jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cooper Tezak | Jacob M. Clary | Sophie Gerits | Joshua Quinton | Benjamin Rich | Nicholas R. Singstock | Abdulaziz Alherz | Taylor J. Aubry | Struan Clark | R. B. Hurst | Mauro Del Ben | Christopher Sutton | Ravishankar Sundararaman | Charles B. Musgrave | Derek Vigil‐Fowler
Source: The Journal of Physical Chemistry C (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

BEAST DB: Grand-Canonical Database of Electrocatalyst Properties We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze the electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain a granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage. Electrocatalyst ; Database ; Computer science ; Catalysis ; Fidelity ; Density functional theory ; Set (abstract data type) ; Electrochemistry ; Chemistry ; Electrode ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Ndidiamaka Martina Amadi | Joseph Okechukwu Ezeugo | Chukwunonso Chukwuzuluoke Okoye | John Ifeanyi Obibuenyi | Maduabuchi Arinzechukwu Chidiebere | Dominic O. Onukwuli | Valentine Chikaodili Anadebe
Source: Results in Engineering (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model The current study is focused on the synthesis and evaluation of 1-Hexadecyl-3-methylimidazolium tetrachloroindate [ mim][In ] based ionic liquid (IL) as a corrosion inhibitor for mild steel in 1M HCl. Various advanced methods were employed in this research, such as potentiodynamic polarization (PDP), quantum chemical computations, molecular dynamics simulations, weight loss assessments, electrochemical impedance spectroscopy (EIS) and multivariate statistics via machine learning models. The ionic liquid (IL) under investigation demonstrated a notable corrosion inhibition efficiency (93.88 % weight loss, 94. % PDP, 75 % EIS). The combine electrochemical approach suggested a mechanism influenced by electron transfer, underscoring the IL's as a mixed-type inhibitor. The experimental data based on weight loss was optimized using response surface methodology (RSM). Maximum inhibition efficiency of 93.72 % was predicted by the RSM model. Also, the machine learning models based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) demonstrated good predictive power in analyzing the interactive effects affecting the inhibition process. The adsorption behaviour of [ mim][In ] on the mild steel surface further conformed to the Langmuir isotherm, demonstrating a monolayer adsorption process. The comprehensive nature of this approach facilitated a more in-depth adsorption process through computational modelling based on DFT and molecular dynamics. The machine learning models aligned credibly with the experimental findings with pronounced degree of accuracy. Thus, these integrated approaches unravel the potential of the studied IL as effective and sustainable corrosion inhibitor for severe acidic environments. • [ mim][In ] based ionic liquid acted as a good corrosion inhibitor. • The combine electrochemical studies classify [ mim][In ] ionic liquid as a mixed type inhibitor. • Regression analysis based on ANFIS, RSM and ANN model compliments the experimental findings. • Computational studies based on DFT and molecular simulation align with the experimental results. Multivariate statistics ; Ionic liquid ; Corrosion ; Quadratic equation ; Corrosion inhibitor ; Regression analysis ; Regression ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics ; Metallurgy ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Ionic liquids properties and applications
Authors: Xuan Peng | Hao Wang
Source: Chemical Engineering Science (article, 2024-05-22)
Field: Chemistry — Inorganic Chemistry

Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N2 Separation (statistics) ; Process engineering ; Chemistry ; Chromatography ; Computer science ; Engineering ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Xingqi Liao | Mingqing Liao | Chong Wei | Zhiheng Huang | Wenjiu Duan | Xiaoming Duan | Delong Cai | Laurent Grémillard | Zhihua Yang | Dechang Jia | Yu Zhou
Source: Acta Materialia (article, 2024-03-09)
Field: Materials Science — Ceramics and Composites

A process-structure-property model via physics-based/data-driven hybrid methods for freeze-cast porous ceramics in Si3N4-Si2N2O case system Materials science ; Microstructure ; Ceramic ; Porosity ; Property (philosophy) ; Process (computing) ; Artificial neural network ; Mechanical engineering ; Computer science ; Composite material ; Machine learning ; Engineering Advanced ceramic materials synthesis ; Injection Molding Process and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Zihao Bai | Mangladeep Bhullar | Akinwumi Akinpelu | Yansun Yao
Source: Materials Today Physics (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Unveiling future superconductors through machine learning The recent discovery of superconductivity above 200 K in hydrides of sulfur and lanthanum under high pressure marked a significant advance toward the realization of room-temperature superconductivity. While binary hydrides have almost been completely studied theoretically, experimental evidence suggests that the next breakthrough in finding high-temperature and low-pressure limits is likely connected with ternary and higher hydrides. Unlike the traditional synthesis-test-repeat approach, experimental discovery of superhydrides under high pressure often follows prior theoretical predictions. In this Minireview, we describe how various artificial intelligence schemes enable and enrich each stage of the discovery cycle of superhydrides and new developments made toward predicting ternary and higher hydrides. As a new enabling tool, machine learning-informed material simulation is still making its way into this field but is already playing an essential role in augmenting the prediction of new superhydrides through automated and iterative machine-learning processes. The review concludes with a perspective on outstanding challenges and possible future developments in the field. Ternary operation ; Superconductivity ; Realization (probability) ; Field (mathematics) ; Perspective (graphical) ; Materials science ; Binary number ; Machine learning ; High pressure ; Artificial intelligence ; Computer science ; Nanotechnology ; Engineering physics ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Inorganic Chemistry and Materials
Authors: Mahmuda Akter | Elias Khalil | Md. Haris Uddin | Md. Kamrul Hassan Chowdhury | Maruf Hasan
Source: Textile Research Journal (article, 2024-02-17)
Field: Materials Science — Polymers and Plastics

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design. Woven fabric ; Artificial neural network ; Transmittance ; Materials science ; Composite material ; Linear regression ; Plain weave ; Thermal ; Biological system ; Artificial intelligence ; Computer science ; Machine learning ; Yarn ; Meteorology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Qi Huang | Zuowei Chen | Ziwei Lin | Weimin Li | Wenjie Yu | Lei Zhu
Source: ACS Applied Polymer Materials (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Enhancing Copolymer Property Prediction through the Weighted-Chained-SMILES Machine Learning Framework Accurately predicting copolymer properties plays a pivotal role in the field of polymer informatics. This endeavor necessitates a comprehensive understanding of polymer structures, adept feature engineering, and proficient application of machine learning algorithms. In traditional methodologies, features for each monomer structure were generated independently, thus, segregating features from individual monomers. This approach results in a less informative representation, with limited applicability. To address these challenges, we introduce an innovative machine learning framework, named weighted-chained-SMILES. By constructing a representative SMILES notation, more intricate information can be encapsulated within the generated features. Our experimental results to predict the thermal properties demonstrate that our approach not only delivers competitive predictive performance but also exhibits enhanced adaptability across a diverse range of molecular representations. The versatility showcased by our model suggests promising potential for tackling more complex copolymer systems and extending its predictive capabilities to various other polymer properties. Computer science ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Notation ; Adaptability ; Field (mathematics) ; Representation (politics) ; Copolymer ; Feature (linguistics) ; Materials informatics ; Feature engineering ; Polymer ; Deep learning ; Materials science ; Health informatics ; Mathematics ; Engineering informatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Ruiyu Wang | Shams Mehdi | Ziyue Zou | Pratyush Tiwary
Source: The Journal of Physical Chemistry B (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

Is the Local Ion Density Sufficient to Drive NaCl Nucleation from the Melt and Aqueous Solution? Even though nucleation is ubiquitous in different science and engineering problems, investigating nucleation is extremely difficult due to the complicated ranges of time and length scales involved. In this work, we simulate NaCl nucleation in both molten and aqueous environments using enhanced sampling of all-atom molecular dynamics with deep-learning-based estimation of reaction coordinates. By incorporating various structural order parameters and learning the reaction coordinate as a function thereof, we achieve significantly improved sampling relative to traditional ad hoc descriptions of what drives nucleation, particularly in an aqueous medium. Our results reveal a one-step nucleation mechanism in both environments, with reaction coordinate analysis highlighting the importance of local ion density in distinguishing solid and liquid states. However, although fluctuations in the local ion density are necessary to drive nucleation, they are not sufficient. Our analysis shows that near the transition states, descriptors such as enthalpy and local structure become crucial. Our protocol proposed here enables robust nucleation analysis and phase sampling and could offer insights into nucleation mechanisms for generic small molecules in different environments. Nucleation ; Reaction coordinate ; Chemical physics ; Aqueous solution ; Molecular dynamics ; Ion ; Atom (system on chip) ; Umbrella sampling ; Chemistry ; Materials science ; Physical chemistry ; Computational chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Crystallization and Solubility Studies
Authors: Muyang Li | Jian Liu | Tuo Yao | Zhenguo Gao | Junbo Gong
Source: Powder Technology (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Deep-learning based in-situ micrograph analysis of high-density crystallization slurry using image and data enhancement strategy Crystallization ; Slurry ; Artificial intelligence ; Crystal (programming language) ; Process (computing) ; Materials science ; Computer science ; Pattern recognition (psychology) ; Engineering ; Chemical engineering Crystallization and Solubility Studies ; Cultural Heritage Materials Analysis ; Spectroscopy and Chemometric Analyses
Authors: Linmeng Wang | Shihao Feng | Chenjun Zhang | Xi Zhang | Xiaodan Liu | Hongyi Gao | Zhiyuan Liu | Rushuo Li | Jingjing Wang | Jin Xu
Source: ACS Applied Materials & Interfaces (article, 2024-07-04)
Field: Chemistry — Inorganic Chemistry

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal–Organic Frameworks for Hydrogen Storage Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H<sub>2</sub> storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H<sub>2</sub> storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10<sup>-4</sup> times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H<sub>2</sub> adsorption capacity, which can be extended for the screening and discovery of other functional materials. Hydrogen storage ; Materials science ; Metal-organic framework ; Throughput ; Convolutional neural network ; Workflow ; Artificial neural network ; Porosity ; Nanoporous ; Computational science ; Computer science ; Nanotechnology ; Artificial intelligence ; Composite material ; Adsorption ; Database ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Long Chen | Shu Liu | Rong Sun | Jibao Lu
Source: Computational Materials Science (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Impact of structural characteristics on thermal conductivity of foam structures revealed with machine learning Thermal conductivity ; Materials science ; Thermal ; Composite material ; Thermodynamics ; Physics Thermal properties of materials ; Heat and Mass Transfer in Porous Media ; Radiative Heat Transfer Studies
Authors: Huiru Zhang | Liang Gao | Yu-Hao Ye | Jiaxin Zhang | Tao Zhang | Qing-Zhou Bu | Qun Yang | Zengwei Zhu | Shuai Wei | Hai‐Bin Yu
Source: Materials Futures (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Fragility crossover mediated by covalent-like electronic interactions in metallic liquids Abstract Fragility is one of the central concepts in glass and liquid sciences, as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties. However, the intervention of crystallization often prevents the assessment of fragility in poor glass-formers, such as supercooled metallic liquids. Hence experimental data on their compositional dependence are scarce, let alone fundamentally understood. In this work, we use fast scanning calorimetry to overcome this obstacle and systematically study the fragility in a ternary La–Ni–Al system, over previously inaccessible composition space. We observe fragility dropped in a small range with the Al alloying, indicating an alloying-induced fragility crossover. We use x-ray photoelectron spectroscopy, resistance measurements, electronic structure calculations, and DFT-based deep-learning atomic simulations to investigate the cause of this fragility drop. These results show that the fragility crossover can be fundamentally ascribed to the electronic covalency associated with the unique Al–Al interactions. Our findings provide insight into the origin of fragility in metallic liquids from an electronic structure perspective and pave a new way for the design of metallic glasses. Fragility ; Crossover ; Covalent bond ; Metal ; Materials science ; Chemical physics ; Nanotechnology ; Chemistry ; Computer science ; Metallurgy ; Organic chemistry ; Physical chemistry Material Dynamics and Properties ; Electrochemical Analysis and Applications ; Ionic liquids properties and applications
Authors: Amith Gadagi | Baskaran Sivaprakash | Chandrashekar Adake | Umesh B. Deshannavar | Prasad G. Hegde | P Santhosh | Natarajan Rajamohan | Ahmed I. Osman
Source: Composites Part C Open Access (article, 2024-03-22)
Field: Materials Science — Polymers and Plastics

Epoxy composite reinforced with jute/basalt hybrid – Characterisation and performance evaluation using machine learning techniques Epoxy resins, prized for their versatile properties, are derived from bio-based materials, contributing to sustainability and eco-friendliness in both production and application. This study focuses on the application of gradient boosting machine learning techniques in the field of machining to predict the surface roughness and also the contour based experimental validation of the numerical results. The turning experiments, conducted via Taguchi's L27 array, aimed to explore the effects of depth of cut, feed rate, and spindle speed. Higher spindle speeds, lower feed rates, and shallower cuts led to smoother surfaces in turned jute/basalt epoxy composites. Machine learning models (Gradient Boosting Machine, AdaBoost, and XGBoost) were then used to predict surface roughness. Amongst these, XGBoost outperformed GBM and AdaBoost, exhibiting maximum and average prediction errors of 3.78 % and 2.24 %, respectively. XGBoost accurately predicted 2D surface roughness contours that closely matched experimental contours for training and test cases. Taguchi's Orthogonal Matrix identified minimum surface roughness values as 0.773 μm (experimental), 0.800 μm (GBM), 0.880 μm (AdaBoost), and 0.774 μm (XGBoost). All were achieved at 1500 rpm spindle speed, 0.05 mm/rev feed rate, and 0.3 mm depth of cut. Epoxy ; Composite number ; Composite material ; Basalt ; Materials science ; Composite epoxy material ; Computer science ; Geology Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Zhaoyi Zhang | Hefu Li | Lili Huang | Hongjun Wang | Huijuan Niu | Zhenshan Yang | Minghong Wang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-06-13)
Field: Chemistry — Analytical Chemistry

Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network Malachite green ; Convolutional neural network ; Identification (biology) ; Fish <Actinopterygii> ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Biology ; Fishery ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xinhui Li | Guan Wang | Linyuan Kou | Pei Zhang | Jialong Du | Hongping Liu | Xin Shang
Source: Engineering Fracture Mechanics (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Corrosion pit-induced stress concentration in 7005 aluminium alloy: Mechanical degradation and pit parameter analysis Mechanical testing and surface analysis were used to investigate the corrosion behaviour of 7005 aluminium alloys in simulated atmospheric environments. Stress concentrations of pits lead to degradation of mechanical properties. The effect of pit sizes and location parameters on stress concentration was analysed using finite element (FE) method. The location of the secondary pits within the primary pits affects the stress concentration factor (SCF). Secondary pits located in the unaffected region have almost no effect on the stress concentration. A Back Propagation neural network (BPNN) model was developed to predict the stress concentrations of secondary pits with different location parameters. Aluminium ; Materials science ; Corrosion ; Stress (linguistics) ; Degradation (telecommunications) ; Alloy ; Stress concentration ; Metallurgy ; Aluminium alloy ; Composite material ; Fracture mechanics Corrosion Behavior and Inhibition ; Aluminum Alloy Microstructure Properties ; Hydrogen embrittlement and corrosion behaviors in metals