jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Felix Mütter | Vanja Subotić
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

PERFORMANCE OPTIMIZATION OF SOLID OXIDE FUEL CELL OPERATION WITH ARTIFICIAL INTELLIGENCE With their ability to convert chemically bound energy directly into electrical energy, solid oxide fuel cells (SOFCs) are a promising solution to meet the increasing demand for clean and efficient electricity. To accelerate the successful commercialization of this technology, knowledge of the optimal process parameters for efficient, long-term stable and safe operation is of great importance. To reduce the amount of time-consuming and money-consuming experiments required to determine the optimal operating parameters, we used artificial intelligence and combined it with various optimization algorithms to optimize the process parameters of a SOFC. Using a stacked artificial neural network with algorithm-optimized model architecture, we were able to predict the cell voltage of an industrial-scale electrolyte-based SOFC from the cell temperature, gas composition and flow rate on the fuel side, air flow rate, and current. Due to the tremendous prediction speed in the range of milliseconds, a genetic algorithm could be used to optimize the process parameters. The optimization resulted in electric efficiencies >54% and process efficiencies >68% with 80% fuel utilization for operation with methane/steam mixtures and electrical system efficiencies of >66% for operation with hydrogen/steam mixtures and ideal exhaust gas recirculation. Solid oxide fuel cell ; Process engineering ; Process (computing) ; Computer science ; Electric potential energy ; Chemical energy ; Materials science ; Energy (signal processing) ; Engineering ; Electrolyte ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalysis and Oxidation Reactions
Source: Angewandte Chemie International Edition (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

Lukáš Grajciar "The most exciting thing about my research is that it works! Our machine learning models are general and robust enough to 'discover' new chemistry that we have not thought about beforehand, but which often makes a lot of sense a posteriori … I celebrate success by starting to think about what to do next." Find out more about Lukáš Grajciar in his Introducing … Profile. Chemistry Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Yangpeng Huang | Naixing Feng | Yijun Cai
Source: arXiv (Cornell University) (preprint, 2023-07-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Intelligence-Generated Terahertz Multi-Resonant Metasurfaces via Improved Transformer and CGAN Neural Networks It is well known that the inverse design of terahertz (THz) multi-resonant graphene metasurfaces by using traditional deep neural networks (DNNs) has limited generalization ability. In this paper, we propose improved Transformer and conditional generative adversarial neural networks (CGAN) for the inverse design of graphene metasurfaces based upon THz multi-resonant absorption spectra. The improved Transformer can obtain higher accuracy and generalization performance in the StoV (Spectrum to Vector) design compared to traditional multilayer perceptron (MLP) neural networks, while the StoI (Spectrum to Image) design achieved through CGAN can provide more comprehensive information and higher accuracy than the StoV design obtained by MLP. Moreover, the improved CGAN can achieve the inverse design of graphene metasurface images directly from the desired multi-resonant absorption spectra. It is turned out that this work can finish facilitating the design process of artificial intelligence-generated metasurfaces (AIGM), and even provide a useful guide for developing complex THz metasurfaces based on 2D materials using generative neural networks. Terahertz radiation ; Transformer ; Artificial neural network ; Computer science ; Inverse ; Materials science ; Multilayer perceptron ; Artificial intelligence ; Electronic engineering ; Optoelectronics ; Engineering ; Electrical engineering ; Mathematics ; Voltage Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Nooshin Zeinali Galabi | Shipeng Jia | Antranik Jonderian | Gabin Yoon | Sang Bok | Eric McCalla
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Invited) High-Throughput Development of LiCoPO<sub>4</sub> High voltage cathodes are attractive for high energy density Li-ion batteries, particularly with the innovation of solid electrolytes with large stability windows. However, candidates such as LiCoPO 4 have presented numerous problems due to poor electronic/ionic conductivities. Typical solutions involving nanosizing result in extremely poor cycling performance. Herein, we first apply high-throughput methods to develop near-micron sized carbon-coated LiCoPO 4 with improved energy density and capacity retention. 1 In total, 1300 materials with 46 different substituents were synthesized and characterized. A number of substituents showed greatly improved capacity (e.g. 160 mAh/g for 1% In substitution vs. 95 mAh/g for the pristine). However, co-doping was required to improve extended cycling. Li 1-3x Co 1-2x In x Mo x PO 4 was found to be particularly effective with dramatically improved cycling (as high as 100 % after 10 cycles, vs. ~50 % in unsubstituted). While In improved the electronic conductivity of the carbon-coated materials, Mo co-doping gave larger particles and DFT calculations showed that Mo impedes the formation of Li/Co antisite defects. While the above described experiments were solely guided by the researchers intuition, we also explore potential benefits of using machine-learning (ML) algorithms to guide the composition exploration. Random compositions were first prepared to supplement our existing data as part of the training set, then further synthesis was performed based on the ML outputs. The results will be presented in detail to help establish to what extent ML can be used to further leverage our group's high-throughput data. References 1. A. Jonderian, S. Jia, G. Yoon, V.T. Cozea, N. Zeinali Galabi, S.B. Ma, and E. McCalla. Accelerated Development of High Voltage Li-Ion Cathodes. Adv. Energy Mater. 2022, 12, 2201704. Doping ; Electrolyte ; Cathode ; Materials science ; Conductivity ; Leverage (statistics) ; Ionic conductivity ; Ionic bonding ; Energy density ; Cycling ; Intuition ; Chemical engineering ; Ion ; Nanotechnology ; Computer science ; Electrode ; Engineering physics ; Chemistry ; Physical chemistry ; Optoelectronics ; Physics ; Machine learning Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Xiuming Du | Peng Xu | Yong‐Sheng Chen | Han Li | Yuan Li
Source:  (article, 2023-07-27)
Field: Chemistry — Analytical Chemistry

Quantitative Evaluation of DP of Insulating Paper by NIRS Based on Negative Correlation Learning The introduction of NIR spectroscopy for accurate and nondestructive aging condition assessment of insulating paper has important engineering application values. At present, neural network is the mainstream method to establish the correlation model between NIR spectra and the degree of polymerization (DP), but it has the problems of random weights and susceptible to local optimum, and its accuracy and generalization ability are poor in field applications. This paper proposes a neural network integration strategy based on negative correlation learning (NCL) to achieve weak correlation coupling of multiple sub-neural networks, which adding correlation penalty in the cost function to enhance the feature information diversity of neural network integration and improve the model accuracy and generalization ability. The results show that: the appropriate number of sub-neural networks and correlation penalty coefficients can utilize the maximum advantages of negative correlation integration when modeling; compared with a single neural network, negative correlation neural network integration (NCNN) has higher accuracy (RMSE =74.1, MRE=8.0%), it can be used as an alternative method to neural networks for a more precise model. Artificial neural network ; Correlation ; Generalization ; Computer science ; Correlation coefficient ; Feature (linguistics) ; Artificial intelligence ; Algorithm ; Pattern recognition (psychology) ; Biological system ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Industrial Vision Systems and Defect Detection
Authors: Joshua A. Vita | Daniel Schwalbe‐Koda
Source: arXiv (Cornell University) (preprint, 2023-02-12)
Field: Materials Science — Materials Chemistry

Data efficiency and extrapolation trends in neural network interatomic potentials Over the last few years, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in energy/forces errors, improvements in accuracy are still considered the main target when developing new NNIP architectures. In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. Using the 3BPA dataset, we show that test errors in NNIP follow a scaling relation and can be robust to noise, but cannot predict MD stability in the high-accuracy regime. To circumvent this problem, we propose the use of loss landscape visualizations and a metric of loss entropy for predicting the generalization power of NNIPs. With a large-scale study on NequIP and MACE, we show that the loss entropy predicts out-of-distribution error and MD stability despite being computed only on the training set. Using this probe, we demonstrate how the choice of optimizers, loss function weighting, data normalization, and other architectural decisions influence the extrapolation behavior of NNIPs. Finally, we relate loss entropy to data efficiency, demonstrating that flatter landscapes also predict learning curve slopes. Our work provides a deep learning justification for the extrapolation performance of many common NNIPs, and introduces tools beyond accuracy metrics that can be used to inform the development of next-generation models. Extrapolation ; Computer science ; Overfitting ; Normalization (sociology) ; Weighting ; Artificial neural network ; Stability (learning theory) ; Scaling ; Cross entropy ; Entropy (arrow of time) ; Artificial intelligence ; Algorithm ; Machine learning ; Principle of maximum entropy ; Mathematics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Samuel P. Gleason | Deyu Lu | Jim Ciston
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Charge State from EELS Spectra of Third Row Transition Metals Journal Article Machine Learning Prediction of Charge State from EELS Spectra of Third Row Transition Metals Get access Samuel P Gleason, Samuel P Gleason National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United StatesDepartment of Chemistry, University of California, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Deyu Lu, Deyu Lu Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, United States Search for other works by this author on: Oxford Academic Google Scholar Jim Ciston Jim Ciston National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1921–1922, https://doi.org/10.1093/micmic/ozad067.993 Published: 22 July 2023 National laboratory ; Library science ; State (computer science) ; Charge (physics) ; Engineering physics ; Art history ; Chemistry ; Nanotechnology ; Political science ; Physics ; History ; Materials science ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Yang Xiang | Wencheng Wang | Yanxia Zhou
Source: Highlights in Science Engineering and Technology (article, 2023-02-28)
Field: Chemistry — Analytical Chemistry

Composition analysis and identification model of ancient glass products based on Spearman correlation coefficient and BP neural network This paper analyzes the composition of glass products by statistical analysis of known data and then uses Spearman's correlation coefficient analysis model to analyze the composition of glass products, and uses BP neural network to build a prediction and classification model. We use BP neural network to build a prediction and classification model for the composition of glass products and classify the types. In this paper, a BP neural network classification model is constructed using C language. The number of neuron nodes in the input layer and the number of neuron nodes in the output layer are selected. The chemical composition content of the existing sample data is used as the input layer and the category is converted to the output layer after data pre-processing. Finally, a BP neural network classification model with very low classification error is obtained. The sensitivity of the classification results is obtained by sample carryover network calibration and Spearman correlation coefficient. Artificial neural network ; Correlation coefficient ; Spearman's rank correlation coefficient ; Sample (material) ; Computer science ; Artificial intelligence ; Layer (electronics) ; Identification (biology) ; Correlation ; Pearson product-moment correlation coefficient ; Data mining ; Pattern recognition (psychology) ; Statistics ; Mathematics ; Machine learning ; Materials science Spectroscopy and Chemometric Analyses ; Color Science and Applications
Authors: Julija Zavadlav | Sebastien Röcken
Source: Research Square (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

Accurate machine learning force fields via experimental and simulation data fusion Leverage (statistics) ; Experimental data ; Computer science ; Fidelity ; Statistical physics ; Lattice (music) ; Fusion ; Density functional theory ; Artificial intelligence ; Algorithm ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Force Microscopy Techniques and Applications
Authors: Marvelous Ajala | Blessing I. Oko | David Oba-Fidelis | Joycelyn Iyasele | Joy Ifunanya Odimegwu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Pharmaceutical Drug Formulation Drug ; Starch ; Artificial intelligence ; Computer science ; Chemistry ; Medicine ; Pharmacology ; Food science Spectroscopy and Chemometric Analyses
Authors: Jianan Xie | Ji Liu | Chi Zhang | Xihui Chen | Ping Huai | Jie Zheng | Xiaofeng Zhang
Source: arXiv (Cornell University) (preprint, 2023-07-30)
Field: Materials Science — Materials Chemistry

Weakly supervised learning for pattern classification in serial femtosecond crystallography Serial femtosecond crystallography at X-ray free electron laser facilities opens a new era for the determination of crystal structure. However, the data processing of those experiments is facing unprecedented challenge, because the total number of diffraction patterns needed to determinate a high-resolution structure is huge. Machine learning methods are very likely to play important roles in dealing with such a large volume of data. Convolutional neural networks have made a great success in the field of pattern classification, however, training of the networks need very large datasets with labels. Th is heavy dependence on labeled datasets will seriously restrict the application of networks, because it is very costly to annotate a large number of diffraction patterns. In this article we present our job on the classification of diffraction pattern by weakly supervised algorithms, with the aim of reducing as much as possible the size of the labeled dataset required for training. Our result shows that weakly supervised methods can significantly reduce the need for the number of labeled patterns while achieving comparable accuracy to fully supervised methods. Computer science ; Femtosecond ; Convolutional neural network ; Artificial intelligence ; Supervised learning ; Pattern recognition (psychology) ; Diffraction ; Resolution (logic) ; Machine learning ; Artificial neural network ; Data mining ; Laser ; Physics ; Optics X-ray Diffraction in Crystallography
Authors: Ning Wang
Source: Materials Lab (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence in accelerating the development of air electrode materials for proton conducting solid oxide cells Proton conducting solid oxide cells (P-SOCs) operating at intermediate temperature, working in both fuel cell mode for power generation and electrolysis mode for hydrogen production, gain much attention due to their unique advantages. However, the lack of efficient air electrode is the main obstacle to get high-performance P-SOCs, moreover, developing such materials relies on the high-cost and time-waste traditional way. The application of artificial intelligence (AI) to the field of P-SOCs can solve the problems that the traditional way faced. In this perspective, we discussed the current reports relating to the development of air electrode materials of P-SOC by constructing machine learning models. Finally, the future directions of AI guiding the discovery of key materials and high-performance P-SOCs are proposed. Electrolysis ; Computer science ; Electrode ; Oxide ; Obstacle ; Materials science ; Hydrogen production ; Nanotechnology ; Process engineering ; Electrical engineering ; Hydrogen ; Engineering ; Chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Gas Sensing Nanomaterials and Sensors
Authors: Yuan-Sen Ting | Binny Sharma
Source: arXiv (Cornell University) (preprint, 2023-06-25)
Field: Chemistry — Analytical Chemistry

Weisfeiler-Lehman Graph Kernel Method: A New Approach to Weak Chemical Tagging Stars' chemical signatures provide invaluable insights into stellar cluster formation. This study utilized the Weisfeiler-Lehman (WL) Graph Kernel to examine a 15-dimensional elemental abundance space. Through simulating chemical distributions using normalizing flows, the effectiveness of our algorithm was affirmed. The results highlight the capability of the WL algorithm, coupled with Gaussian Process Regression, to identify patterns within elemental abundance point clouds correlated with various cluster mass functions. Notably, the WL algorithm exhibits superior interpretability, efficacy and robustness compared to deep sets and graph convolutional neural networks and enables optimal training with significantly fewer simulations (O(10)), a reduction of at least two orders of magnitude relative to graph neural networks. Interpretability ; Computer science ; Graph ; Convolutional neural network ; Kernel (algebra) ; Artificial intelligence ; Gaussian ; Robustness (evolution) ; Regression ; Algorithm ; Pattern recognition (psychology) ; Theoretical computer science ; Mathematics ; Statistics ; Discrete mathematics ; Chemistry ; Computational chemistry Spectroscopy and Chemometric Analyses
Authors: Enrico Brugnolotto | Preslav Aleksandrov | Marilyne Sousa | Vihar Georgiev
Source:  (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

Machine Learning based Nanowire Classification method based on Nanowire Array Scanning Electron Microscope Images &lt;p&gt;This article introduces an innovative classification methodology for identifying nanowires within scanning electron microscope images. Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays. The method’s capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model’s proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall. Such a high level of performance and accuracy of the ML methods demonstrate the viability of our technique for not only academic but also practical commercial implementation and usage.&lt;/p&gt; Nanowire ; Scanning electron microscope ; Materials science ; Categorization ; Artificial intelligence ; Chemical vapor deposition ; Nanotechnology ; Pattern recognition (psychology) ; Computer science ; Optoelectronics Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Cell Image Analysis Techniques
Authors: Ryosuke Nishitsuji | Daiki Kawasaki | Tomoharu Nakashima | Kenji Sueyoshi | Hideaki Hisamoto | Tatsuro Endo
Source: Research Square (preprint, 2023-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Investigation of multivariate analysis of surface-enhanced Raman scattering spectra using simple machine-learning models: Prediction of the composition of mixed self-assembled monolayer on gold surface Raman scattering ; Surface (topology) ; Monolayer ; Multivariate statistics ; Simple (philosophy) ; Materials science ; Raman spectroscopy ; Composition (language) ; Spectral line ; Multivariate analysis ; Artificial intelligence ; Analytical Chemistry (journal) ; Computer science ; Nanotechnology ; Machine learning ; Chemistry ; Optics ; Mathematics ; Physics ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Chemometric Analyses