jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Karlo Nolkemper | Markus Antonietti | Thomas D. Kühne | S. Alireza Ghasemi
Source: The Journal of Physical Chemistry C (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Kinetically Stable and Highly Ordered Two-Dimensional CN<sub>2</sub> Crystal Structures We present three dynamically and thermally stable polymorphs of CN2, which we identify through a systematic, multistep, and comprehensive structure search. They consist of nitrogen-rich structural motifs and modestly large pores. Kinetic stability is examined through long-term molecular dynamical simulations at an elevated temperature, where the interatomic interactions are treated using a new machine learning potential. Using density functional theory, we demonstrate their structural and electronic properties and compare them to polymorphs already available in the literature. The proposed structures are considerably more thermodynamically stable than their counterparts while desirably exhibiting energy levels suitable for photocatalytic water splitting. Also, calculations of enthalpy up to a modest value of external pressure show that the predicted polymorphs are thermodynamically more favorable than the previously proposed structures for high-pressure CN2. Enthalpy ; Density functional theory ; Materials science ; Kinetic energy ; Molecular dynamics ; Thermodynamics ; Crystal structure ; Structural stability ; Standard enthalpy change of formation ; Chemical stability ; Crystal structure prediction ; Chemical physics ; Computational chemistry ; Crystallography ; Physical chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Yanxuan Du | Zhengjie Xu | Jia-Xin Huang | C T Lyu | Cunhao Lu | Jian Chen
Source: Preprints.org (preprint, 2023-08-17)
Field: Chemistry — Organic Chemistry

Integrated Learning Activity Prediction Model of BHO-AdaBoosting Anti-Breast Cancer ERα Inhibitor Based on Improved Random Forest Breast cancer is the most common malignancy in women worldwide. The pathogenesis of this disease is closely related to the estrogen receptor alpha subtype (ERα). Therefore, it is of great importance to develop effective inhibitors of ERα activity for the treatment of breast cancer. In this paper, we propose a novel ensemble machine learning model for quantitative structure-activity relationship of anti-breast cancer drugs, which can effectively predict drug activity in small samples with multiple characteristic variables. To avoid the problem of over-fitting caused by low-correlation independent variables, the scoring mechanism of random forest was improved by incorporating three relevance indicators, including the maximum mutual information number, Pearson correlation coefficient and distance correlation coefficient, and 20 optimal molecular descriptors were selected. The Bayesian hyperparameter optimization method was used to optimize the parameters of multiple linear regression (MLR), support vector regression (SVR), and extreme gradient boosting (XGBoost), respectively. The AdaBoost strong learner was constructed by combining the weak learner with the weighted linear addition method. The results show that the proposed ensemble learning model has the best prediction performance compared to the three basic learner models and the CNN-LSTM combination prediction model. The root mean square error was reduced by 7.60%-26.51%. The mean relative error was reduced by 6.46%-30.92%. Goodness of fit increased by 9.57%-36.94%. Finally, the biological activities of 50 candidate compounds for ERα inhibitors were predicted, and it was found that 4-[2-benzyl-1-[4-(2-pyrrolidin-1-ylethoxy)phenyl]but-1-enyl]phenol had an excellent biological activity value pIC50, which had the potential to be an ERα inhibitor. The model proposed in this paper has good prediction accuracy, which can provide an effective reference for the discovery and development of anti-breast cancer drugs. Random forest ; AdaBoost ; Support vector machine ; Artificial intelligence ; Quantitative structure–activity relationship ; Ensemble learning ; Machine learning ; Correlation ; Hyperparameter ; Correlation coefficient ; Pearson product-moment correlation coefficient ; Mathematics ; Breast cancer ; Linear regression ; Mean squared error ; Bayesian multivariate linear regression ; Regression ; Computer science ; Statistics ; Cancer ; Medicine ; Internal medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Estrogen and related hormone effects
Authors: Hualing Lin | Yiming Bi | Xiaobing Zhang | Bei Yu | Jianxin Hu | Hui Wang | Heng Yu | Hui‐Liang Shen
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Deep Tobacco Leaf Grading Using Self-Adaptive Attention Grading (engineering) ; Tobacco leaf ; Artificial intelligence ; Computer science ; Psychology ; Biology ; Engineering ; Agricultural engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Yoshihiro Hayashi | Miho Noguchi | Takuya Oishi | Takashi Ono | Kotaro Okada | Yoshinori Onuki
Source: International Journal of Pharmaceutics (article, 2023-05-20)
Field: Chemistry — Analytical Chemistry

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales Granulation ; Principal component analysis ; Partial least squares regression ; Regression analysis ; Artificial intelligence ; Ultimate tensile strength ; Similarity (geometry) ; Unsupervised learning ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Database ; Materials science ; Statistics ; Composite material Spectroscopy and Chemometric Analyses ; Drug Solubulity and Delivery Systems ; Mineral Processing and Grinding
Authors: Wei Tang | Shizheng Wen | Huilong Hou | Qihua Gong | Min Yi | Wanlin Guo
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase-Field Simulation and Machine Learning of Low-Field Magneto-Elastocaloric Effect in a Multiferroic Composite Multiferroics ; Composite number ; Field (mathematics) ; Magneto ; Materials science ; Phase (matter) ; Condensed matter physics ; Mechanical engineering ; Physics ; Optoelectronics ; Magnet ; Engineering ; Composite material ; Ferroelectricity ; Mathematics ; Quantum mechanics Magnetic Properties and Applications ; Solidification and crystal growth phenomena ; Advanced Mathematical Modeling in Engineering
Authors: Q. Shan | Qian Ma | Yudong Xue | Anze Shui
Source: Advanced Engineering Materials (article, 2023-02-11)
Field: Materials Science — Ceramics and Composites

The Damage Mode Automatic Identification of Al<sub>2</sub>TiO<sub>5</sub> Flexible Ceramics Based on Machine Learning Machine learning is applied to study the damage evolution of Al 2 TiO 5 (AT) flexible ceramics at different sintering temperatures to realize the automatic identification of damage modes. Three damage modes (i.e., friction of Al 2 TiO 5 grains, damage of grain boundary glass phase, and fracture of single Al 2 TiO 5 grain) can be successfully distinguished by processing feature values of acoustic emission (AE) collected from material damage through k ‐means algorithm. Though the specimen sintered at 1600 °C possesses the largest grain, the mean feature value of AE in cluster‐3 (fracture of AT single grain) is not maximum since the decomposition of Al 2 TiO 5 is serious at 1600 °C. Meanwhile, the amplitude‐centralized area of the corresponding cluster first expands and then decreases with the increase of sintering temperature. When the sintering temperature is 1500 °C, this centralized area reaches to the maximum. Correspondingly, the specimen sintered at 1500 °C has the maximum failure displacement and the most AE damage events. To realize the automatic identification of damage modes, random forests algorithm is applied to distinguish the damage modes of other samples in the same set. The almost same trend of damage modes evolution in new samples and training sample proves high efficiency of this method. Sintering ; Materials science ; Ceramic ; Acoustic emission ; Fracture (geology) ; Feature (linguistics) ; Cluster (spacecraft) ; Composite material ; Grain boundary ; Displacement (psychology) ; Grain size ; Mode (computer interface) ; Identification (biology) ; Phase (matter) ; Microstructure ; Computer science Advanced ceramic materials synthesis ; Advanced machining processes and optimization ; Injection Molding Process and Properties
Authors: Ajay Patel | Payal Patel | Manisha Parmar | Haren B. Gosai
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Biomaterials

Employing Rsm and Ann-Based Applications for Modelling Enhanced Bacterial Cellulose Production from Pineapple Peel Waste Using Komagateibacter Saccharivorans Appk1 Cellulose ; Waste paper ; Pulp and paper industry ; Bacterial cellulose ; Production (economics) ; Biochemical engineering ; Response surface methodology ; Environmental science ; Food science ; Chemistry ; Waste management ; Process engineering ; Engineering ; Chemical engineering ; Chromatography ; Economics Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Plant tissue culture and regeneration
Authors: Assad Farooq | Nayab Khan | Muhammad Awais | Khalil Ahmad | Muhammad Mohsin | Usama Akhtar | Fiaz Hussaın
Source: Tekstil ve Mühendis (article, 2023-12-17)
Field: Materials Science — Polymers and Plastics

ARTIFICIAL NEURAL NETWORKS TO ENHANCE THE RING MACHINE EFFICIENCY AND YARN QUALITY BY DETERMINATION AND OPTIMIZATION OF DYNAMIC YARN TENSION The yarn spinning process involves the interaction of large varieties of variables. The relation between the dynamic yarn tension (DYT), yarn quality, and production efficiency of the spinning frame cannot be established conclusively. Artificial neural network (ANN) is a promising step in this filed. In this research work, ANNs simulation and modeling is applied for the optimization of the DYT n to improve the production efficiency and quality of yarn. The research to date in DYT is insufficient to meet the developmental requirement of the high-speed and efficient ring spinning frame. One of the major problems facing the effective use of the ANN is the correct selection of the input parameters to be fed for the training of ANNs. Data of various input variables such as count, traveler no., spindle speed and dynamic yarn tension etc., was used for ANN modeling and simulation. DYT plays a significant role in the determination of yarn quality and its productivity in terms of end breakage rate. However, it has never been explained in terms of displacement from the original yarn path. This work is aimed to the determination and optimization of DYT at ring spinning frame. The influence of different yarn geometry parameters on DYT, measured by the tensiometer was investigated. The optimized DYT values for the machines, running at different speed and different counts were determined using ANN modeling. It is found that the optimized values predicted from ANN resulted in better quality, high production, and decreased end-breakage at industrial ring spinning frames. By the implementation of ANNs the optimum speed and effective utilization of textile raw materials can be achieved. Yarn ; Spinning ; Artificial neural network ; Breakage ; Frame (networking) ; Tension (geology) ; Process (computing) ; Quality (philosophy) ; Process variable ; Engineering ; Mechanical engineering ; Computer science ; Materials science ; Composite material ; Artificial intelligence ; Ultimate tensile strength ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Atanu Paul | Anthony Ruffino | Stefan Masiuk | Jonathan E. Spanier | Ilya Grinberg
Source: arXiv (Cornell University) (preprint, 2023-04-15)
Field: Materials Science — Materials Chemistry

An atomistic model of electronic polarizability for calculation of Raman scattering from large-scale MD simulations The application of molecular dynamics (MD) simulations to the interpretation of Raman scattering spectra is hindered by inability of atomistic simulations to account for the dynamic evolution of electronic polarizability, requiring the use of either ab initio method or parameterization of machine learning models. More broadly, the dynamic evolution of electronic-structure-derived properties cannot be treated by the current atomistic models. Here, we report a simple, physically-based atomistic model with few (maximum 10 parameters for the systems considered here) adjustable parameters that can accurately represent the changes in the electronic polarizability tensor for molecules and solid-state systems. Due to its compactness, the model can be applied for simulations of Raman spectra of large (~ 1,000,000-atom) systems with modest computational cost. To demonstrate its accuracy, the model is applied to the CO2 molecule, water clusters, and BaTiO3 and CsPbBr3 perovskites and shows good agreement with ab-initio-derived and experimental polarizability tensor and Raman data. The atomistic nature of the model enables local analysis of the contributions to Raman spectra, paving the way for the application of MD simulations for the interpretation of Raman spectroscopy results. Furthermore, our successful atomistic representation of the evolution of electronic polarizability suggests that the evolution of electronic structure and its derivative properties can be represented by atomistic models, opening up the possibility of studies of electronic-structure-dependent properties using large-scale atomistic simulations. Polarizability ; Raman spectroscopy ; Electronic structure ; Molecular dynamics ; Raman scattering ; Ab initio ; Tensor (intrinsic definition) ; Statistical physics ; Computational chemistry ; Chemical physics ; Materials science ; Chemistry ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Yiming Chen | Chi Chen | Inhui Hwang | Michael J. Davis | Wanli Yang | Chengjun Sun | Lee, Gi-Hyeok | Maria K. Y. Chan | Allen, Daniel | Arias, Juan Marulanda | Ong, Shyue Ping | Chan, Maria K. Y.
Source: arXiv (Cornell University) (preprint, 2023-10-10)
Field: Materials Science — Materials Chemistry

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization X-ray absorption spectroscopy (XAS) is a commonly-employed technique for characterizing functional materials. In particular, x-ray absorption near edge spectra (XANES) encodes local coordination and electronic information and machine learning approaches to extract this information is of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. In this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed datasets and validated their performance on previously unseen experimental datasets. Our analysis revealed an intriguing discovery: the cumulative distribution function (CDF) feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community. XANES ; X-ray absorption spectroscopy ; Computer science ; Inference ; Artificial intelligence ; Spectroscopy ; Benchmark (surveying) ; Machine learning ; Dimensionality reduction ; Spectral line ; Absorption (acoustics) ; Curse of dimensionality ; Feature (linguistics) ; Absorption spectroscopy ; Pattern recognition (psychology) ; Data mining ; Materials science ; Physics ; Optics ; Geology Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Tayyaba Mubashir | Mudassir Hussain Tahir | Sumaira Naeem | Jawayria Najeeb
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Search of Electron-Rich and Electron-Deficient Building Blocks Through Data Mining and Library Generation for the Designing of Polymers for Organic Solar Cells Organic solar cell ; Electron ; Polymer ; Materials science ; Nanotechnology ; Computer science ; Physics ; Composite material Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Stephanie Cheung | Moe Elbadawi | Atheer Awad | Youssef Abdalla | Simon Gaisford | Abdul W. Basit
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Human in the Loop: Accelerating Pharmaceutical SLS 3D Printing Through Active Machine Learning Human-in-the-loop ; Loop (graph theory) ; 3D printing ; Computer science ; Active learning (machine learning) ; Manufacturing engineering ; Artificial intelligence ; Mechanical engineering ; Engineering ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Injection Molding Process and Properties
Authors: Khadijeh Jafari | Mohsen Heidari | Ali Fatehizadeh | Kavoos Dindarloo | Vali Alipour | Omid Rahmanian
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Extensive Sorption of Amoxicillin by Highly Efficient Carbon-Based Adsorbent from Palm Kernel: Artificial Neural Network Modeling Sorption ; Adsorption ; Artificial neural network ; Palm kernel ; Kernel (algebra) ; Activated carbon ; Carbon fibers ; Palm ; Amoxicillin ; Environmental science ; Computer science ; Biological system ; Artificial intelligence ; Chemistry ; Mathematics ; Algorithm ; Biology ; Agroforestry ; Organic chemistry ; Composite number ; Physics Dye analysis and toxicity
Authors: Hehe Fan | Linchao Zhu | Yi Yang | Mohan Kankanhalli
Source:  (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

PointListNet: Deep Learning on 3D Point Lists Deep neural networks on regular 1D lists (e.g., natural languages) and irregular 3D sets (e.g., point clouds) have made tremendous achievements. The key to natural language processing is to model words and their regular order dependency in texts. For point cloud understanding, the challenge is to understand the geometry via irregular point coordinates, in which point-feeding orders do not matter. However, there are a few kinds of data that exhibit both regular 1 D list and irregular 3D set structures, such as proteins and non-coding RNAs. In this paper, we refer to them as 3D point lists and propose a Transformer-style PointListNet to model them. First, PointListNet employs non-parametric distance-based attention because we find sometimes it is the distance, instead of the feature or type, that mainly determines how much two points, e.g., amino acids, are correlated in the micro world. Second, different from the vanilla Transformer that directly performs a simple linear transformation on inputs to generate values and does not explicitly model relative relations, our PointListNet integrates the 1D order and 3D Euclidean displacements into values. We conduct experiments on protein fold classification and enzyme reaction classification. Experimental results show the effectiveness of the proposed PointListNet. Point cloud ; Computer science ; Parametric statistics ; Transformer ; Algorithm ; Artificial intelligence ; Deep learning ; Artificial neural network ; Euclidean distance ; Theoretical computer science ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Handwritten Text Recognition Techniques ; Image Processing and 3D Reconstruction
Authors: Tasnim N. Shaikh | Hardik Pujara
Source: Recent Patents on Engineering (article, 2023-04-28)
Field: Materials Science — Polymers and Plastics

Back-propagation ANN to Predict Cleanliness and Quality of Cotton SpinningPreparatory Output: A Comprehensive Research Background: Modern spinning preparatory has undergone drastic technological changes, but still, individual’s expertise-based decisions govern the complex and non-linear multivariant relationships prevailing amongst raw material (cotton) variables, machine variables, process variables (waste), and product (card sliver) quality. The scientifically precise prediction regarding the cleanliness and quality of card sliver and waste control for the given inputted cotton variables processed on the state-of-the-art machinery setup without waiting for the production and testing of card sliver is still impossible. Methods: The present work describes the use of Aritificial Neural Networks (ANN) for ruling out these limitations on scientific grounds. A complex system targeted at ANN was developed using the "newff" function on the mill's five-year database. Single-group ANN was initially designed to determine the influence of inherent variations in raw cotton fibre properties and trash content on blow room and card performance. A multi-group approach of ANN was developed at a later stage to define the influence of complex interactions amongst various fibre properties on three main quality measures considered at blow room and carding, viz., i) influence of blow room and card on fibre length properties, ii) fibre damage at blow room or improvement at card, and iii) degree of cleanliness of the output material. Results: Reverse modelling for both groups was also successfully designed to demarcate feed cotton quality and cleanliness requirements for targeted blow room or card cleaning performance. Conclusion: A high level of positive endurance was observed for all ANNs. Multigroup networking has proven to be more precise than single group networking. Carding ; Spinning ; Quality (philosophy) ; Engineering ; Product (mathematics) ; Agricultural engineering ; Process engineering ; Manufacturing engineering ; Operations management ; Computer science ; Mechanical engineering ; Mathematics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Research in Cotton Cultivation