jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifan Dang | Kentaro Kutsukake | Xin Liu | Yoshiki Inoue | Xinbo Liu | Shota Seki | Can Zhu | Shunta Harada | Miho Tagawa | Toru Ujihara
Source: Advanced Theory and Simulations (article, 2022-07-03)
Field: Materials Science — Materials Chemistry

A Transfer Learning‐Based Method for Facilitating the Prediction of Unsteady Crystal Growth Abstract Real‐time prediction and dynamic control systems that can adapt to an unsteady environment are necessary for material fabrication processes, especially crystal growth. Recent studies have demonstrated the effectiveness of machine learning in predicting an unsteady crystal growth process, but its wider application is hindered by the large amount of training data required for sufficient accuracy. To address this problem, this study investigates the capability of transfer learning to predict geometric evolution in an unsteady silicon carbide (SiC) solution growth system based on a small amount of data. The performance of transferred models is discussed regarding the effect of the transfer learning method, training data amount, and time step length. The transfer learning strategy yields the same accuracy as that of training from scratch but requires only 20% of the training data. The accuracy is stably inherited through successive time steps, which demonstrates the effectiveness of transfer learning in reducing the required amount of training data for predicting evolution in an unsteady crystal growth process. Moreover, the transferred models trained with relatively more data (no more than 100%) further improve the accuracy inherited from the source model through multiple time steps, which broadens the application scope of transfer learning. Transfer of learning ; Computer science ; Process (computing) ; Scope (computer science) ; Artificial intelligence ; Transfer (computing) ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: Sabiha Rustam | Malachi Schram | Zexi Lu | Anne M. Chaka | W. Steven Rosenthal | Jim Pfaendtner
Source: ACS Applied Materials & Interfaces (article, 2022-07-08)
Field: Materials Science — Materials Chemistry

Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms Optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices. Although several examples of compositional intermixing at the interfaces having a positive effect on interfacial thermal conductance (ITC) have been reported, an optimum arrangement has not yet been determined because of the large number of potential atomic configurations and the significant computational cost of evaluation. On the other hand, computation-driven materials design efforts are rising in popularity and importance. Yet, the scalability and transferability of machine learning models remain as challenges in creating a complete pipeline for the simulation and analysis of large molecular systems. In this work we present a scalable Bayesian optimization framework, which leverages dynamic spawning of jobs through the Message Passing Interface (MPI) to run multiple parallel molecular dynamics simulations within a parent MPI job to optimize heat transfer at the silicon and aluminum (Si/Al) interface. We found a maximum of 50% increase in the ITC when introducing a two-layer intermixed region that consists of a higher percentage of Si. Because of the random nature of the intermixing, the magnitude of increase in the ITC varies. We observed that both homogeneity/heterogeneity of the intermixing and the intrinsic stochastic nature of molecular dynamics simulations account for the variance in ITC. Bayesian optimization ; Materials science ; Molecular dynamics ; Scalability ; Computation ; Conductance ; Computer science ; Interface (matter) ; Thermal ; Nanotechnology ; Computational science ; Algorithm ; Machine learning ; Condensed matter physics ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Shaokai Wang | Haochen Zhao
Source: Briefings in Bioinformatics (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry. Crystallization ; Protein crystallization ; Benchmark (surveying) ; Computer science ; Sequence (biology) ; Encoder ; Artificial intelligence ; Algorithm ; Chemistry ; Chemical engineering ; Engineering Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Hanqiu Wang | Aybek Rehmetulla | Shanshan Guo | Xin Kong | Zhiwei Lü | Yu Guan | Cong Xu | Kaiser Sulaiman | Gongxiang Wei | Huiqiang Liu
Source: RSC Advances (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification A single feature set is often unable to effectively classify complex biological samples due to their similar morphology and sizes. This paper proposes a protocol for the fast identification of seed medicinal materials based on micro-structural and infrared spectroscopic characteristics. Three different feature datasets, namely micro-CT, FTIR, and mixed datasets, were established <i>via</i> principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) and then used to train a back-propagation neural network. The mixed dataset consists of 34-dimensional micro-CT eigenvalues and 13-dimensional FTIR eigenvalues, optimized by PCA and CARS processing and then used to train a BP neural network. The results showed that the classification accuracy reached 89.5% for the micro-CT dataset and 93.3% for the FTIR dataset, and the classification accuracy of the mixed dataset achieved 99.2%, much higher than those of the traditional single feature datasets. This study provides a new protocol for multi-dimensional characteristic architecture with excellent performance for the classification and identification of Chinese medicinal materials. Principal component analysis ; Pattern recognition (psychology) ; Feature (linguistics) ; Fourier transform infrared spectroscopy ; Artificial intelligence ; Computer science ; Identification (biology) ; Artificial neural network ; Eigenvalues and eigenvectors ; Protocol (science) ; Feature extraction ; Set (abstract data type) ; Data mining ; Engineering ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Yang Yang | Hongxiang Zong | Xiangdong Ding | Jun Sun
Source: Applied Physics Letters (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Size-dependent ferroic phase transformations in GeSe nanoribbons Ferroic phase transformation in monolayer nanosheets or nanoribbons endows 2D nanoelectronic devices with novel functionalities. However, less is known how the phase transformation behaves with the system size. Combined with molecular dynamic simulations and a machine learning model, we systematically investigate the temperature induced ferroic phase transformation in monolayer GeSe nanoribbons, which exhibits remarkable size effect. Specifically, the transformation hysteresis is found continuously decreased with ribbon width at the investigated scales. In contrast, the transformation temperature of monolayer GeSe nanoribbons shows non-monotonic size-dependency, i.e., it is first increased and then decreased as we narrow the GeSe nanoribbons. We attribute this to a competition between the enhanced ripple deformation, which will promote phase transformation upon cooling, and the stronger edge effect that can suppress phase transformation. In addition, the two factors are well captured by the Landau model, which will deepen our understanding of phase transformation behaviors in 2D ferroic materials. Monolayer ; Materials science ; Transformation (genetics) ; Condensed matter physics ; Hysteresis ; Phase (matter) ; Ribbon ; Phase transition ; Chemical physics ; Enhanced Data Rates for GSM Evolution ; Nanotechnology ; Composite material ; Chemistry ; Physics ; Computer science 2D Materials and Applications ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Jacek Goniakowski | Sarath Menon | Gaétan Laurens | Julien Lam
Source: The Journal of Physical Chemistry C (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

Nonclassical Nucleation of Zinc Oxide from a Physically Motivated Machine-Learning Approach Observing nonclassical nucleation pathways remains challenging in simulations of complex materials with technological interests. This is because it requires very accurate force fields that can capture the whole complexity of their underlying interatomic interactions and an advanced structural analysis able to discriminate between competing crystalline phases. Here, we first report the construction and particularly thorough validation of a machine-learning force field for zinc oxide interactions using the Physical LassoLars Interaction Potentials approach which allows us to be predictive even for high-temperature dynamical systems such as ZnO melt. Then, we carried out several types of crystallization simulations and followed the formation of ZnO crystals with atomistic precision. Our results, which were analyzed using a data-driven approach based on bond order parameters, demonstrate the presence of both prenucleation clusters and two-step nucleation scenarios, thus retrieving seminal predictions of nonclassical nucleation pathways made on much simpler models. Dedicated calculations of high-temperature ZnO free energy within a newly developed automated nonequilibrium thermodynamic integration method revealed the existence of a thermodynamic bias for the predicted nonclassical nucleation scenarios. Nucleation ; Non-equilibrium thermodynamics ; Crystallization ; Statistical physics ; Materials science ; Computer science ; Field (mathematics) ; Thermodynamic integration ; Molecular dynamics ; Chemical physics ; Physics ; Chemistry ; Thermodynamics ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Crystallization and Solubility Studies
Authors: Zhenlong Hu
Source: Journal of Sensors (article, 2022-08-08)
Field: Materials Science — Polymers and Plastics

Prediction Model of Rotor Yarn Quality Based on CNN-LSTM In the whole textile industry chain, yarn production is one of the key links, which has a great impact on the quality of textile and clothing products. For a long time, the textile industry has been hoping for a yarn quality prediction technology, which can accurately predict the final yarn quality indicators according to the known conditions such as raw materials and production processes. CNN-LSTM yarn prediction model is a deep neural network model based on the assumption that the influence of textile processing time series on yarn quality is considered. CNN optimizes the input eigenvalues through one-dimensional convolution and pooling, and LSTM matches the optimized fiber performance indexes and process parameters in time series according to the processing sequence and excavates their laws, thus realizing the goal of predicting yarn quality indexes. The effects of input fiber performance index, process parameters, convolution kernel parameters, pool kernel parameters, LSTM unit number, LSTM layer number, and optimization algorithm on prediction accuracy were studied, and the parameters of CNN-LSTM model were determined. Experiments on the data set of spinning yarn show that the mean square error (MSE) of CNN-LSTM model in predicting yarn strength, Dan Qiang unevenness, evenness unevenness, and total neps is lower than that of linear regression model and BP neural network. At the same time, it is found that the prediction accuracy of CNN-LSTM model is greatly influenced by process parameters and optimization algorithm. Yarn ; Convolutional neural network ; Kernel (algebra) ; Artificial intelligence ; Convolution (computer science) ; Computer science ; Spinning ; Artificial neural network ; Process (computing) ; Deep learning ; Pattern recognition (psychology) ; Series (stratigraphy) ; Quality (philosophy) ; Mean squared error ; Mean squared prediction error ; Fiber ; Algorithm ; Engineering ; Mathematics ; Statistics ; Mechanical engineering ; Materials science Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Hamed Abdoli | T. Hermann | Simon Bickerton
Source: Composites Part A Applied Science and Manufacturing (article, 2022-08-23)
Field: Materials Science — Polymers and Plastics

A novel, fast and clean 3D permeability measurement method for multi-layer textiles and fabrics Materials science ; Volumetric flow rate ; Air permeability specific surface ; Composite material ; Permeability (electromagnetism) ; Woven fabric ; Artificial neural network ; Porosity ; Textile ; Crimp ; Biological system ; Flow (mathematics) ; Airflow ; Computer science ; Mechanics ; Mechanical engineering ; Layer (electronics) ; Artificial intelligence ; Engineering Textile materials and evaluations ; Epoxy Resin Curing Processes ; Material Properties and Processing
Authors: Tongqi Wen | Linfeng Zhang | Handong Wang | E Weinan | David J. Srolovitz
Source: arXiv (Cornell University) (preprint, 2022-03-01)
Field: Materials Science — Materials Chemistry

Deep Potentials for Materials Science To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials. Extant taxon ; Computer science ; Ab initio ; Range (aeronautics) ; Interatomic potential ; Computational science ; Artificial intelligence ; Computational chemistry ; Materials science ; Molecular dynamics ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yinsheng Zhang | Wenhao Ma | Ruiqi Hou | Dian Rong | Xiaolin Qin | Yongbo Cheng | Haiyan Wang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2022-05-04)
Field: Chemistry — Analytical Chemistry

Spectroscopic profiling-based geographic herb identification by neural network with random weights Profiling (computer programming) ; Support vector machine ; Random forest ; Artificial neural network ; Computer science ; Decision tree ; Multilayer perceptron ; Artificial intelligence ; Perceptron ; Data mining ; Pattern recognition (psychology) ; Classifier (UML) ; Machine learning Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sarat Babu Imandi | Kumari Sita | Radhakrishna Nagumantri | Rajesh K. Srivastava | Prakash Kumar Sarangi
Source: Biotechnology and Applied Biochemistry (article, 2022-11-02)
Field: Materials Science — Biomaterials

Neural networks and genetic algorithm as robust optimization tools for modeling the microbial production of poly‐β‐hydroxybutyrate (PHB) from Brewers’ spent grain This research work has been carried out to establish the combinatorial impact of various fermentation medium constituents, used for poly-β hydroxybutyrate (PHB) biosynthesis. Model development was performed with an optimized medium composition that enhanced the biosynthesis of PHB from the biowaste material Brewers' spent grain (BSG). The latter was used as a carbon substrate in submerged fermentation with Bacillus sphaericus NCIM 2478. Three independent variables: BSG, yeast extract (YE), and salt solution concentration (SS) and one dependent variable (amount of PHB produced) were assigned. A total of 35 microbial fermentation trials were conducted by which a nonlinear mathematical relationship was established in terms of neural network model between independent and dependent variables. The resulting artificial neural networks (ANNs) model for this process was further optimized using a global genetic algorithm optimization technique, which predicted the maximum production of PHB (916.31 mg/L) at a concentration of BSG (50.12 g/L), concentration of YE (0.22 g/L), and concentration of SS (24.06%, v/v). The experimental value of the quantity of PHB (concentration ∼916 mg/L) was found to be very close to the value predicted by the ANN-GA model approach. Fermentation ; Artificial neural network ; Yeast extract ; Substrate (aquarium) ; Chemistry ; Food science ; Mathematics ; Biology ; Computer science ; Artificial intelligence biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Microbial Metabolic Engineering and Bioproduction
Authors: Sathya A. Bose | Satish Babu Rajulapati | Sivasubramanian Velmurugan | Santhiagu Arockiasamy | J. Kanimozhi | Anand Kishore Kola | R. Sivashankar
Source: Chemosphere (article, 2022-11-23)
Field: Materials Science — Biomaterials

Process intensification of biopolymer polyhydroxybutyrate production by pseudomonas putida SS9: A statistical approach Response surface methodology ; Pseudomonas putida ; Polyhydroxybutyrate ; Biopolymer ; Raw material ; Pulp and paper industry ; Yield (engineering) ; Fermentation ; Bioproducts ; Mathematics ; Food science ; Biochemical engineering ; Biotechnology ; Chemistry ; Biology ; Materials science ; Engineering ; Statistics ; Polymer ; Biofuel ; Biochemistry ; Bacteria ; Organic chemistry biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Microplastics and Plastic Pollution
Authors: Sourav Bej | Meenakshi Ghosh | Riyanka Das | Priyabrata Banerjee
Source: Journal of the Indian Chemical Society (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

Evaluation of nanomaterials-grafted enzymes for application in contaminants degradation: Need of the hour with proposed IoT synchronized nanosensor fit sustainable clean water technology in en masse Pollutant ; Nanosensor ; Water treatment ; Population ; Biochemical engineering ; Potable water ; Groundwater remediation ; Environmental remediation ; Contamination ; Process engineering ; Computer science ; Chemistry ; Risk analysis (engineering) ; Environmental science ; Nanotechnology ; Environmental engineering ; Business ; Engineering Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Chen Zhang | Yewei Yu | Jingwen Xu | Miaolei Zhou
Source: IEEE Transactions on Nanotechnology (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Hysteresis Modeling and Analysis of Magnetic Shape Memory Alloy-Driven Actuator The accuracy of the micro-positioning system impelled by a magnetic shape memory alloy-driven actuator (MSMADA), is severely restricted by the frequency-dependent hysteresis nonlinearity. Moreover, the actuating accuracy is further affected by various operating factors such as load and temperature. In this study, a Duhem model (DM) identified online by a Takagi-Sugeno fuzzy neural network (TSFNN-DM) is innovatively proposed for describing the frequency-dependent hysteresis nonlinearity of the MSMADA. The DM, which has the explicit function expression, is one of the popular differential equation-based hysteresis models. However, the determination of the DM parameters is difficult and hinders its further applications. The TSFNN, which combines the advantages of easy expressing of the fuzzy inference system and self-adjustment ability of the NN, is employed to identify the DM parameters online. The rationality of the developed method is proved by a Taylor expansion in theory. Plenty of experiments verify that the proposed TSFNN-DM method is an efficient manner to capture the frequency-dependent hysteresis nonlinearity under different working conditions. Hysteresis ; Control theory (sociology) ; Nonlinear system ; Actuator ; Fuzzy logic ; Magnetic hysteresis ; Shape-memory alloy ; Computer science ; Physics ; Artificial intelligence ; Control (management) ; Magnetic field ; Condensed matter physics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems