jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aleksandra Głowska | Elsa Jolimaître | Adam Hammoumi | Maxime Moreaud | Loïc Sorbier | Caroline de Faria Barros | Véronique Lefebvre | Marc‐Olivier Coppens
Source: The Journal of Physical Chemistry C (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer The pore network architecture of porous heterogeneous catalyst supports has a significant effect on the kinetics of mass transfer occurring within them. Therefore, characterizing and understanding structure-transport relationships is essential to guide new designs of heterogeneous catalysts with higher activity and selectivity and superior resistance to deactivation. This study combines classical characterization via N<sub>2</sub> adsorption and desorption and mercury porosimetry with advanced scanning electron microscopy (SEM) imaging and processing approaches to quantify the spatial heterogeneity of γ-alumina (γ-Al<sub>2</sub>O<sub>3</sub>), a catalyst support of great industrial relevance. Based on this, a model is proposed for the spatial organization of γ-Al<sub>2</sub>O<sub>3</sub>, containing alumina inclusions of different porosities with respect to the alumina matrix. Using original, advanced SEM image analysis techniques, including deep learning semantic segmentation and porosity measurement under gray-level calibration, the inclusion volume fraction and interphase porosity difference were identified and quantified as the key parameters that served as input for effective tortuosity factor predictions using effective medium theory (EMT)-based models. For the studied aluminas, spatial porosity heterogeneity impact on the effective tortuosity factor was found to be negligible, yet it was proven to become significant for an inclusion content of at least 30% and an interphase porosity difference of over 20%. The proposed methodology based on machine-learning-supported image analysis, in conjunction with other analytical techniques, is a general platform that should have a broader impact on porous materials characterization. Mass transfer ; Mesoporous material ; Porosity ; Characterization (materials science) ; Materials science ; Catalysis ; Kinetics ; Selectivity ; Biological system ; Chemical engineering ; Computer science ; Nanotechnology ; Chemistry ; Chromatography ; Engineering ; Composite material ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Anodic Oxide Films and Nanostructures
Authors: Noritaka Usami | Kentaro Kutsukake | Takuto Kojima | Hiroaki Kudo | Tatsuya Yokoi | Yutaka Ohno
Source: Science and Technology of Advanced Materials (review, 2024-09-18)
Field: Materials Science — Materials Chemistry

Multicrystalline informatics: a methodology to advance materials science by unraveling complex phenomena Multicrystalline materials play a crucial role in our society. However, their microstructure is complicated, and there is no universal approach to achieving high performance. Therefore, a methodology is necessary to answer the fundamental question of how we should design and create microstructures. 'Multicrystalline informatics' is an innovative approach that combines experimental, theoretical, computational, and data sciences. This approach helps us understand complex phenomena in multicrystalline materials and improve their performance. The paper covers various original research bases of multicrystalline informatics, such as the three-dimensional visualization of crystal defects in multicrystalline materials, the machine learning model for predicting crystal orientation distribution, network analysis of multicrystalline structures, computational methods using artificial neural network interatomic potentials, and so on. The integration of these research bases proves to be useful in understanding unexplained phenomena in complex multicrystalline materials. The paper also presents examples of efficient optimization of the growth process of high-quality materials with the aid of informatics, as well as prospects for extending the methodology to other materials. Informatics ; Materials science ; Engineering ethics ; Materials informatics ; Nanotechnology ; Data science ; Engineering physics ; Computer science ; Health informatics ; Engineering ; Medicine ; Engineering informatics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Kun Zhang | Xiaochuan Wang | Hongxing Li | Xueting Zhao | Guangzu Zhang | Changlong Tan | Yanxu Wang | Bing Li
Source: Applied Energy (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Large thermal hysteresis enabled caloric batteries Caloric theory ; Hysteresis ; Thermal hysteresis ; Thermal ; Materials science ; Automotive engineering ; Environmental science ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Lei Yang | Jiake Fan | Weihua Zhu
Source: Fuel (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Single atom decorated wavy antimony nitride for nitric oxide degradation: A first-principles and machine learning study Degradation (telecommunications) ; Nitride ; Atom (system on chip) ; Materials science ; Oxide ; Nitrogen atom ; Antimony ; Chemical engineering ; Metallurgy ; Nanotechnology ; Chemistry ; Computer science ; Engineering ; Parallel computing ; Organic chemistry ; Group (periodic table) Catalytic Processes in Materials Science ; Advanced Memory and Neural Computing ; Gas Sensing Nanomaterials and Sensors
Authors: Dan Wang | Zan Rui-guang | Xiaorong Zhu | Yuwei Zhang | Yu Wang | Yanhui Gu | Yafei Li
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide Defect engineering of semiconductor photocatalysts is critical in reducing the reaction barriers. The generation of surface oxygen vacancies allows substantial tuning of the electronic structure of anatase titanium dioxide (TiO<sub>2</sub>), but disclosing the vacancy formation at the atomic level remains complex or time-consuming. Herein, we combine density functional theory calculations with machine learning to identify the main factors affecting the formation of oxygen defects and accelerate the prediction of vacancy formation. The results show that the first two-layer oxygen atoms on the typical surfaces of TiO<sub>2,</sub> including (100), (110), and (211) facets, are more likely to be activated when the gas is more reduced, the pressure is higher, and the reduction temperature is increased. Through machine learning, we can conveniently predict the formation of oxygen defects with high accuracy. Furthermore, we present an equation with acceptable accuracy for quantitatively describing the formation of oxygen vacancies in different chemical environments. Our work provides a fast and efficient strategy for characterizing the surface structure with atomic defects. Titanium dioxide ; Anatase ; Oxygen ; Vacancy defect ; Materials science ; Titanium ; Chemical engineering ; Chemistry ; Metallurgy ; Catalysis ; Crystallography ; Organic chemistry ; Engineering ; Photocatalysis Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Zongya Wu | Xian Shi | Xueting Wang | Yanlong Luo | Xiujuan Wang
Source: Journal of Molecular Structure (article, 2024-06-10)
Field: Chemistry — Organic Chemistry

Modeling and analysis of synergistic phenolic antioxidants in edible oil: Integrating molecular simulation and artificial neural network approach Chemistry ; Antioxidant ; Syringic acid ; Artificial neural network ; Fourier transform infrared spectroscopy ; Biological system ; Organic chemistry ; Chemical engineering ; Machine learning ; Gallic acid ; Computer science Free Radicals and Antioxidants ; Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities
Authors: Franck Polewczyk | Jean‐Marc Leyssale | Philippe Aurel | Nicolas Pineau | Christophe Denoual | Gérard L. Vignoles | Paul Lafourcade
Source: Acta Materialia (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Elasticity of dense anisotropic carbons: A machine learning model of the structure–property relationship informed by large scale molecular dynamics data Anisotropy ; Materials science ; Adiabatic process ; Isothermal process ; Isotropy ; Moduli ; Elastic modulus ; Texture (cosmology) ; Elasticity (physics) ; Thermodynamics ; Composite material ; Statistical physics ; Physics ; Optics ; Computer science Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites
Authors: Emily Shinkle | Aleksandra Pachalieva | Riti Bahl | Sakib Matin | Brendan J. Gifford | Galen T. Craven | Nicholas Lubbers
Source: Journal of Chemical Theory and Computation (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Thermodynamic Transferability in Coarse-Grained Force Fields Using Graph Neural Networks Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared with corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse-grained force fields, which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach yields not only highly accurate force fields but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques, such as graph neural networks, to improve the construction of transferable coarse-grained force fields. Granularity ; Computer science ; Force field (fiction) ; Transferability ; Graph ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Mohamed M. Elsenety
Source: Scientific Reports (article, 2024-11-03)
Field: Materials Science — Polymers and Plastics

Design and predict the potential of imidazole-based organic dyes in dye-sensitized solar cells using fingerprint machine learning and supported by a web application This scientific paper presents a novel approach to explore and predict the potential of imidazole-based organic dyes for use in Dye-Sensitized Solar Cells (DSSCs) using a machine learning web application. The design of efficient and cost-effective organic dyes is critical to enhance the performance of DSSCs. Traditional experimental methods are time-consuming and resource-intensive, making it challenging to screen a large number of potential dyes. In this study, we propose a machine learning-based approach to accelerate the discovery process by predicting the photovoltaic performance of imidazole-based organic dyes. Machin learning predictions provide valuable insights into the expected PCE% and behaviors of the molecules toward DSSCs. Based on the RDKit library, several fingerprints such as Molecular ACCess System, Avalon, Daylight, Pharmacophore and Morgan with different radius (r2, r3, r4), were studied. In addition, more than 20 ML algorithms using different cross validation (3, 5, 7, 10) were also evaluated. Among of these, Deep Neural Network models of MLPRegressor algorithm based on the daylight fingerprint shows a significant coefficient of determination combined with the lowest errors. Utilize the trained ML models to screen of 50 million SMILE structure for identify promising imidazole and nitrogen-containing derivative as a doner group. By replacing the donor groups in the well-known MK2 dye structure with the top imidazole derivatives proposed by machine learning, significant improvements in PCE were observed, increasing from 7.70% to as high as 11.49%, representing nearly a 50% enhancement over the control. DFT calculations confirm the ML predictions and clarify the significantly higher oscillator strength and charge transfer properties of MK2-DM1, compared to MK2. This result provides a promising pathway for developing new dye materials that can push the efficiency limits of DSSCs, leading to more efficient solar energy conversion technologies in the future. In addition, a developed web application offers a user-friendly interface for researchers to input their molecular structures and obtain PCE% predictions toward DSSCs. This information can guide researchers in designing a new imidazole dye with high photovoltaic performance to validate and refine the predictions without time consuming. Imidazole ; Computer science ; Artificial intelligence ; Fingerprint (computing) ; Machine learning ; Dye-sensitized solar cell ; Deep learning ; Pharmacophore ; Chemistry ; Organic chemistry Conducting polymers and applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Silvia Licciardi | Guido Ala | Elisa Francomano | Fabio Viola | Michele Lo Giudice | Alessandro Salvini | F. Sargeni | Vittorio Bertolini | Andrea Di Schino | Antonio Faba
Source: Mathematics (article, 2024-10-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of electrical equipment. In particular, the modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of the deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. The intent of this paper is to provide the features of a wide range of deep learning tools to be applied to magnetic hysteresis context and beyond. The possibilities of building neural networks in hybrid form are innumerable, so it is not plausible to illustrate them in a single paper, but in the present context, several neural networks used in the scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modeling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for the analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices. Hysteresis ; Artificial neural network ; Computer science ; Artificial intelligence ; Physics ; Condensed matter physics Magnetic Properties and Applications ; Neural Networks and Applications ; Piezoelectric Actuators and Control
Authors: Logan Ward | Ben Blaiszik | Cheng‐Wei Lee | Troy Martin | Ian Foster | André Schleife
Source: npj Computational Materials (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning Knowing the rate at which particle radiation releases energy in a material, the “stopping power,” is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the “Bragg Peak,” varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science. Stopping power ; Density functional theory ; Computer science ; Time-dependent density functional theory ; Statistical physics ; Power (physics) ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Memory and Neural Computing
Authors: Vijaykumar S. Jatti | Dhruv A. Sawant | Rashmi R. Deshpande | Sachin Saluankhe | Róbert Čep | Emad Abouel Nasr | Haitham A. Mahmoud
Source: Frontiers in Materials (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

Tribological analysis of titanium alloy (Ti-6Al-4V) hybrid metal matrix composite through the use of Taguchi’s method and machine learning classifiers The preparation and tribological behavior of the titanium metal matrix (Ti-6Al-4V) composite reinforced with tungsten carbide (WCp) and graphite (Grp) particles were investigated in this study. The stir casting procedure was used to fabricate the titanium metal matrix composites (TMMCs), which had 8 weight percent of WCp and Grp. The tribological studies were designed using Taguchi’s L 27 orthogonal array technique and were carried out as wear tests using a pin-on-disc device. According to Taguchi’s analysis and ANOVA, the most significant factors that affect wear rate are load and distance, followed by velocity. The wear process was ascertained by scanning electron microscopy investigation of the worn surfaces of the composite specimens. Pearson’s heatmap and Feature importance (F-test) were plotted for data analysis to study the significance of input parameters on wear. Machine learning classification algorithms such as k-nearest neighbors, support vector machine, and XGBoost algorithms accurately classified the wear rate data, giving an accuracy value of 71.25%, 65%, and 56.25%, respectively. Taguchi methods ; Materials science ; Tungsten carbide ; Tribology ; Composite number ; Titanium carbide ; Metal matrix composite ; Titanium alloy ; Scanning electron microscope ; Composite material ; Matrix (chemical analysis) ; Titanium ; Casting ; Carbide ; Alloy ; Metallurgy Titanium Alloys Microstructure and Properties ; Aluminum Alloys Composites Properties ; Advanced materials and composites
Authors: Richard Tran | Liqiang Huang | Yuan Zi | Shengguang Wang | Benjamin M. Comer | Xuqing Wu | Stefan J. Raaijman | Nishant K. Sinha | Sajanikumari Sadasivan | Shibin Thundiyil | Kuldeep Mamtani | Ganesh Iyer | Lars C. Grabow | Ligang Lu | Jiefu Chen
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Rational design of nanoscale stabilized oxide catalysts for OER with OC22 The efficiency of H<sub>2</sub> production <i>via</i> water electrolysis is limited by the sluggish oxygen evolution reaction (OER). As such, significant emphasis has been placed upon improving the rate of OER through the anode catalyst. More recently, the Open Catalyst 2022 (OC22) framework has provided a large dataset of density functional theory (DFT) calculations for OER intermediates on the surfaces of oxides. When coupled with state-of-the-art graph neural network models, total energy predictions can be achieved with a mean absolute error as low as 0.22 eV. In this work, we interpolated a database of the total energy predictions for all slabs and OER surface intermediates for 4119 oxide materials in the original OC22 dataset using pre-trained models from the OC22 framework. This database includes all terminations of all facets up to a maximum Miller index of 1. To demonstrate the full utility of this database, we constructed a flexible screening framework to identify viable candidate anode catalysts for OER under varying reaction conditions for bulk, surface, and nanoscale Pourbaix stability as well as material cost, overpotential, and metastability. From our assessment, we were able to identify 122 and 68 viable candidates for OER under the bulk and nanoscale regime, respectively. Nanoscopic scale ; Rational design ; Catalysis ; Oxide ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Metallurgy ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Julia Santana-Andreo | Holger‐Dietrich Saßnick | Caterina Cocchi
Source: Journal of Physics Materials (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Thermodynamic stability and vibrational properties of multi-alkali antimonides Abstract Modern advances in generating ultrabright electron beams have unlocked unprecedented experimental advances based on synchrotron radiation. Current challenges lie in improving the quality of electron sources with novel photocathode materials such as alkali-based semiconductors. To unleash their potential, a detailed characterization and prediction of their fundamental properties is essential. In this work, we employ density functional theory combined with machine learning techniques integrated into the hiphive package to probe the thermodynamic stability of various alkali antimonide crystals, emphasizing the role of the approximations taken for the exchange-correlation potential. Our results reveal that the SCAN functional offers an optimal trade-off between accuracy and computational costs to describe the vibrational properties of these materials. Furthermore, it is found that systems with a higher concentration of Cs atoms exhibit enhanced anharmonicities, which are accurately predicted and characterized with the employed methodology. Antimonide ; Stability (learning theory) ; Density functional theory ; Alkali metal ; Materials science ; Synchrotron radiation ; Photocathode ; Characterization (materials science) ; Chemical stability ; Semiconductor ; Work (physics) ; Electron ; Nanotechnology ; Computational chemistry ; Chemistry ; Computer science ; Optoelectronics ; Thermodynamics ; Physics ; Optics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials