jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hasan Murat Aydoğdu | Ömer Hakan ÇİLOĞLU | İrem EFE | Gamze ULU | Göksel Doğan
Source: Çanakkale Onsekiz Mart University AVESIS (article, 2023-11-26)
Field: Chemistry — Inorganic Chemistry

Renk Değişiminin Bağımsız Değişkenlerle İncelenmesi: Akrilik Kaide Materyali, Solüsyon ve Zaman Amaç: Bu çalışmanın amacı Trakya bölgesine has 3 farklı geleneksel içecekte bekletilmiş farklı markalardaki sıcak ve soğuk akriliklerin zamana bağlı renk değişimini incelemektir. Materyal ve Methot: Birbirinden farklı bir tip soğuk akrilik, iki tip sıcak akrilik olmak üzere 3 grup (Meliodent Rapid Repair,Imıcryl IQ15, Duracryl Heat Cure) oluşturuldu. 3 farklı içeceğe(karadut suyu, erik suyu, hayıt suyu) ilave olarak kontrol grubu olarak distile su kullanıldı. Her grup için 40 adet, toplamda 120 adet 20mm çapında 2mm kalınlığında silindirik modeller hazırlandı. Hazırlanan modeller 48 saat süreyle distile suda bekletilip 'Konica Minolta' kromametre kullanılarak ilk ölçümleri yapıldıktan sonra içeceklere yerleştirildi. 7. gün ve 21. günlerde de ölçümler yapılarak değişim kaydedildi. Farklı tipteki akriliklerin renk değişimi ölçümleri yapılırken CIEDE2000 formülü kullanılarak ΔE00 değerleri hesaplandı. Verilerin değerlendirilmesinde bir istatistik yazılımı (SPSS ((Statistical Package for the Social Sciences)) 26.0 )kullanılmıştır. Renk farkları tek yönlü 'ANOVA' ve 'Kruskal-Wallis' testleri ile ve içecekler arasındaki farklar 'multiple pairwise comparison' testleri ile değerlendirilmiş ve 'Bonferroni' düzeltmesi uygulanmıştır. Tüm analizlerde istatistiksel anlam derecesi olarak p=0,05 belirlenmiştir. Bulgular: Farklı içeceklerde bekletilen modellerden elde edilen ΔE00 değerleri arasında istatistiksel olarak anlamlılık saptandı. İçecekler arası karşılaştırmada 'Imıcryl IQ15' ile hazırlanan örneklerde yedinci gün sonunda farklı içeceklerin sebep olduğu renk değişimleri arasında fark bulunmuştur. Erik suyunda bekletilen örneklerdeki renk değişimi distile suda bekletilen örneklere göre anlamlı ölçüde yüksektir(p=0,002). Yirmibirinci günde karadut suyunda bekletilen 'Duracryl Heat Cure' örneklerinde ''Meliodent Rapid Repair'' ’e göre anlamlı derecede daha fazla renk değişimi tespit edilmiştir(p=0.018). Sonuç: Renkte meydana gelen değişim, kullanılan akrilik materyalinin tipine , akrilik modellerin içecekler içinde beklediği süreye, içeceğin tipine göre değişiklik gösterebilmektedir. Yedinci ve yirmibirinci günlerde tüm içeceklerde ΔE00 renk değişimi değerleri gözle algılanabilecek sınırın altında(1.72) olarak ölçülmüştür<br> Significant difference ; Statistical analysis Pigment Synthesis and Properties ; Cultural Heritage Materials Analysis ; Dyeing and Modifying Textile Fibers
Authors: Bouanane, Ibtissem
Source: theses.fr (ABES) (article, 2023-07-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Absorbing metasurface in the visible and infrared bands : design of nanostructures with controlled optical and electrical properties La conception de méta-surfaces absorbantes dans les domaines du visible et de l'infrarouge s'avère très intéressante pour différentes applications telles que le solaire thermique et photovoltaïque, les photodétecteurs ou encore les capteurs. L'opportunité d'utiliser des nanostructures artificielles pour réaliser ces nouveaux matériaux a suscité un énorme intérêt au cours des dernières décennies. Leurs propriétés optiques uniques permettent d'envisager un contrôle de l'interaction lumière-matière, pour des longueurs d'onde optiques spécifiques contrôlables par le design de la structure et ses paramètres géométriques.Parmi les différentes nanostructures existantes, les résonateurs métalliques à anneau fendu sont des méta-matériaux bien connus offrant des possibilités uniques de contrôle de la réponse aux composantes électriques et magnétiques de la lumière dans les bandes visibles et infrarouges. Leur forme circulaire leur confère, en plus des résonances à caractère électrique, des résonances magnétiques induites optiquement en raison de leur forme. Les champs électriques et magnétiques peuvent alors provoquer une exaltation des propriétés d'absorption à la longueur d'onde de résonance. De plus, les nano-anneaux fendus offrent des possibilités uniques pour contrôler avec précision la réponse lumineuse en fonction de leurs paramètres géométriques variés.L'objet de cette thèse est l'étude théorique et expérimentale d'une méta-surface absorbante, composée de nano-anneaux métalliques aux paramètres géométriques réalistes (vis-à-vis de contraintes de fabrication) et qui doit prendre la forme d'un revêtement souple et flexible. Dans un premier temps, une étude paramétrique réalisée à l'aide d'outils de simulation numérique permet l'optimisation de la structure. Au cours de ce travail, deux approches expérimentales ont été définies pour la réalisation. La première est une méthode classique descendante réalisée par lithographie électronique et métallisation qui a permis de valider le modèle de simulation. La seconde est une approche ascendante innovante pour la fabrication de surfaces nanophotoniques à partir de nanocubes colloïdaux auto-assemblés. Cette seconde méthode doit permettre une réalisation à grande échelle, contrôlée, et à moindre coût comparée aux méthodes existantes. Ce travail a été réalisé dans le cadre d'une collaboration entre l'IM2NP, le CINAM et Thalès. Optical materials ; Architectural design ; Integrated optics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Deng Bo-Wen
Source: ChemRxiv (preprint, 2023-05-30)
Field: Materials Science — Materials Chemistry

Catalysis distillation neural network for the few-shot open catalyst challenge The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts. Nonetheless, the challenges of electrocatalytic reactions and developing a “large-scale language model” in catalysis persist, and the recent success of ChatGPT's (Chat Generative Pre-trained Transformer) few-shot methods surpassing BERT (Bidirectional Encoder Representation from Transformers) underscores the importance of addressing limited data, expensive computations, time constraints and structure-activity relationship in research. Hence, the development of few-shot techniques for catalysis is critical and essential, regardless of present and future requirements. This paper introduces the Few-Shot Open Catalyst Challenge 2023, a competition aimed at advancing the application of machine learning technology for predicting catalytic reactions on catalytic surfaces, with a specific focus on dual-atom catalysts in hydrogen peroxide electrocatalysis. To address the challenge of limited data in catalysis, we propose a machine learning approach based on MLP-Like and a framework called Catalysis Distillation Graph Neural Network (CDGNN). Our results demonstrate that CDGNN effectively learns embeddings from catalytic structures, enabling the capture of structure-adsorption relationships. This accomplishment has resulted in the utmost advanced and efficient determination of the reaction pathway for hydrogen peroxide, surpassing the current graph neural network approach by 16.1%. Consequently, CDGNN presents a promising approach for few-shot learning in catalysis. Artificial neural network ; Computer science ; Catalysis ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Bowen Deng
Source: arXiv (Cornell University) (preprint, 2023-05-31)
Field: Materials Science — Materials Chemistry

Catalysis distillation neural network for the few shot open catalyst challenge The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts. Nonetheless, the challenges of electrocatalytic reactions and developing a large-scale language model in catalysis persist, and the recent success of ChatGPT's (Chat Generative Pre-trained Transformer) few-shot methods surpassing BERT (Bidirectional Encoder Representation from Transformers) underscores the importance of addressing limited data, expensive computations, time constraints and structure-activity relationship in research. Hence, the development of few-shot techniques for catalysis is critical and essential, regardless of present and future requirements. This paper introduces the Few-Shot Open Catalyst Challenge 2023, a competition aimed at advancing the application of machine learning technology for predicting catalytic reactions on catalytic surfaces, with a specific focus on dual-atom catalysts in hydrogen peroxide electrocatalysis. To address the challenge of limited data in catalysis, we propose a machine learning approach based on MLP-Like and a framework called Catalysis Distillation Graph Neural Network (CDGNN). Our results demonstrate that CDGNN effectively learns embeddings from catalytic structures, enabling the capture of structure-adsorption relationships. This accomplishment has resulted in the utmost advanced and efficient determination of the reaction pathway for hydrogen peroxide, surpassing the current graph neural network approach by 16.1%.. Consequently, CDGNN presents a promising approach for few-shot learning in catalysis. Artificial neural network ; Computer science ; Catalysis ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Topic Modeling
Authors: Dario Massa | Daniel Cieśliński | Amirhossein D. Naghdi | Stefanos Papanikolaou
Source: arXiv (Cornell University) (preprint, 2023-06-19)
Field: Materials Science — Materials Chemistry

Substitutional Alloying Using Crystal Graph Neural Networks Materials discovery, especially for applications that require extreme operating conditions, requires extensive testing that naturally limits the ability to inquire the wealth of possible compositions. Machine Learning (ML) has nowadays a well established role in facilitating this effort in systematic ways. The increasing amount of available accurate DFT data represents a solid basis upon which new ML models can be trained and tested. While conventional models rely on static descriptors, generally suitable for a limited class of systems, the flexibility of Graph Neural Networks (GNNs) allows for direct learning representations on graphs, such as the ones formed by crystals. We utilize crystal graph neural networks (CGNN) to predict crystal properties with DFT level accuracy, through graphs with encoding of the atomic (node/vertex), bond (edge), and global state attributes. In this work, we aim at testing the ability of the CGNN MegNet framework in predicting a number of properties of systems previously unseen from the model, obtained by adding a substitutional defect in bulk crystals that are included in the training set. We perform DFT validation to assess the accuracy in the prediction of formation energies and structural features (such as elastic moduli). Using CGNNs, one may identify promising paths in alloy discovery. Computer science ; Artificial neural network ; Parameterized complexity ; Graph ; Crystal (programming language) ; Artificial intelligence ; Flexibility (engineering) ; Moduli ; Machine learning ; Algorithm ; Vertex (graph theory) ; Theoretical computer science ; Mathematics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Bharvee Srivastava | C. Sivakumar | Prashant Kumar | Lakshmana Phaneendra Maguluri
Source:  (article, 2023-05-11)
Field: Materials Science — Materials Chemistry

Faster Response Chemical Reaction Using Machine Learning The majority of advancements are still generally attributed to trial-and-error experimentation, which takes a long time and a lot of money and can be a significant portion of research in physics, chemistry, and materials science. This type of experimentation can be a significant portion of the process. Despite the fact that a large percentage of research is devoted to the identification and characterization of novel chemicals that have the potential to be of use to society. In this paper, the chemical reactions are quickened using machine learning modelling. The paper uses supervised machine learning approach to fasten the process of chemical reaction by learning it in a supervised manner. The research provides details of how the process of chemical reactions taking place in conventional and using machine learning models. The simulation is conducted in python to test if the chemical reactions are produced with faster convergence time than the existing methods. Machine learning ; Computer science ; Artificial intelligence ; Process (computing) ; Chemical reaction ; Python (programming language) ; Convergence (economics) ; Identification (biology) ; Chemistry ; Programming language Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Neural Networks and Applications
Authors: Vadim Korolev | Artem Mitrofanov
Source: arXiv (Cornell University) (preprint, 2023-10-30)
Field: Chemistry — Inorganic Chemistry

Coarse-grained crystal graph neural networks for reticular materials design Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine relative ease of synthesis and an impressive range of applications in various fields, from gas storage to biomedicine. Diverse properties arise from the variation of building units$\unicode{x2013}$metal centers and organic linkers$\unicode{x2013}$in almost infinite chemical space. Such variation substantially complicates experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from incorporation of irrelevant and redundant features such as full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design. Computer science ; Theoretical computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Algorithm ; Topology (electrical circuits) ; Machine learning ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Daniel J. Hutton | Kari E. Cordes | Carine Michel | Florian Göltl
Source: ChemRxiv (preprint, 2023-05-03)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of activation energies for chemical reactions on metal surfaces In computational surface catalysis, the calculation of activation energies of chemical reactions is expensive, which in many cases limits our ability to understand complex reaction networks. Here, we present a universal, machine learning-based approach for the prediction of activation energies for reactions of C, O, and H containing molecules on transition metal surfaces. We rely on generalized Bronsted-Evans-Polanyi relationships in combination with machine learning-based multiparameter regression techniques to train our model for reactions included in the University of Arizona Reaction database. In our best approach, we find a Mean Absolute Error for activation energies within our test set of 0.15 eV if the reaction energy is known and 0.19 eV if the reaction energy is unknown. We expect that this methodology will often replace the explicit calculation of activation energies within surface catalysis when exploring large reaction networks or screening catalysts for desirable properties in the future. Catalysis ; Activation energy ; Chemistry ; Chemical reaction ; Energy (signal processing) ; Computer science ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Yifan Li | Yihan Wu | Yuhang Han | Qujie Lyu | Hao Wu | Xiuying Zhang | Lei Shen
Source: arXiv (Cornell University) (preprint, 2023-11-19)
Field: Materials Science — Materials Chemistry

Local environment-based machine learning for molecular adsorption energy prediction Most machine learning (ML) models in Materials Science are developed by global geometric features, often falling short in describing localized characteristics, like molecular adsorption on materials. In this study, we introduce a local environment framework that extracts local features from crystal structures to portray the environment surrounding specific adsorption sites. Upon OC20 database (~20,000 3D entries), we apply our local environment framework on several ML models, such as random forest, convolutional neural network, and graph neural network. It is found that our framework achieves remarkable prediction accuracy in predicting molecular adsorption energy, significantly outperforming other examined global-environment-based models. Moreover, the employment of this framework reduces data requirements and augments computational speed, specifically for deep learning algorithms. Finally, we directly apply our Local Environment ResNet (LERN) on a small 2DMatPedia database (~2,000 2D entries), which also achieves highly accurate prediction, demonstrating the model transferability and remarkable data efficiency. Overall, the prediction accuracy, data-utilization efficiency, and transferability of our local-environment-based ML framework hold a promising high applicability across a broad molecular adsorption field, such as catalysis and sensor technologies. Transferability ; Computer science ; Convolutional neural network ; Adsorption ; Artificial neural network ; Artificial intelligence ; Graph ; Machine learning ; Field (mathematics) ; Random forest ; Data mining ; Theoretical computer science ; Chemistry ; Mathematics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Advanced Graph Neural Networks
Authors: Xueying Yuan | Xiupeng Chen | Yuanxin Zhou | Zhiao Yu | Xian Kong
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Design Principles of Fluoroether Solvents for Lithium Metal Battery Electrolytes Unveiled by Extensive Molecular Simulation and Machine Learning Lithium metal ; Electrolyte ; Battery (electricity) ; Lithium (medication) ; Metal ; Computer science ; Materials science ; Chemistry ; Thermodynamics ; Psychology ; Metallurgy ; Physics ; Physical chemistry ; Electrode Machine Learning in Materials Science
Authors: Jia ZHAO | Yutaro MAEDA | Kenjiro Sugio | Gen Sasaki
Source: Mechanical Engineering Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Development of neural network potential for Al-based alloys containing vacancy Potential energy of an alloy is an essential indicator for evaluating the stability of the structure in predicting new materials. Therefore, how to calculate the potential energy in material design has become an inevitable problem. While first-principles calculations can provide chemical accuracy for arbitrary atomic arrangements, they are prohibitive in terms of computational effort and time. To enable atomistic-level simulations of both the processing and performance of Aluminum alloys, neural network potential was proposed to predict the binding energy of vacancy-containing aluminum alloys in a highly accurate state. This method combined first-principles calculations and machine learning techniques to explore the intrinsic link between solid solution structure and binding energies. In this study, four binary alloys (aluminum-silicon, aluminum- zirconium, aluminum-magnesium and aluminum-titanium alloys) were investigated. The mean squared errors were used to quantify the quality of the neural network potential models and it was found that the trained model is more stable and exhibits high accuracy for energy prediction. The Monte Carlo simulation results show that using this neural network potential successfully simulated aging process of aluminum alloys, and the neural network potential can be much faster than first-principles calculations, even with high accuracy. Artificial neural network ; Materials science ; Aluminium ; Vacancy defect ; Monte Carlo method ; Alloy ; Zirconium ; Statistical physics ; Titanium alloy ; Energy (signal processing) ; Algorithm ; Computer science ; Artificial intelligence ; Metallurgy ; Chemistry ; Mathematics ; Physics ; Crystallography Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metal and Thin Film Mechanics
Authors: Jianchuan Liu | Xingchen Zhang | Chen, Tao | Yuzhi Zhang | Duo Zhang | Linfeng Zhang | Mohan Chen
Source: arXiv (Cornell University) (preprint, 2023-11-19)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal inter-atomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning inter-atomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pre-trained model towards a universal model to study group IIB to VIA semiconductors. Semiconductor ; Atomic orbital ; Ab initio ; Machine learning ; Materials science ; Cadmium telluride photovoltaics ; Density functional theory ; Deep learning ; Artificial intelligence ; Artificial neural network ; Computer science ; Group (periodic table) ; Computational chemistry ; Chemistry ; Nanotechnology ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Boron and Carbon Nanomaterials Research