fall into three categories: design representation, design evaluation, and design synthesis. Developments in each of these aspects are guided by and benefit from domain knowledge. Hence, for each aspect, we present a wide range of computational methods whose integration realizes data-centric materials discovery and design. Computer science ; Domain (mathematical analysis) ; Systems engineering ; Perspective (graphical) ; Representation (politics) ; Property (philosophy) ; Data structure ; Data science ; Industrial engineering ; Management science ; Artificial intelligence ; Engineering ; Programming language Machine Learning in Materials Science
Authors: Jingjin He | Xiaopo Su | Changxin Wang | Junjie Li | Yuxuan Hou | Zhonghua Li | Chuanbao Liu | Dezhen Xue | Jiangli Cao | Yanjing Su | Lijie Qiao | Turab Lookman | Yang Bai
Source: Acta Materialia (article, 2022-09-09)
Field: Materials Science — Materials Chemistry

Machine learning assisted predictions of multi-component phase diagrams and fine boundary information Phase diagram ; Phase boundary ; Materials science ; Component (thermodynamics) ; Phase (matter) ; Valence (chemistry) ; Phase transition ; Electronegativity ; Artificial intelligence ; Machine learning ; Statistical physics ; Thermodynamics ; Computer science ; Physics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Multiferroics and related materials
Authors: Qiyuan Zhao | Hsuan‐Hao Hsu | Brett M. Savoie
Source: Journal of Chemical Theory and Computation (article, 2022-04-11)
Field: Materials Science — Materials Chemistry

Conformational Sampling for Transition State Searches on a Computational Budget Transition state searches are the basis for computationally characterizing reaction mechanisms, making them a pivotal tool in myriad chemical applications. Nevertheless, common search algorithms are sensitive to reaction conformations, and the conformational spaces of even medium-sized reacting systems are too complex to explore with brute force. Here, we show that it is possible to train a classifier to learn the features of reaction conformers that conduce successful transition state searches, such that optimal conformers can be down-selected before incurring the cost of a high-level transition state search. The efficacy and transferability of this approach were tested using four distinct benchmarks comprising over three hundred individual reactions. Neglecting conformer contributions led to qualitatively incorrect activation energy estimations for a broad range of reactions, whereas simple random forest classifiers reliably down-selected low-barrier reaction conformers for unseen reactions. The robust performance of these machine learning classifiers mitigates cost as a factor when implementing conformational sampling into contemporary reaction prediction workflows and opens up many avenues for further improvements as transition state data grow. Conformational isomerism ; Computer science ; Transferability ; Random forest ; Classifier (UML) ; Artificial intelligence ; Transition state ; Workflow ; Machine learning ; Chemistry ; Database ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kihyun Lee | Jinsub Park | Soyeon Choi | Yangjin Lee | Sol Lee | Jung Joowon | Jong‐Young Lee | Farman Ullah | Zeeshan Tahir | Yong Soo Kim | Gwan‐Hyoung Lee | Kwanpyo Kim
Source: Nano Letters (article, 2022-06-08)
Field: Materials Science — Materials Chemistry

STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS<sub>2</sub> Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here, we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of MoS<sub>2</sub> from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data. Vacancy defect ; Materials science ; Identification (biology) ; Crystallography ; Chemistry ; Biology 2D Materials and Applications ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: İbrahim Tanrıöver | Doksoo Lee | Wei Chen | Koray Aydın
Source: ACS Photonics (article, 2022-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Generative Modeling and Inverse Design of Manufacturable Free-Form Dielectric Metasurfaces Conventional approaches on design and modeling of metasurfaces employ accurate simulation methods. However, these methods require considerable computational power and time for every simulation, making them computationally expensive in the long run. To address this high computational cost and learn compact yet expressive design representations of high-dimensional meta-atoms for efficient design optimization, deep learning (DL) based approaches have emerged as an alternative solution and numerous applications have been demonstrated in recent years. However, there are still outstanding challenges in DL-assisted modeling and design that need to be overcome, such as limited degrees of design freedom, insufficient generalizability of models, and poor fabrication feasibility of final designs. Here, concurrently addressing these challenges, we propose an end-to-end framework for generative modeling and inverse design of dielectric free-form metasurfaces. The framework is generic, as it can accommodate a variety of physical scenarios including dispersion, incident polarization, and operation wavelength using a single data set and model. We develop a shape generation method to generate an inclusive, free-form, and feasible meta-atom library with manufacturability considerations. A forward model that exhibits improved generalizability in terms of material dispersion, polarization, and spectral window of operation is constructed using neural networks. In the final stage, an inverse design of free-form yet manufacturable metasurfaces is realized. As a proof-of-concept, forward design of a meta-lens and inverse optimization of a polarization filter and a quarter-wave plate are demonstrated. Computer science ; Design for manufacturability ; Generative Design ; Generalizability theory ; Inverse ; Computer engineering ; Engineering design process ; Electronic engineering ; Mechanical engineering ; Engineering ; Metric (unit) ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Acoustic Wave Phenomena Research
Authors: Yong Yang | Hamayun Khan | Shixiong Gao | Abdul Kabir Khalil | Nisar Ali | Adnan Khan | Pau Loke Show | Muhammad Bilal | Hammad Khan
Source: Chemosphere (article, 2022-07-08)
Field: Chemistry — Organic Chemistry

Fabrication, characterization, and photocatalytic degradation potential of chitosan-conjugated manganese magnetic nano-biocomposite for emerging dye pollutants Biocomposite ; Photocatalysis ; Aqueous solution ; Materials science ; Wastewater ; Nuclear chemistry ; Industrial wastewater treatment ; Chitosan ; Degradation (telecommunications) ; Nano- ; Pollutant ; Chemical engineering ; Chemistry ; Environmental engineering ; Composite number ; Catalysis ; Composite material ; Organic chemistry Nanomaterials for catalytic reactions ; Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications