jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jia-Hao 佳豪 Xiong 熊 | Zi-Jun 梓俊 Qi 戚 | Kang 康 Liang 梁 | Xiang 祥 Sun 孙 | Zhan-Peng 展鹏 Sun 孙 | Qi-Jun 启军 Wang 汪 | Li-Wei 黎玮 Chen 陈 | Gai 改 Wu 吴 | Wei 威 Shen 沈
Source: Chinese Physics B (article, 2023-07-06)
Field: Materials Science — Materials Chemistry

Molecular dynamics study of thermal conductivities of cubic diamond, lonsdaleite, and nanotwinned diamond via machine-learned potential Diamond is a wide-bandgap semiconductor with a variety of crystal configurations, and has the potential applications in the field of high-frequency, radiation-hardened, and high-power devices. There are several important polytypes of diamonds, such as cubic diamond, lonsdaleite, and nanotwinned diamond (NTD). The thermal conductivities of semiconductors in high-power devices at different temperatures should be calculated. However, there has been no reports about thermal conductivities of cubic diamond and its polytypes both efficiently and accurately based on molecular dynamics (MD). Here, using interatomic potential of neural networks can provide obvious advantages. For example, comparing with the use of density functional theory (DFT), the calculation time is reduced, while maintaining high accuracy in predicting the thermal conductivities of the above-mentioned three diamond polytypes. Based on the neuroevolution potential (NEP), the thermal conductivities of cubic diamond, lonsdaleite, and NTD at 300 K are respectively 2507.3 W⋅m −1 ⋅K −1 , 1557.2 W⋅m −1 ⋅K −1 , and 985.6 W⋅m −1 ⋅K −1 , which are higher than the calculation results based on Tersoff-1989 potential (1508 W⋅m −1 ⋅K −1 , 1178 W⋅m −1 ⋅K −1 , and 794 W⋅m −1 ⋅K −1 , respectively). The thermal conductivities of cubic diamond and lonsdaleite, obtained by using the NEP, are closer to the experimental data or DFT data than those from Tersoff-potential. The molecular dynamics simulations are performed by using NEP to calculate the phonon dispersions, in order to explain the possible reasons for discrepancies among the cubic diamond, lonsdaleite, and NTD. In this work, we propose a scheme to predict the thermal conductivity of cubic diamond, lonsdaleite, and NTD precisely and efficiently, and explain the differences in thermal conductivity among cubic diamond, lonsdaleite, and NTD. Diamond ; Materials science ; Thermal conductivity ; Molecular dynamics ; Phonon ; Thermal ; Semiconductor ; Condensed matter physics ; Crystal (programming language) ; Nanotechnology ; Thermodynamics ; Optoelectronics ; Composite material ; Computational chemistry ; Physics ; Chemistry Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Machine Learning in Materials Science
Authors: Vicente Orts Mercadillo | Happiness V. Ijije | Luke Chaplin | Ian A. Kinloch | Mark A. Bissett
Source: 2D Materials (article, 2023-03-02)
Field: Materials Science — Materials Chemistry

Novel techniques for characterising graphene nanoplatelets using Raman spectroscopy and machine learning Abstract A significant challenge for graphene nanoplatelet (GNP) suppliers is the characterisation of platelet morphology in industrial environments. This challenge is further exacerbated to platelet surface chemistry when scalable functionalisation processes, such as plasma treatment, are used to modify the GNPs to improve the filler-matrix interphase in nanocomposites. The costly and complex suite of analytical equipment necessary for a complete material description makes quality control and process optimisation difficult. Raman spectroscopy is a facile and accessible characterisation technique, with recent advancements unlocking fast mapping for rapid data collection. In this study, we develop novel techniques to better characterise GNP morphology and changes in surface chemistry using Raman maps of bulk powders. Providing a bespoke algorithmic framework for the analysis of these advanced materials. An unsupervised peak fitting and processing algorithm was used to extract crystallinity data and correlate it with laser-diffraction-derived lateral size values for a commercial set of GNPs rapidly and accurately. Classical machine learning was used to identify the most informative Raman features for classifying the plasma-functionalised GNPs. The initial material properties were found to affect the peak features that were the most useful for classification. In low defect density and low specific surface area GNPs, the D peak full width at half maximum is found to be the most useful, whereas the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>I</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>2D</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow> <mml:msub> <mml:mi>I</mml:mi> <mml:mrow> <mml:mtext>G</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ratio is the most useful in the opposite case. Finally, a convolutional neural network was trained to discern between different GNP grades with 86% accuracy. This work demonstrates how computer vision could be deployed for rapid and accurate quality control on the factory floor. Raman spectroscopy ; Graphene ; Materials science ; Nanotechnology ; Spectroscopy ; Physics ; Optics Graphene research and applications ; Graphene and Nanomaterials Applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Qingjin Chi | Linbo Tian | Rongqi Xu | Zhao Wang | Fengrong Zhao | Kegang Guo | Zhaowen Liang | Jinbao Xia | Sasa Zhang
Source: ACS Omega (article, 2023-03-30)
Field: Chemistry — Spectroscopy

Dual-Comb Gas Sensor Integrated with a Neural Network-Based Spectral Decoupling Algorithm of Overlapped Spectra for Gas Mixture Sensing Cross-interference among absorptions severely affects the ability to achieve accurate gas concentration retrieval through gas molecular specificity. In this study, a novel dual gas sensor was proposed to separate methane and water absorbance from the blended spectra of their mixture in the mid-infrared (MIR) band by employing a neural network algorithm. To address the scarcity of experimental data, the neural network was trained over a simulated data set constructed with the same distribution as the experimental ones. The system takes advantages of the broadband spectra to provide high-quality comb data and allows the neural network to establish an accurate spectral decoupling function. In addition, a feature absorption peak screening mechanism was proposed to achieve more accurate concentration retrieval, which avoids the prediction error introduced by interrogating the only peak of the separated spectra. The promising results of the systematic evaluation have demonstrated the feasibility of our methods in practical detections. Decoupling (probability) ; Artificial neural network ; Spectral line ; Computer science ; Algorithm ; Broadband ; Biological system ; Artificial intelligence ; Engineering ; Physics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Shamim Pourrahimi | Soroosh Hakimian | Abdel‐Hakim Bouzid | Lucas A. Hof
Source: Metals (article, 2023-08-13)
Field: Materials Science — Materials Chemistry

On the Use of Machine Learning Algorithms to Predict the Corrosion Behavior of Stainless Steels in Lactic Acid Predicting the corrosion behavior of materials in specific environmental conditions is important for establishing a sustainable manufacturing system while reducing the need for time-consuming experimental investigations. Recent studies started to explore the application of supervised Machine Learning (ML) techniques to forecast corrosion behavior in various conditions. However, there is currently a research gap in utilizing classification ML techniques specifically for predicting the corrosion behavior of stainless steel (SS) material in lactic acid-based environments, which are extensively used in the pharmaceutical and food industry. This study presents a ML-based prediction model for corrosion behavior of SSs in different lactic acid environmental conditions, using a database that described the corrosion behavior by qualitative labels. Decision tree (DT), random forest (RF) and support vector machine (SVM) algorithms were applied for classification. Training and testing accuracies of, respectively 97.5% and 92.5% were achieved using the DT classifier. Four SS alloy composition elements (C, Cr, Ni, Mo), acid concentration, and temperature were found sufficient to consider as input data for corrosion prediction. The developed models are reliable for predicting corrosion degradation and, as such, contribute to avoiding failures and catastrophes in industry. Corrosion ; Random forest ; Support vector machine ; Lactic acid ; Machine learning ; Classifier (UML) ; Decision tree ; Artificial intelligence ; Alloy ; Computer science ; Materials science ; Process engineering ; Algorithm ; Metallurgy ; Engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Pascal Pernot
Source: APL Machine Learning (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Calibration in machine learning uncertainty quantification: Beyond consistency to target adaptivity Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is becoming the focus of many studies in materials and chemical science. It is now well understood that average calibration is insufficient, and most studies implement additional methods for testing the conditional calibration with respect to uncertainty, i.e., consistency. Consistency is assessed mostly by so-called reliability diagrams. There exists, however, another way beyond average calibration, which is conditional calibration with respect to input features, i.e., adaptivity. In practice, adaptivity is the main concern of the final users of the ML-UQ method, seeking the reliability of predictions and uncertainties for any point in the feature space. This article aims to show that consistency and adaptivity are complementary validation targets and that good consistency does not imply good adaptivity. An integrated validation framework is proposed and illustrated with a representative example. Consistency (knowledge bases) ; Calibration ; Reliability (semiconductor) ; Computer science ; Machine learning ; Feature (linguistics) ; Point (geometry) ; Uncertainty quantification ; Focus (optics) ; Data mining ; Weak consistency ; Artificial intelligence ; Mathematics ; Strong consistency ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Brigitte Walla | Daniel Bischoff | Iliana Corona Viramontes | Sergio Montes Figueredo | Dirk Weuster‐Botz
Source: Crystals (article, 2023-05-06)
Field: Materials Science — Materials Chemistry

Recent Advances in the Monitoring of Protein Crystallization Processes in Downstream Processing Protein crystallization is nowadays performed at the micro to macro scale in academia and industry, being particularly interesting for pharmaceutical applications. Protein crystallization offers an attractive alternative to chromatography as a downstream processing step in the biotechnology industry, but also in the food and chemical industries. Monitoring of the protein crystallization process is required to understand the crystal growth mechanism and to obtain the information necessary for efficient process control, which needs to comply with the critical quality attributes of the product. Since a wide range of monitoring techniques have already been developed and established, this review focuses on the recent advances of selected techniques in monitoring protein crystallization processes such as the focused beam reflectance method (FBRM), and machine learning-based image analysis for solid-phase monitoring, as well as the spectroscopic methods for liquid-phase monitoring, such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) and UV/Vis spectroscopy. Crystallization ; Downstream processing ; Process analytical technology ; Protein crystallization ; Process engineering ; Fourier transform infrared spectroscopy ; Downstream (manufacturing) ; Process (computing) ; Materials science ; Environmental science ; Biochemical engineering ; Computer science ; Nanotechnology ; Chemistry ; Chemical engineering ; Engineering ; Chromatography ; Bioprocess Crystallization and Solubility Studies ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Linna An | Meerit Y. Said | Long Tran | Sagardip Majumder | Inna Goreshnik | Gyu Rie Lee | David Juergens | Justas Dauparas | Ivan Anishchenko | Brian Coventry | Asim K. Bera | Alex Kang | Paul M. Levine | Valentina Álvarez | Arvind Pillai | Christoffer Norn | David Feldman | Dmitri Zorine | Derrick R. Hicks | Xinting Li | M. SANCHEZ | Dionne Vafeados | Patrick J. Salveson | Anastassia A. Vorobieva | David Baker
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-12-21)
Field: Chemistry — Organic Chemistry

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition. Molecule ; Materials science ; Nanotechnology ; Computer science ; Chemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Eisuke F. Sato | Gaku Tachiwaki | Mayu Fujii | Koichi Mitsudo | Takashi Washio | Shinobu Takizawa | Seiji Suga
Source: Organic Process Research & Development (article, 2023-01-27)
Field: Materials Science — Materials Chemistry

Electrochemical Carbon-Ferrier Rearrangement Using a Microflow Reactor and Machine Learning-Assisted Exploration of Suitable Conditions The use of a flow reactor in electrolysis enables efficient and scalable synthesis, which is normally difficult to accomplish by batch reactors. We achieved electrochemical carbon-Ferrier rearrangement which proceeded with catalytic anodic oxidation, and this transformation could be performed using an electrochemical flow reactor. Additional numeric parameters derived from the flow reactor could be adjusted using Gaussian process regression (GPR), which is a machine learning method. GPR enables the construction of two models to estimate yields and productivity, and the reaction condition can be selected rationally. Electrolysis ; Electrochemistry ; Anode ; Kriging ; Flow chemistry ; Process engineering ; Flow (mathematics) ; Process (computing) ; Computer science ; Chemistry ; Catalysis ; Electrode ; Engineering ; Organic chemistry ; Mechanics ; Machine learning ; Physics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electrochemical Analysis and Applications
Authors: Haojie Mei | Liang Chen | Feifei Wang | Guisen Liu | Jing Hu | Weitong Lin | Yao Shen | Jinfu Li | Lingti Kong
Source: Journal of Nuclear Materials (article, 2023-10-22)
Field: Materials Science — Materials Chemistry

Development of machine learning and empirical interatomic potentials for the binary Zr-Sn system Zirconium ; Materials science ; Interatomic potential ; Density functional theory ; Tin ; Atomic radius ; Molecular dynamics ; Metallurgy ; Chemistry ; Computational chemistry Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Zhutian Ding | Annabella Selloni
Source: The Journal of Chemical Physics (article, 2023-07-10)
Field: Chemistry — Electrochemistry

Modeling the aqueous interface of amorphous TiO2 using deep potential molecular dynamics Amorphous titanium dioxide (a-TiO2) is widely used as a coating material in applications such as electrochemistry and self-cleaning surfaces where its interface with water has a central role. However, little is known about the structures of the a-TiO2 surface and aqueous interface, particularly at the microscopic level. In this work, we construct a model of the a-TiO2 surface via a cut-melt-and-quench procedure based on molecular dynamics simulations with deep neural network potentials (DPs) trained on density functional theory data. After interfacing the a-TiO2 surface with water, we investigate the structure and dynamics of the resulting system using a combination of DP-based molecular dynamics (DPMD) and ab initio molecular dynamics (AIMD) simulations. Both AIMD and DPMD simulations reveal that the distribution of water on the a-TiO2 surface lacks distinct layers normally found at the aqueous interface of crystalline TiO2, leading to an ∼10 times faster diffusion of water at the interface. Bridging hydroxyls (Ti2-ObH) resulting from water dissociation decay several times more slowly than terminal hydroxyls (Ti-OwH) due to fast Ti-OwH2 → Ti-OwH proton exchange events. These results provide a basis for a detailed understanding of the properties of a-TiO2 in electrochemical environments. Moreover, the procedure of generating the a-TiO2-interface employed here is generally applicable to studying the aqueous interfaces of amorphous metal oxides. Molecular dynamics ; Amorphous solid ; Aqueous solution ; Materials science ; Chemical physics ; Bridging (networking) ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Crystallography ; Computer science Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Lennart Schmies | Matthias Hemmleb | Dirk Bettge
Source: Engineering Failure Analysis (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Relevant input data for crack feature segmentation with deep learning on SEM imagery and topography data Hyperparameter ; Artificial neural network ; Artificial intelligence ; Computer science ; Segmentation ; Fracture (geology) ; Deep learning ; Data set ; Set (abstract data type) ; Data acquisition ; Machine learning ; Data quality ; Pattern recognition (psychology) ; Data mining ; Engineering ; Geotechnical engineering Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Fang Li | Geng Chen | Taili Dong | Changjun Zhu | Kanghua Chen
Source: International Journal of Mechanical Sciences (article, 2023-05-09)
Field: Materials Science — Materials Chemistry

Microplastic deformation activating residual stress relief for Al alloy Residual stress ; Materials science ; Composite material ; Stress (linguistics) ; Alloy ; Dislocation ; Residual ; Thermal ; Shear stress ; Metallurgy ; Thermodynamics ; Computer science Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: Xiaoxin Lu | Jiabin Huang | Nan Cheng | Zhuang Cheng | Linlin Ren | Jianbin Xu | Rong Sun | Jibao Lu
Source: ACS Applied Materials & Interfaces (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Data-Driven Framework toward Accurate Prediction of Interfacial Thermal Resistance in Particulate-Filled Composites The interfacial thermal resistance (ITR) inside the particulate-filled polymer composite is a bottleneck for improving the thermal conductivity (TC) of the material. Getting full knowledge of the ITR is crucial to the material design as well as to a faithful prediction of TC of the composite. However, a method fully taking into account the local circumstances inside the composite is yet to be developed to precisely characterize the ITR. Here, we propose a comprehensive framework combining high-throughput numerical simulations, machine learning and optimization algorithms, and experiments, which is demonstrated to be robust for the accurate determination of ITRs inside the particulate-filled composites. The strategy extracts as much information as possible about the structure and heat transfer characteristics of the composite based on simple experiments, which lays the foundation for the method to be effective. We show that the polymer-filler ITRs and the effective filler-filler contact ITRs predicted with the method faithfully represent the true characteristics inside the composite materials; they also provide the exact effective parameters, which cannot be obtained from experiments, for accurate numerical prediction of TCs of composite materials with high efficiency. As a result, the framework not only provides a robust tool for accurate characterization of ITRs inside composites but also paves the way for virtual high-throughput formula screening of thermally conductive composite materials that could be used in industrial product design. Materials science ; Composite number ; Composite material ; Thermal conductivity ; Filler (materials) ; Thermal ; Bottleneck ; Particulates ; Characterization (materials science) ; Computer science ; Nanotechnology Thermal properties of materials ; Composite Material Mechanics ; Heat Transfer and Optimization