jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kyle R. Lennon | Gareth H. McKinley | James W. Swan
Source: arXiv (Cornell University) (preprint, 2022-04-20)
Field: Materials Science — Materials Chemistry

A Data-Driven Method for Automated Data Superposition with Applications in Soft Matter Science The superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences. Typically, this superposition is performed manually, or recently by one of a few automated algorithms. However, these methods are often heuristic in nature, are prone to user bias via manual data shifting or parameterization, and lack a native framework for handling uncertainty in both the data and the resulting model of the superposed data. In this work, we develop a data-driven, non-parametric method for superposing experimental data with arbitrary coordinate transformations, which employs Gaussian process regression to learn statistical models that describe the data, and then uses maximum a posteriori estimation to optimally superpose the data sets. This statistical framework is robust to experimental noise, and automatically produces uncertainty estimates for the learned coordinate transformations. Moreover, it is distinguished from black-box machine learning in its interpretability -- specifically, it produces a model that may itself be interrogated to gain insight into the system under study. We demonstrate these salient features of our method through its application to four representative data sets characterizing the mechanics of soft materials. In every case, our method replicates results obtained using other approaches, but with reduced bias and the addition of uncertainty estimates. This method enables a standardized, statistical treatment of self-similar data across many fields, producing interpretable data-driven models that may inform applications such as materials classification, design, and discovery. Interpretability ; Computer science ; Superposition principle ; Parametric statistics ; Data mining ; Machine learning ; Artificial intelligence ; Heuristic ; Data point ; Salient ; Experimental data ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Functional Brain Connectivity Studies
Authors: Deny Haryadi | Sasmi Hidayatul Yulianing Tyas | Adi Kuncoro | Fiqry Firdhan Pratama Putra | Andri Ariyanto
Source: Jurnal Rekayasa Elektrika (article, 2022-12-01)
Field: Chemistry — Inorganic Chemistry

Identifikasi Citra Kualitas Minyak Kelapa Sawit Berbasis Android Menggunakan Algoritma Convolutional Neural Network The Central Statistics Agency reports that the average development of palm cooking oil consumption at the household level in Indonesia during the 2015-2020 period has increased by 2.32% per year. The use of cooking oil repeatedly is commonplace among the people of Indonesia and quite a lot. Even though the use of cooking oil can endanger health because the frying process at high temperatures can damage the chemical structure of the oil. Therefore, in this study, image processing was carried out to identify the quality of palm oil using the Convolutional Neural Network (CNN) algorithm. This research was conducted through several stages, namely dataset collection, dataset preprocessing, CNN algorithm implementation, testing, and development of information systems. The dataset consists of image data of palm cooking oil that has not been used, palm cooking oil used for frying twice, and palm cooking oil used for frying more than twice. The total amount of data is 3000 image data. Distribution of training data and test data using the Pareto division of 80:20. Based on the test, the best accuracy is 97.08%. This research produces an android-based information system that can identify the quality of cooking oil based on the classification. Palm oil ; Convolutional neural network ; Computer science ; Agricultural engineering ; Artificial intelligence ; Environmental science ; Engineering ; Agricultural science Coconut Research and Applications ; Food and Agricultural Sciences ; Natural Products and Applications
Authors: Haoyu Yin | Tan Huang | Xiaonan Wang
Source:  (preprint, 2022-11-06)
Field: Chemistry — Inorganic Chemistry

Discovery of MOFs for Hydrogen Storage via Machine Learning and First Principles Methods Hydrogen energy provides an approach to addressing the energy crisis and global climate change.However, additional energy consumption and carbon emission in the conventional storage process of H2 tackle the hydrogen economy's prosperity.Metalorganic frameworks (MOFs), new type materials with exciting structures and properties, represent a blueprint for realizing large-scale applications of hydrogen energy by lowering energy consumption and cost of facilities.Traditional hydrogen storage MOFs have stepped to an advanced level, and the discovery inevitably slows down.Materials are fundamental to low-cost hydrogen storage, and the screening and design of H2 storage MOFs are crucial for the hydrogen economy.We aim to propose a novel paradigm of hydrogen storage MOFs material design that combines machine learning and first principles calculation, such as density functional theory (DFT).By constructing an active learning framework and using DFT calculation results as training data, a self-improving model that can screen existing material databases and guide experiments design is obtained.The prediction model's performance is examined in conventional ways (root mean square error, coefficient of determination, etc.) and will be further tested in practical considerations (test the performance of MOFs guided by the model). Hydrogen storage ; Blueprint ; Prosperity ; Computer science ; Hydrogen ; Hydrogen economy ; Energy consumption ; Density functional theory ; Process (computing) ; Energy storage ; Hydrogen fuel ; Process engineering ; Engineering ; Mechanical engineering ; Chemistry ; Thermodynamics ; Computational chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: R. M’nassri | Muaffaq M. Nofal | Elham M. A. Dannoun | H. Rahmouni
Source: Applied Physics A (article, 2022-10-23)
Field: Materials Science — Materials Chemistry

Analysis of the electric field dependence on the electrocaloric properties on BaHf0.11Ti0.89O3 ferroelectric ceramics Electric field ; Isothermal process ; Materials science ; Ferroelectricity ; Condensed matter physics ; Ceramic ; Curie temperature ; Pyroelectricity ; Phase transition ; Electrocaloric effect ; Thermodynamics ; Figure of merit ; Heat capacity ; Exponent ; Ferroelectric ceramics ; Physics ; Composite material ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Nikhil Joseph Joy | M K Ranjuna | Jayakumar Balakrishnan
Source: Journal of Physics Condensed Matter (article, 2022-11-02)
Field: Materials Science — Materials Chemistry

A simple and robust machine learning assisted process flow for the layer number identification of TMDs using optical contrast spectroscopy Layered transition metal dichalcogenides (TMDs) like tungsten disulphide (WS<sub>2</sub>) possess a large direct electronic band gap (∼2 eV) in the monolayer limit, making them ideal candidates for opto-electronic applications. The size and nature of the bandgap is strongly dependent on the number of layers. However, different TMDs require different experimental tools under specific conditions to accurately determine the number of layers. Here, we identify the number of layers of WS<sub>2</sub>exfoliated on top of SiO<sub>2</sub>/Si wafer from optical images using the variation of optical contrast with thickness. Optical contrast is a universal feature that can be easily extracted from digital images. But fine variations in the optical images due to different capturing conditions often lead to inaccurate layer number determination. In this paper, we have implemented a simple Machine Learning assisted image processing workflow that uses image segmentation to eliminate this difficulty. The workflow developed for WS<sub>2</sub>is also demonstrated on MoS<sub>2</sub>, graphene and h-BN, showing its applicability across various classes of 2D materials. A graphical user interface is provided to enhance the adoption of this technique in the 2D materials research community. Workflow ; Computer science ; Materials science ; Layer (electronics) ; Contrast (vision) ; Optoelectronics ; Artificial intelligence ; Nanotechnology 2D Materials and Applications ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Rajat Srivastava | Vinay Avasthi | Priya R Krishna
Source: Parallel Processing Letters (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Self-Adaptive Optimization Assisted Deep Learning Model for Partial Discharge Recognition In the power system, research is being conducted in diagnosing and monitoring the condition of power equipment in a precise way. The Partial Discharges (PD) estimations under high voltage is recognized to be the most renowned and useful approach for accessing the electrical behaviour of the insulation material. The PD is good at localizing the dielectric failures even in the smaller regions before the occurrence of the dielectric breakdown. Therefore, the PD condition monitoring with accurate feature specification will be the appropriate model for enhancing the life span of the electrical apparatus. In this research work, a novel data-driven approach is introduced to detect the PD pulses in power cables using optimization based machine learning models. The proposed model will encompass two major phases: feature extraction and recognition. The first phase of the proposed method concentrates on extracting the wavelet scattering transform-based features. In the second phase, these features are fed as the input to optimized Deep Belief Network (DBN), whose count of the hidden neuron is optimized via a Self Adaptive Border Collie Optimization algorithm (SA-BCO). Finally, the performance evaluation is done in terms of diverse performance measures. Partial discharge ; Computer science ; Power (physics) ; Feature (linguistics) ; Feature extraction ; Artificial intelligence ; Phase (matter) ; Deep learning ; Deep belief network ; Wavelet transform ; Pattern recognition (psychology) ; Algorithm ; Voltage ; Machine learning ; Wavelet ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Marina A. Guvakova | Sergueï Sokol
Source: Scientific Reports (article, 2022-11-05)
Field: Chemistry — Spectroscopy

The g3mclass is a practical software for multiclass classification on biomarkers The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output's accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence. Computer science ; Machine learning ; Interpretability ; Software ; Artificial intelligence ; Scalability ; Data mining ; Classifier (UML) ; Inference ; Robustness (evolution) ; Database Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification
Authors: Boris Albrecht | Constantin Dalyac | Lucas Leclerc | Luis Ortiz-Gutiérrez | Slimane Thabet | Mauro D'Arcangelo | Vincent E. Elfving | Lucas Lassablière | Henrique Silvério | Bruno Ximenez | Louis-Paul Henry | Adrien Signoles | Loïc Henriet
Source: arXiv (Cornell University) (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor Using a quantum processor to embed and process classical data enables the generation of correlations between variables that are inefficient to represent through classical computation. A fundamental question is whether these correlations could be harnessed to enhance learning performances on real datasets. Here, we report the use of a neutral atom quantum processor comprising up to $32$ qubits to implement machine learning tasks on graph-structured data. To that end, we introduce a quantum feature map to encode the information about graphs in the parameters of a tunable Hamiltonian acting on an array of qubits. Using this tool, we first show that interactions in the quantum system can be used to distinguish non-isomorphic graphs that are locally equivalent. We then realize a toxicity screening experiment, consisting of a binary classification protocol on a biochemistry dataset comprising $286$ molecules of sizes ranging from $2$ to $32$ nodes, and obtain results which are comparable to those using the best classical kernels. Using techniques to compare the geometry of the feature spaces associated with kernel methods, we then show evidence that the quantum feature map perceives data in an original way, which is hard to replicate using classical kernels. Computer science ; Qubit ; Quantum ; Quantum computer ; Quantum machine learning ; Feature (linguistics) ; Theoretical computer science ; Quantum algorithm ; Kernel (algebra) ; Graph kernel ; Hamiltonian (control theory) ; ENCODE ; Algorithm ; Support vector machine ; Artificial intelligence ; Kernel method ; Mathematics ; Quantum mechanics ; Physics ; Discrete mathematics ; Chemistry ; Polynomial kernel Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Mass Spectrometry Techniques and Applications
Authors: Chun-I Wang | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2022-12-28)
Field: Materials Science — Materials Chemistry

Bringing Quantum Mechanics to Coarse-Grained Soft Materials Modeling Fundamental knowledge gaps are endemic in our understanding of how emergent soft materials properties are linked to the quantum mechanical (QM) world. The limitations of current QM modeling paradigms inhibit the understanding and design of soft materials classes for which QM phenomenology is critical. At its root, these limitations derive from the seemingly innocuous premise of requiring all atomic positions to solve the molecular Schrodinger equation, which necessitates supercomputing resources to incorporate even simple QM phenomenology into small (~nm) soft materials systems. Here, we review emerging efforts to overcome these challenges through the development of electronic prediction models that operate at the coarse-grained resolution. We motivate the origins of this new computational paradigm, denoted Electronic Coarse-Graining (ECG), discuss its relationship to existing molecular modeling frameworks, and describe recent successes of ECG and related models for soft materials. Importantly, we highlight the classes of soft materials where ECG models can be potentially transformative. Computer science ; Phenomenology (philosophy) ; Premise ; Multiscale modeling ; Transformative learning ; Soft materials ; Soft systems methodology ; Granularity ; Statistical physics ; Quantum ; Paradigm shift ; Nanotechnology ; Management science ; Physics ; Quantum mechanics ; Epistemology ; Engineering ; Materials science ; Chemistry ; Sociology ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Surface Chemistry and Catalysis
Authors: Sathiyaraj Chinnasamy | M Ramachandran | Sowmiya Soundharaj | Yihe Zhang | Chunxiao Yu | Paul Chu | Changan Fengzhulv | Junhui Zhang | Rui Ji | Heli Zhang | Wang | Yeng- Shih | L Fong | R Chen | Jeng | Platnieks | Oskars | Andabarkane | Gerda Nikaijudina | Vijay Gaidukova | Sergejsgaidukovs Thakur | K Sekar | Anil Kumar | Priyanka Dahiya | Mohd Anul Haq | S Subiksha | S Sethuvarsha | Rong-Yuan Chen | Wei Zou | Hai-Chen Zhang | Gui-Zhen Zhang | Zhi-Tao Yang | Gang Jin | Jin-Ping Qu | Masaki Harada | Kouji Tsubasaohya | Hideki Iida | Koji Hayashi | Hiroyuki Hirano | Fukuda | N Deepa | Anjum Asmat Parveen | M Khurshid | C Ramachandran | C Sathiyaraj | Vimala | Xiaopeng Yue | Yu Li | Jian Li | Yongjian Xu | Shafiqul Abidin | M Mukherjee | A Datta | D Chakravorty | D Ataur Rahman Farooqi | M R Pallavi | S Ramachandran | Manjula Sowmiya | Selvam | Yan Feng | Da-Wei Hong | Jin-Ping Zhang | He-Zhi Qu | Bai-Ping He | Xu | Manu Kohli | Swati Kohli | Lifang Liu | Jianyong Yu | Longdi Cheng | Weiwei Qu | S Murugan | A Sampathkumar | S Kanaga Suba Raja | S Ramesh | R Manikandan | Deepak Gupta | Shuaicheng Jiang | Yafeng Yang | Shengbo Ge | Zhongfeng Zhang | Wanxi Peng | Wei Zhang | Ying Xu | Pingli Wang | Jian Hong | Jun Liu | Junhui Ji | Fegade | Krishnakumar Vishal | M Gupta | C Madhu | R Sathiyaraj | M Kurinji< | Amudha | Shuidong Zhang | Yan He | Guo Yue Yin
Source:  (article, 2022-08-01)
Field: Materials Science — Polymers and Plastics

Mechanical and Thermal Properties of Poly Butylene Succinct (PBS) Nano Composites The active substance of Catalyst TiCl4 and the microcrystalline is MgCl2. has been Polybutylene composites, Mechanical properties, Thermal properties, Nan composites, Biodegradability, Morphology; these catalysts contain organic compounds belonging to the class of special modifiers, esters or ethers. Pre-catalysts Organo-aluminum alloys and other types of organic or organ metallics are activated by a combination of converters. Two of the most important technologies of catalysts support the advantages. High productivity and high content of crystal isotope polymers are manufactured under stable polymerization conditions at 70-80 C. This product is designed to combine your rainwater tank with washing machine and household appliances Toilet bowl. The polypropylene pipe should be physically protected from direct sunlight to avoid long-term UV decay. Bb pipe is lightweight, it has the advantages of being flexible and easy to install. Polybutene is 1-butane, 2-butane and an organism made from its protein compound is polymer. It is similar to polypropylene (PIB), It is made from pure is protein produced on the C4 premises of a large refinery. Polypropylene ; Materials science ; Butane ; Catalysis ; Composite material ; Microcrystalline ; Polymer ; Biodegradation ; Chemical engineering ; Organic chemistry ; Chemistry Natural Fiber Reinforced Composites
Authors: Guangjing Li | Patricia Poths | Tsugunosuke Masubuchi | Harry W. T. Morgan | Anastassia N. Alexandrova | Scott L. Anderson
Source: ChemRxiv (preprint, 2022-02-16)
Field: Materials Science — Materials Chemistry

Got Coke? Self-Limiting Poisoning Makes an Ultra Stable and Selective Sub-nano Cluster Catalyst Supported sub-nano clusters hold great promise as economical and highly active catalysts. However, they tend to deactivate rapidly by poisoning and sintering, impeding their widespread use. We find that self-limiting poisoning can stabilize and promote cluster catalysis, i.e., poisoning is not always detrimental, but can sometimes be exploited. Specifically, Pt-Ge alloy clusters supported on alumina undergo slow coking (carbon deposition) under conditions of thermal dehydrogenation, yet preserve strong binding sites. For the case of Pt4Ge/alumina, theory shows a number of thermally populated isomers, one of which catalyzes carbon deposition. Because the clusters are fluxional at high temperatures, this isomer acts as a gateway, slowly converting all the clusters to Pt4GeC2. The surprising result is that Pt4GeC2 is highly catalytically active and selective against further coking, i.e., coking produces functional, stable catalytic clusters. Ge and C2 have synergistic electronic effects, leading to efficient and highly selective catalytic dehydrogenation that stops at alkenes, and improving stability. Thus, under reaction conditions, the clusters develop into a robust catalyst, suggesting an approach to practicable cluster catalysis. Dehydrogenation ; Catalysis ; Cluster (spacecraft) ; Limiting ; Coke ; Sintering ; Thermal stability ; Chemical engineering ; Materials science ; Carbon fibers ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Computer science ; Composite number Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Shunsaku Yasumura | Kenichiro Saita | Takumi Miyakage | Kenichi Kon | Takashi Toyao | Zen Maeno | Tetsuya Taketsugu | Ken‐ichi Shimizu
Source: ChemRxiv (preprint, 2022-08-23)
Field: Materials Science — Materials Chemistry

Computational Design of Main-Group Catalysts for Low-Temperature Methane Combustion by Ozone The catalytic combustion of methane (CH4) at a low temperature is becoming increasingly key to controlling unburned CH4 emissions from natural gas vehicles and power plants, although the low activity of benchmark platinum-group-metal (PGM) catalysts hinders its application. Based on the automated reaction route mapping, we designed the main-group catalyst for low-temperature CH4 combustion with ozone (O3). The computational screening of the active site predicted the strong Brønsted acid sites (BASs) as promising ones. We experimentally demonstrated that the catalyst comprising strong BASs exhibited improved CH4 conversion at 200 °C, correlating with the theoretically predicted design concept. The main-group catalyst (proton-type beta zeolite) delivered a reaction rate, which was 442 times higher than that of a benchmark catalyst (5wt% Pd-loaded Al2O3), at 190 °C and exhibited higher tolerance to steam and SO2. Our strategy demonstrated the rational design of earth-abundant catalysts based on automated reaction route mapping. Catalysis ; Methane ; Combustion ; Platinum group ; Zeolite ; Ozone ; Combustor ; Catalytic combustion ; Chemistry ; Metal ; Natural gas ; Platinum ; Chemical engineering ; Environmental science ; Materials science ; Waste management ; Physical chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions