jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xu Qu | Yutao Qin | Yogesh B. Gianchandani
Source: Separations (article, 2023-10-31)
Field: Chemistry — Spectroscopy

A Rule-Based Automated Chemical Recognition Algorithm for a Multi-Cell Multi-Detector Micro Gas Chromatograph A chemical recognition algorithm is an integral part of any autonomous microscale gas chromatography (µGC) system for automated chemical analysis. For a multi-detector µGC system, the chemical analysis must account for the retention time of each chemical analyte as well as the relative response of each detector to each analyte, i.e., the detector response pattern (DRP). In contrast to the common approaches of heuristically using principal component analysis and machine learning, this paper reports a rule-based automated chemical recognition algorithm for a multi-cell, multi-detector µGC system, in which the DRP is related to theoretical principles; consequently, this algorithm only requires a small amount of calibration data but not extensive training data. For processing both the retention time and the raw DRP, the algorithm applies rules based on expert knowledge to compare the detected peaks; these rules are located in a customized software library. Additionally, the algorithm provides special handling for chromatogram peaks with a small signal-to-noise ratio. It also provides separate special handling for asymmetrical peaks that may result from surface adsorptive analytes. This work also describes an experimental evaluation in which the algorithm used the relative response of two complementary types of capacitive detectors as well as a photoionization detector that were incorporated into the µGC system of interest. In these tests, which were performed on chromatograms with 21–31 peaks for each detector, the true positive rate was 96.3%, the true negative rate was 94.1%, the false positive rate was 5.9%, and the false negative rate was 3.7%. The results demonstrated that the algorithm can support µGC systems for automated chemical screening and early warning applications. Detector ; Analyte ; Algorithm ; Gas chromatography ; False positive rate ; Artificial intelligence ; Calibration ; Computer science ; Principal component analysis ; Chemistry ; Pattern recognition (psychology) ; Chromatography ; Mathematics Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: C. S. Chou | Hsueh-Li Liu | Linchia Huang | Wilson Guo | Peichen Yu | You-Chia Chang | Yao‐Wei Huang | Jia‐Min Shieh
Source:  (article, 2023-10-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent reticle modification enabled large-area metalens patterning Dielectric metalenses realized by economic photolithography technology are vital to their mass deployment in optoelectronic applications. However, pattern fidelity has become a serious issue that degrades the device performance due to optical proximity effects. Here, we demonstrate an intelligent reticle modification system which modifies the sizes and shapes of designed patterns based on a neural-network U-net lithographic model to produce nanostructures with desired dimensions. We demonstrate 2 mm-diameter visible metalenses with diffraction-limited focusing using DUV KrF 248 nm photolithography. This work bridges between the semiconductor process and lens-making industries to realize high-volume manufacturing of versatile metalens and metasurface products. Reticle ; Photolithography ; Photomask ; Lithography ; Materials science ; Extreme ultraviolet lithography ; Dielectric ; Lens (geology) ; Photoresist ; Optoelectronics ; Nanotechnology ; Semiconductor device fabrication ; Resist ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Nikhil Shenoy | Prudencio Tossou | Emmanuel Noutahi | Hadrien Mary | Dominique Beaini | Jiarui Ding
Source: arXiv (Cornell University) (preprint, 2023-10-30)
Field: Materials Science — Materials Chemistry

Role of Structural and Conformational Diversity for Machine Learning Potentials In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal nuanced patterns in generalization metrics. Notably, for optimal structural and conformational generalization, a careful balance between structural and conformational diversity is required, but existing QM datasets do not meet that trade-off. Additionally, our results highlight the limitation of the MLIP models at generalizing beyond their training distribution, emphasizing the importance of defining applicability domain during model deployment. These findings provide valuable insights and guidelines for QM data generation efforts. Generalization ; Diversity (politics) ; Computer science ; Set (abstract data type) ; Quality (philosophy) ; Artificial intelligence ; Machine learning ; Mathematics ; Physics ; Sociology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Haixiao Wan | Sai Li | Yachen Wang | Zhudan Chen | Junwei He | Chunhua Li | Gengxin Liu | Jun Liu | Xiaodong Wang | Thomas P. Russell | Liqun Zhang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

All-Polymer Nanocomposites Having Superior Strength, Toughness and Ultralow Energy Dissipation Dissipation ; Materials science ; Toughness ; Polymer ; Elastomer ; Composite material ; Nanocomposite ; Modulus ; Toughening ; Physics ; Thermodynamics Polymer Nanocomposites and Properties ; Conducting polymers and applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Yemin Yu | Luotian Yuan | Ying Wei | Hanyu Gao | Xinhai Ye | Zhihua Wang | Fei Wu
Source: arXiv (Cornell University) (preprint, 2023-12-18)
Field: Materials Science — Materials Chemistry

RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions. Despite steady progress on standard benchmarks, our understanding of existing retrosynthesis prediction models under the premise of distribution shifts remains stagnant. To this end, we first formally sort out two types of distribution shifts in retrosynthesis prediction and construct two groups of benchmark datasets. Next, through comprehensive experiments, we systematically compare state-of-the-art retrosynthesis prediction models on the two groups of benchmarks, revealing the limitations of previous in-distribution evaluation and re-examining the advantages of each model. More remarkably, we are motivated by the above empirical insights to propose two model-agnostic techniques that can improve the OOD generalization of arbitrary off-the-shelf retrosynthesis prediction algorithms. Our preliminary experiments show their high potential with an average performance improvement of 4.6%, and the established benchmarks serve as a foothold for further retrosynthesis prediction research towards OOD generalization. Generalization ; Retrosynthetic analysis ; Benchmark (surveying) ; Artificial intelligence ; Computer science ; Machine learning ; Distribution (mathematics) ; Construct (python library) ; Engineering ; Geography ; Mathematics ; Cartography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Yanyan Liang | Grisell Díaz Leines | Ralf Drautz | Jutta Rogal
Source: arXiv (Cornell University) (preprint, 2023-10-18)
Field: Materials Science — Materials Chemistry

Structural transformations driven by local disorder at interfaces Despite the fundamental importance of solid-solid transformations in many technologies, the microscopic mechanisms remain poorly understood. Here, we explore the atomistic mechanisms at the migrating interface during solid-solid phase transformations between the topologically closed-packed A15 and body-centred cubic phase in tungsten. The high energy barriers and slow dynamics associated with this transformation require the application of enhanced molecular sampling approaches. To this end, we performed metadynamics simulations in combination with a path collective variable derived from a machine learning classification of local structural environments, which allows the system to freely sample the complex interface structure. A disordered region of varying width forming at the migrating interface is identified as a key physical descriptor of the transformation mechanisms, facilitating the atomic shuffling and rearrangement necessary for structural transformations. Furthermore, this can directly be linked to the differences in interface mobility for distinct orientation relationships as well as the formation of interfacial ledges during the migration along low-mobility directions. Metadynamics ; Shuffling ; Interface (matter) ; Molecular dynamics ; Chemical physics ; Transformation (genetics) ; Phase (matter) ; Path (computing) ; Materials science ; Nanotechnology ; Biological system ; Computer science ; Crystallography ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Ishan Nadkarni | Haiyi Wu | N. R. Aluru
Source: Journal of Chemical Theory and Computation (article, 2023-10-04)
Field: Materials Science — Materials Chemistry

Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement We propose a data-driven framework for identifying coarse-grained (CG) Lennard-Jones (LJ) potential parameters in confined systems for simple liquids. Our approach involves the use of a Deep Neural Network (DNN) that is trained to approximate the solution of the Inverse Liquid State (ILST) problem for confined systems. The DNN model inherently incorporates essential physical characteristics specific to confined fluids, enabling an accurate prediction of inhomogeneity effects. By utilizing transfer learning, we predict single-site LJ potentials of simple multiatomic liquids confined in a slit-like channel, which effectively replicate both the fluid structure and molecular force of the target All-Atom (AA) system when the electrostatic interactions are not dominant. In addition, we showcase the synergy between the data-driven approach and the well-known Bottom-Up coarse-graining method utilizing Relative-Entropy (RE) Minimization. Through the sequential utilization of these two methods, the robustness of the iterative RE method is significantly augmented, leading to a remarkable enhancement in convergence. Granularity ; Computer science ; Robustness (evolution) ; Simple (philosophy) ; Artificial neural network ; Deep neural networks ; Minification ; Statistical physics ; Convergence (economics) ; Data-driven ; Algorithm ; Biological system ; Artificial intelligence ; Physics ; Chemistry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies
Authors: Connor Hainje | Cody A. Nizinski | SL Jackson | Richard A. Clark | Forrest D. Heller | Ian J. Schwerdt | Edgar C. Buck | David E. Meier | Alexander Hagen
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Investigation of Process History and Phenomenology of Plutonium Oxides Using Vector Quantizing Variational Autoencoder Autoencoder ; Plutonium ; Phenomenology (philosophy) ; Process (computing) ; Epistemology ; Artificial intelligence ; Chemistry ; Philosophy ; Computer science ; Radiochemistry ; Artificial neural network Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Farzaneh Khajehgili-Mirabadi | Mohammad Reza Keyvanpour
Source:  (article, 2023-12-26)
Field: Chemistry — Analytical Chemistry

Enhancing QSAR Modeling: A Fusion of Sequential Feature Selection and Support Vector Machine Quantitative Structure-Activity Relationship (QSAR) modeling is an approach employed to predict the biological response of chemical compounds by considering their structural attributes. Classification machine learning algorithms can learn patterns and relationships between chemical structure (descriptors) and biological activity from datasets and then use this knowledge to predict active or inactive compounds. This study introduces a new approach that combines Sequential Feature Selection (SFS) with the Support Vector Machine (SVM) algorithm to select the most relevant molecular descriptors for QSAR modeling. SFS and SVM work collaboratively to identify the best subset of descriptors, resulting in improved predictive accuracy. The key steps include selecting an appropriate subset of descriptors using SFS from a larger set, SVM models are built using different subsets of descriptors, and the most accurate model is chosen for final use. As shown by measuring Accuracy and precision, In our study, the SFS-SVM model demonstrated superior performance in QSAR modeling on both DKPES and PubChem datasets. It achieved the highest accuracy and precision among the compared models, with 92.87% accuracy and 86.66% precision on DKPES, and 98.18% accuracy and 98.00% precision on PubChem. This outperformed other models like SFS-LR, Cluster-GA, RF, DT, and SVM, highlighting its efficiency in feature selection and predictive accuracy. Feature selection ; Computer science ; Quantitative structure–activity relationship ; Support vector machine ; Artificial intelligence ; Selection (genetic algorithm) ; Fusion ; Feature (linguistics) ; Machine learning ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Zijian Li | Zunhong Xu | Ruichu Cai | Zhenhui Yang | Yuguang Yan | Zhifeng Hao | Guangyi Chen | Kun Zhang
Source: arXiv (Cornell University) (preprint, 2023-11-08)
Field: Materials Science — Materials Chemistry

Identifying Semantic Component for Robust Molecular Property Prediction Although graph neural networks have achieved great success in the task of molecular property prediction in recent years, their generalization ability under out-of-distribution (OOD) settings is still under-explored. Different from existing methods that learn discriminative representations for prediction, we propose a generative model with semantic-components identifiability, named SCI. We demonstrate that the latent variables in this generative model can be explicitly identified into semantic-relevant (SR) and semantic-irrelevant (SI) components, which contributes to better OOD generalization by involving minimal change properties of causal mechanisms. Specifically, we first formulate the data generation process from the atom level to the molecular level, where the latent space is split into SI substructures, SR substructures, and SR atom variables. Sequentially, to reduce misidentification, we restrict the minimal changes of the SR atom variables and add a semantic latent substructure regularization to mitigate the variance of the SR substructure under augmented domain changes. Under mild assumptions, we prove the block-wise identifiability of the SR substructure and the comment-wise identifiability of SR atom variables. Experimental studies achieve state-of-the-art performance and show general improvement on 21 datasets in 3 mainstream benchmarks. Moreover, the visualization results of the proposed SCI method provide insightful case studies and explanations for the prediction results. The code is available at: https://github.com/DMIRLAB-Group/SCI. Identifiability ; Discriminative model ; Computer science ; Generalization ; Latent variable ; Theoretical computer science ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Data mining ; Algorithm ; Natural language processing ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Karma Wangchuk | Tsheten Dorji | Parshu Ram Dhungyel | Pema Galey
Source: Zorig Melong. (article, 2023-06-15)
Field: Chemistry — Analytical Chemistry

Fruits and Vegetables Recognition System in Dzongkha Using Visual Geometry Group Network English speaking among the youth is gaining popularity in Bhutan. The Ministry of Education and Dzongkha Development Commission have been trying to promote the national language through different strategies. Furthermore, developed interesting learning materials in Dzongkha. However, youth find it difficult even to name common fruits and vegetables in Dzongkha, and this is an increasing concern. The purpose of this study was to develop an automatic fruit recognition system in Dzongkha using machine learning techniques. Ten classes of fruits and 17 classes of vegetables that are found in Bhutan are considered for the study. The fruits and vegetable datasets were downloaded from Kaggle and Websites. Furthermore, images were augmented and rotated every 15 degrees. The dataset comprised 405000 images. The model was trained and deployed using customized VGGNet and Gradio. The training and validation accuracy of the proposed model was 97.76% and 97.80% respectively. Group (periodic table) ; Geometry ; Computer science ; Artificial intelligence ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Currency Recognition and Detection
Authors: Joel Luther Thambi | Subhransu Sekhar Mohapatra | Vinod Jose Kavalakkat | U Ullas | Saibal Kanchan Barik
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2023-04-08)
Field: Materials Science — Materials Chemistry

A Combined Data Science and Simulation-Based Methodology for Efficient and Economic Prediction of Thermoplastic Performance for Automotive Industry &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;There are significant predictive tool usages by design engineers in automotive industry to capture material composition and manufacturing process-induced variables. In specific, an accurate modeling of material behavior to predict the mechanical performance of a thermoplastic part is an evolving subject in this field as one needs to consider multiple factors and steps to achieve the right prediction accuracies. The variability in prediction comes from different factors such as polymer type (filled vs. unfilled, amorphous vs semi crystalline etc.), design and manufacturing features (weldline, gate locations, thickness, notches etc.), operating conditions (temperature, moisture etc.) and finally load states (tension, compression, flexural, impact etc.). Using traditional numerical simulation-based modelling to study and validate all these factors requires significant computational time and effort. An alternative method by using data science and AI-ML models is proposed to reduce the overall validation time needed for simulation. To validate this methodology, extensive part level experiments were done on a representative cylindrical geometry to accommodate all these factors using different ULTEM™ Resin materials (PEI). The results show that by using neural network ML model, it is possible to accurately predict the structural response like maximum displacement and force. The ML model results were compared to the CAE based approaches and results overlapped with each other well within the 95% scatter band. By combining both the CAE modelling and ML modelling it is possible to accurately predict the critical structural response of applications more efficiently and economically.&lt;/div&gt;&lt;/div&gt; Automotive industry ; Artificial neural network ; Computer science ; Field (mathematics) ; Displacement (psychology) ; Paragraph ; Mechanical engineering ; Predictive modelling ; Machine learning ; Engineering ; Mathematics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced machining processes and optimization
Authors: Kristian Hovde Liland | Giorgio Luciano | Roman Svoboda
Source: Journal of Non-Crystalline Solids (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Artificial neural networks in kinetic analysis of glass crystallization: The case of complex nucleation-growth mechanisms Nucleation ; Kinetic energy ; Artificial neural network ; Robustness (evolution) ; Multilayer perceptron ; Biological system ; Convolutional neural network ; Computer science ; Pattern recognition (psychology) ; Crystallization ; Perceptron ; Artificial intelligence ; Materials science ; Chemistry ; Physics ; Thermodynamics Thermal and Kinetic Analysis ; Cultural Heritage Materials Analysis ; Phase-change materials and chalcogenides
Authors: Chenxing Yao | Jing Zhang | Liansong Gao | Chengyu Jin | Shengzhe Wang | Wenshuo Jiang | Hanrui Liang | Feng Pan | Xianru Li | Lei Ma | Huangzhao Wei | Chenglin Sun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

The Application of Sodium Percarbonate in Catalytic Wet Peroxide Oxidation Enhanced by Swirl Flow Optimized by Artificial Intelligence:Carbon Nanotubes with Ni Single Atom Sites Promote the Dual Improvement of Activated Sodium Percarbonate and Silicon Resistance Dual (grammatical number) ; Carbon nanotube ; Catalysis ; Sodium ; Activated carbon ; Atom (system on chip) ; Peroxide ; Hydrogen peroxide ; Chemical engineering ; Materials science ; Chemistry ; Nanotechnology ; Organic chemistry ; Computer science ; Metallurgy ; Adsorption ; Engineering Catalytic Processes in Materials Science ; Minerals Flotation and Separation Techniques ; Analytical chemistry methods development
Authors: Xiaoyu Yang | Zhichun Bi | Chenghui Yin | Hui Huang | Yongxin Li
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

A Novel Hybrid Sensor Array Based on the Polyphenol Oxidase and its Nanozymes Combined with the Machine Learning Based Dual Output Model to Identify Tea Polyphenols and Chinese Teas Polyphenol ; Dual (grammatical number) ; Polyphenol oxidase ; Chemistry ; Biological system ; Computer science ; Biochemistry ; Biology ; Antioxidant ; Enzyme ; Art Advanced Nanomaterials in Catalysis ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects