jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qionghai Chen | Zhanjie Liu | Yongdi Huang | Anwen Hu | Wanhui Huang | Liqun Zhang | Lihong Cui | Jun Liu
Source: Langmuir (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Predicting Natural Rubber Crystallinity by a Novel Machine Learning Algorithm Based on Molecular Dynamics Simulation Data Natural rubber (NR) with excellent mechanical properties, mainly attributed to its strain-induced crystallization (SIC), has garnered significant scientific and technological interest. With the aid of molecular dynamics (MD) simulations, we can investigate the impacts of crucial structural elements on SIC on the molecular scale. Nonetheless, the computational complexity and time-consuming nature of this high-precision method constrain its widespread application. The integration of machine learning with MD represents a promising avenue for enhancing the speed of simulations while maintaining accuracy. Herein, we developed a crystallinity algorithm tailored to the SIC properties of natural rubber materials. With the data enhancement algorithm, the high evaluation value of the prediction model ensures the accuracy of the computational simulation results. In contrast to the direct utilization of small sample prediction algorithms, we propose a novel concept grounded in feature engineering. The proposed machine learning (ML) methodology consists of (1) An eXtreme Gradient Boosting (XGB) model to predict the crystallinity of NR; (2) a generative adversarial network (GAN) data augmentation algorithm to optimize the utilization of the limited training data, which is utilized to construct the XGB prediction model; (3) an elaboration of the effects induced by phospholipid and protein percentage (ω), hydrogen bond strength (ε<sub>H</sub>), and non-hydrogen bond strength (ε<sub>NH</sub>) of natural rubber materials with crystallinity prediction under dynamic conditions are analyzed by employing weight integration with feature importance analysis. Eventually, we succeeded in concluding that ε<sub>H</sub> has the most significant effect on the strain-induced crystallinity, followed by ω and finally ε<sub>NH</sub>. Crystallinity ; Natural rubber ; Algorithm ; Computer science ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Crystallization ; Materials science ; Biological system ; Chemistry ; Composite material ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Polymer crystallization and properties
Authors: Dorián László Galata | Boldizsár Zsiros | Gábor Knyihár | Orsolya Péterfi | Lilla Alexandra Mészáros | Ferenc Ronkay | Brigitta Nagy | Edina Szabó | Zsombor Kristóf Nagy | Attila Farkas
Source: International Journal of Pharmaceutics (article, 2023-04-30)
Field: Chemistry — Analytical Chemistry

Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles In this work, the capabilities of a state-of-the-art fast Raman imaging apparatus are exploited to gain information about the concentration and particle size of hydroxypropyl methylcellulose (HPMC) in sustained release tablets. The extracted information is utilized to predict the in vitro dissolution profile of the tablets. For the first time, convolutional neural networks (CNNs) are used for the processing of the chemical images of HPMC distribution and to directly predict the dissolution profile based on the image. This new method is compared to wavelet analysis, which gives a quantification of the texture of HPMC distribution, carrying information regarding both concentration and particle size. A total of 112 training and 32 validation tablets were used, when a CNN was used to characterize the particle size of HPMC, the dissolution profile of the validation tablets was predicted with an average f<sub>2</sub> similarity value of 62.95. Direct prediction based on the image had an f<sub>2</sub> value of 54.2, this demonstrates that the CNN is capable of recognizing the patterns in the data on its own. The presented methods can facilitate a better understanding of the manufacturing processes, as detailed information becomes available with fast measurements. Dissolution ; Convolutional neural network ; Particle size ; Computer science ; Biological system ; Artificial intelligence ; Wavelet transform ; Particle (ecology) ; Pattern recognition (psychology) ; Materials science ; Wavelet ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Drug Solubulity and Delivery Systems
Authors: Yewei Yu | Chen Zhang | En Wang | Miaolei Zhou
Source: IEEE Transactions on Circuits and Systems I Regular Papers (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Neural Network Adaptive Control of Magnetic Shape Memory Alloy Actuator With Time Delay Based on Composite NARMAX Model Magnetic shape memory alloy (MSMA) based actuator plays an important role in the field of micro and nano-fabrication due to its large stroke. However, the inherent hysteresis of MSMA seriously affects the application prospects of MSMA-based actuator in the field of precision positioning. In this study, a composite model is proposed for hysteresis in the MSMA-based actuator by coupling an improved fractional-order Bouc-Wen model to a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model. Based on the proposed model, a neural network adaptive control method is then used to control the MSMA-based actuator, and the controller design takes into account the effect of time delay on the system performance to improve the positioning accuracy of the MSMA-based actuator. First, we fuse the composite NARMAX model with the canonical form of the nonlinear system to describe the MSMA-based actuator. Then, a control strategy is developed to address the impact of the unknown time delay for controller design and achieve the satisfactory positioning accuracy of the MSMA-based actuator. By using Lyapunov theory, the tracking error is proven to be asymptotic convergence. Experimental studies are provided to validate the effectiveness of the proposed modeling and control schemes. Control theory (sociology) ; Actuator ; Nonlinear system ; Controller (irrigation) ; Engineering ; Convergence (economics) ; Adaptive control ; Artificial neural network ; Control engineering ; Tracking error ; Computer science ; Control (management) ; Artificial intelligence Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Ke Chen | Christian Künkel | Bingqing Cheng | Karsten Reuter | Johannes T. Margraf
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Physics-inspired machine learning of localized intensive properties Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a 'local energy'-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations. Pooling ; Scaling ; Statistical physics ; Work (physics) ; Excitation ; Artificial neural network ; Atomic orbital ; Linear scale ; Scale (ratio) ; Computer science ; Physics ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Linkai Han | Zhonghua Xiang
Source: Fundamental Research (review, 2023-12-10)
Field: Materials Science — Materials Chemistry

Intelligent design and synthesis of energy catalytic materials Efficient energy conversion and storage are crucial for the sustainable development and growth of renewable energy sources. However, the limited varieties of traditional energy catalytic materials cannot match the fast-expansion requirement of raising various clean energy for industrial applications. Thus, accelerating the design and synthesis of high-performance catalysts is necessary for the application of energy equipment. Recently, with artificial intelligence (AI) technology being advanced by leaps and bounds, it is feasible to efficiently and precisely screen materials and optimize synthesis conditions in a huge unknown space. Here, we introduce and review AI techniques used in the development of catalytic materials in detail. We describe the workflow for designing and synthesizing new materials using machine learning (ML) and robotics. We summarize the sources of data collection, the intelligent algorithms commonly used to build ML models, and the laboratory modules for the intelligent synthesis of materials. We provide the illustrations of predicting the properties of catalytic materials with ML assistance in different material types. In addition, we present the potential strategies for finding material synthesis pathways, and advances in robotics to accelerate high-performance catalytic materials synthesis in the review. Finally, the summary, challenges, and potential directions in the development of AI-assisted catalytic materials are presented and discussed. Workflow ; Computer science ; Robotics ; LEAPS ; Renewable energy ; Artificial intelligence ; Process engineering ; Systems engineering ; Nanotechnology ; Biochemical engineering ; Robot ; Materials science ; Engineering ; Database ; Electrical engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Shusen Chen | Zack Meyer | Brendan Jensen | Alex Kraus | Allison Lambert | Daniel H. Ess
Source: Journal of Chemical Information and Modeling (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here, we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library, ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. We also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion. Denticity ; Ligand (biochemistry) ; Reactivity (psychology) ; Chemistry ; Metal ; Combinatorial chemistry ; Catalysis ; Molecule ; Computer science ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; X-ray Diffraction in Crystallography
Authors: Kavian Khosravinia | Amirkianoosh Kiani
Source: iScience (article, 2023-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unlocking pseudocapacitors prolonged electrode fabrication via ultra-short laser pulses and machine learning Pseudocapacitors outperform lithium-ion batteries in terms of charging rate and power density. However, their electrode manufacturing procedures are prolonged and environmentally unfriendly, posing a research challenge. To address this issue, the one-step synthesis approach of ultra-short laser pulses for <i>in situ</i> nanostructure generation (ULPING) has been proposed. The generated nanostructures on the substrate through ULPING depend on the laser parameters, which leaves room for improvement in this field. The present study aims to build a theoretical bridge between the laser parameters used in the fabrication of pseudocapacitors and their electrochemical performance through machine learning approaches. Gaussian process regression (GPR), random forest (RF), and artificial neural network (ANN) have been employed to mimic the electrochemical behavior of pseudocapacitors, demonstrating the potential of ULPING in generating nanostructures on transition metals that can serve as pseudocapacitor electrodes. This research presents a promising method for producing binder-free and carbon-free pseudocapacitor electrodes efficiently and sustainably. Pseudocapacitor ; Materials science ; Nanotechnology ; Electrode ; Fabrication ; Computer science ; Nanostructure ; Electrochemistry ; Supercapacitor ; Chemistry Supercapacitor Materials and Fabrication ; Neuroscience and Neural Engineering ; Electrochemical sensors and biosensors
Authors: Chao Liang | Yilimiranmu Rouzhahong | Caiyuan Ye | Chong Li | Biao Wang | Huashan Li
Source: Nature Communications (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Material symmetry recognition and property prediction accomplished by crystal capsule representation Learning the global crystal symmetry and interpreting the equivariant information is crucial for accurately predicting material properties, yet remains to be fully accomplished by existing algorithms based on convolution networks. To overcome this challenge, here we develop a machine learning (ML) model, named symmetry-enhanced equivariance network (SEN), to build material representation with joint structure-chemical patterns, to encode important clusters embedded in the crystal structure, and to learn pattern equivariance in different scales via capsule transformers. Quantitative analyses of the intermediate matrices demonstrate that the intrinsic crystal symmetries and interactions between clusters have been exactly perceived by the SEN model and critically affect the prediction performances by reducing effective feature space. The mean absolute errors (MAEs) of 0.181 eV and 0.0161 eV/atom are obtained for predicting bandgap and formation energy in the MatBench dataset. The general and interpretable SEN model reveals the potential to design ML models by implicitly encoding feature relationship based on physical mechanisms. Property (philosophy) ; Symmetry (geometry) ; Representation (politics) ; Capsule ; Computer science ; Crystal (programming language) ; Artificial intelligence ; Biology ; Mathematics ; Geometry ; Law Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jinbin Zhao | Peitao Liu | Jian-Tao Wang | Jiangxu Li | Haiyang Niu | Yan Sun | Junlin Li | Xing‐Qiu Chen
Source: The European Physical Journal Plus (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Hardness and fracture toughness models by symbolic regression Fracture toughness ; Moduli ; Materials science ; Toughness ; Shear (geology) ; Symbolic regression ; Artificial neural network ; Graph ; Composite material ; Computer science ; Machine learning ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Advanced materials and composites ; Nuclear Materials and Properties
Authors: Lai Wei | Nihang Fu | Yuqi Song | Qian Wang | Jianjun Hu
Source: Journal of Cheminformatics (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Probabilistic generative transformer language models for generative design of molecules Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose the Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering with molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer. Generative grammar ; Computer science ; Transformer ; Probabilistic logic ; Generative Design ; Generative model ; Artificial intelligence ; Language model ; Machine learning ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jiawei Wu | Dingming Chen | Jianfu Chen | Haifeng Wang
Source: The Journal of Physical Chemistry C (article, 2023-03-22)
Field: Materials Science — Materials Chemistry

Structural and Composition Evolution of Palladium Catalyst for CO Oxidation under Steady-State Reaction Conditions Understanding how the composition and structure of heterogeneous catalysts change during in situ reaction is of critical importance but greatly challenging because of the complexity of multiple time–space scale. Herein, we propose a self-adaptive simulation strategy driven by the first-principles microkinetic modeling and genetic-algorithm-based global structural search accelerated by machine learning that allows the interplay of surface reaction and catalyst evolution, and uncover the structural/compositional evolution of a Pd(111) single-crystal catalyst under the reaction conditions for CO oxidation, which is a classic open problem lasting for decades. The possible active phases at the kinetically steady state are identified and their common nature is unraveled. We show that, in the presence of CO/O2 reaction mixtures, a dynamically stable partially oxidized nonstoichiometric palladium oxide (PdO0.44 layer) grown on Pd(111) is formed with the O adatoms inserted into the Pd(111) sublayer that drives the formation of the surface oxide. Remarkably, the first-principles microkinetic analyses demonstrate that this self-evolved PdO0.44 surface at the steady state exhibits higher catalytic activity for CO oxidation as compared with Pd(111) and overoxidized PdO catalyst, which may explain the long-standing puzzle of the “PdOx” active phase for Pd catalyst during CO oxidation. Catalysis ; Palladium ; Oxide ; Redox ; Chemical engineering ; Chemistry ; Heterogeneous catalysis ; Oxidation state ; Steady state (chemistry) ; Reaction rate ; Materials science ; Inorganic chemistry ; Chemical physics ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Hee‐Beom Lee | Min‐Hyoung Jung | Young‐Hoon Kim | Eun‐Byeol Park | Woo‐Sung Jang | Seon-Je Kim | Ki-ju Choi | Ji Young Park | Keebum Hwang | Jae‐Hyun Shim | Songhun Yoon | Young‐Min Kim
Source: Journal of Analytical Science & Technology (article, 2023-11-21)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning image segmentation for the reliable porosity measurement of high-capacity Ni-based oxide cathode secondary particles Abstract The optimization of geometrical pore control in high-capacity Ni-based cathode materials is required to enhance the cyclic performance of lithium-ion batteries. Enhanced porosity improves lithium-ion mobility by increasing the electrode–electrolyte contact area and reducing the number of ion diffusion pathways. However, excessive porosity can diminish capacity, thus necessitating optimizing pore distribution to compromise the trade-off relation. Accordingly, a statistically meaningful porosity estimation of electrode materials is required to engineer the local pore distribution inside the electrode particles. Conventional scanning electron microscopy (SEM) image-based porosity measurement can be used for this purpose. However, it is labor-intensive and subjected to human bias for low-contrast pore images, thereby potentially lowering measurement accuracy. To mitigate these difficulties, we propose an automated image segmentation method for the reliable porosity measurement of cathode materials using deep convolutional neural networks specifically trained for the analysis of porous cathode materials. Combined with the preprocessed SEM image datasets, the model trained for 100 epochs exhibits an accuracy of &gt; 97% for feature segmentation with regard to pore detection on the input datasets. This automated method considerably reduces manual effort and human bias related to the digitization of pore features in serial section SEM image datasets used in 3D electron tomography. Graphical abstract Porosity ; Materials science ; Cathode ; Scanning electron microscope ; Electrolyte ; Convolutional neural network ; Electrode ; Computer science ; Segmentation ; Artificial intelligence ; Composite material ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Neural Network Applications ; Industrial Vision Systems and Defect Detection