jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kongping Wu | Leng Zhang | Danbei Wang | Fangzhen Li | Pengzhan Zhang | Liwen Sang | Meiyong Liao | Kun Tang | Jiandong Ye | Shulin Gu
Source: Scientific Reports (article, 2022-11-19)
Field: Materials Science — Materials Chemistry

A comparative study of interfacial thermal conductance between metal and semiconductor To understand and control thermal conductance of interface between metal and semiconductor has now become a crucial task for the thermal design and management of nano-electronic and micro-electronic devices. The interfacial alignments and electronic characteristics of the interfaces between metal and semiconductor are studied using a first-principles calculation based on hybrid density functional theory. The thermal conductance of interfaces between metal and semiconductor were calculated and analyzed using diffuse mismatch model, acoustic mismatch model and nonequilibrium molecular dynamics methods. Especially, according to nonequilibrium molecular dynamics, the values of thermal conductance were obtained to be 32.55 MW m<sup>-2</sup> K<sup>-1</sup> and 341.87 MW m<sup>-2</sup> K<sup>-1</sup> at C-Cu and Si-Cu interfaces, respectively. These results of theoretical simulation calculations are basically consistent with the current experimental data, which indicates that phonon-phonon interaction play a more important role than electron-phonon interaction during heat transport. It may be effective way to improve the interfacial thermal conductance through enhancing the interface coupling strength at the metal-semiconductor interface because the strong interfacial scattering plays a role in suppressing in the weaker interface coupling heterostructure, leading to the lower thermal conductance of interfaces. This could provide a beneficial reference for the design of the Schottky diode and thermal management at the interfaces between metal and semiconductor. Materials science ; Heterojunction ; Thermal conductivity ; Phonon ; Semiconductor ; Conductance ; Phonon scattering ; Coupling (piping) ; Non-equilibrium thermodynamics ; Condensed matter physics ; Schottky diode ; Schottky barrier ; Thermal ; Diode ; Optoelectronics ; Thermodynamics ; Physics ; Composite material Thermal properties of materials ; Semiconductor materials and interfaces ; Nanowire Synthesis and Applications
Authors: Brian DeCost | Howie Joress | Suchismita Sarker | Apurva Mehta | Jason Hattrick‐Simpers
Source: JOM (article, 2022-06-21)
Field: Materials Science — Materials Chemistry

Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell Characterization (materials science) ; Automation ; Alloy ; Corrosion ; Throughput ; Black box ; Nanotechnology ; Computer science ; Materials science ; Systems engineering ; Process engineering ; Mechanical engineering ; Engineering ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Zhiyong Zou | L. Wang | Jie CHEN | Long Tao | Qingsong Wu | Man Zhou
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Research on peanut variety classification based on hyperspectral image The classification algorithms of different peanut varieties were studied based on hyperspectral imaging technology. Firstly, the spectral images of five peanut species were collected by hyperspectral instrument produced by Zhuolihanguang Co., LTD. Then SpacVIEW was used to correct the spectral images in black and white, and ENVI5.1 was used to extract the interest in the spectral image of each peanut and calculate the mean spectral reflection value of the region. The spectral characteristic curves of the five peanut samples all showed certain differences, which lays a foundation for the feasibility of modeling in the next step. In order to eliminate the influence of non-quality factor information in hyperspectral spectral data, a variety of data preprocessing methods were used to eliminate noise in the original spectral data, and XGBoost, LightGBM, CatBoost and GBDT algorithms were used to extract feature bands. XGBoost and LightGBM were then used for classification modeling of extracted feature bands. In the classification model, both XGBoost and LightGBM can reach 99.33%, while other performance evaluation indexes cannot distinguish these two models well. Therefore, Optuna algorithm was selected to optimize the two algorithms respectively. After optimization, both LightGBM and XGBoost have improved to varying degrees, but LightGBM is relatively obvious, especially in fit_time, which is 11 times faster than XGBoost and 16 times faster than before optimization. Therefore, the best classification algorithm selected in this study is MF-LightGBM-LightGBM-Optuna-LightGBM. The research of peanut classification method provides a strong theoretical basis and technical support for the revitalization of rural industry, the integration of peanut agriculture and industry and the acceleration of the modernization of agricultural industry system, production system and management system. Hyperspectral imaging ; Preprocessor ; Feature (linguistics) ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Computer science ; Mathematics ; Data mining Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote Sensing and Land Use
Authors: Raymundo Arróyave
Source: Journal of Phase Equilibria and Diffusion (article, 2022-11-06)
Field: Materials Science — Materials Chemistry

Phase Stability Through Machine Learning Understanding the phase stability of a chemical system constitutes the foundation of materials science. Knowledge of the equilibrium state of a system under arbitrary thermodynamic conditions provides valuable information about the types of phases that are likely to be synthesized and how to get there. Accessing the phase diagram in a materials system provides one with the information necessary to design materials and microstructures with optimal properties. While the materials science community has long been focused on exploiting this knowledge to navigate the materials space, recent advances in machine learning (ML) and artificial intelligence (AI) have provided the community with novel ways of interrogating the materials thermodynamics space. This work presents some of the most recent advances in ML/AI applied to phase stability and thermodynamics of materials. Prof. John Morral always had a passion for understanding and teaching the fundamental characteristics of phase diagrams. This review is written to honor his memory. Phase diagram ; Stability (learning theory) ; Computer science ; Thermodynamics ; Phase equilibrium ; Artificial intelligence ; Phase (matter) ; Work (physics) ; Space (punctuation) ; Materials science ; Machine learning ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Gayane Chilingaryan | Hovhannes Tamoyan | Ani Tevosyan | Nelly Babayan | Lusine Khondkaryan | Karen Hambardzumyan | Zaven Navoyan | Hrant Khachatrian | Armen Aghajanyan
Source: arXiv (Cornell University) (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

BARTSmiles: Generative Masked Language Models for Molecular Representations We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through a series of tailored, in-depth ablations. Using this pre-training strategy, we train BARTSmiles, a BART-like model with an order of magnitude more compute than previous self-supervised molecular representations. In-depth evaluations show that BARTSmiles consistently outperforms other self-supervised representations across classification, regression, and generation tasks setting a new state-of-the-art on 11 tasks. We then quantitatively show that when applied to the molecular domain, the BART objective learns representations that implicitly encode our downstream tasks of interest. For example, by selecting seven neurons from a frozen BARTSmiles, we can obtain a model having performance within two percentage points of the full fine-tuned model on task Clintox. Lastly, we show that standard attribution interpretability methods, when applied to BARTSmiles, highlight certain substructures that chemists use to explain specific properties of molecules. The code and the pretrained model are publicly available. Interpretability ; Computer science ; Generative grammar ; Task (project management) ; Generative model ; Artificial intelligence ; Language model ; Code (set theory) ; ENCODE ; Natural language processing ; Machine learning ; Series (stratigraphy) ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Bowen Liu | Fangcheng Lv | Xiaozhou Fan | Yuxuan Li | Bowen Jiang
Source: Polymers (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Study of the Influence of Nano SiO2 on the Thermodynamic Properties of PMIA Composites The poly-m-phenyleneisophthalamide (PMIA) is widely used in the electrical field due to its numerous favorable characteristics, but its poor thermal conductivity limits its application. In this study, PMIA was modified with nano-silica (SiO<sub>2</sub>) to improve its thermal and mechanical properties. Using iso-phthalic acid and m-phenylenediamine as monomers, the changes in the thermodynamic properties and microstructure parameters of SiO<sub>2</sub>-modified PMIA were analyzed using molecular dynamics before and after modification in the temperature range of 250~450 K. It was found that adding SiO<sub>2</sub> improves the Young's modulus and Shear modulus of PMIA, and the mechanical properties of PMIA, and SiO<sub>2</sub>/PMIA composites deteriorate with increasing temperature, but the mechanical properties of SiO<sub>2</sub>/PMIA composites are always better than those of pure PMIA in the temperature range of electrical equipment. Meanwhile, after doping SiO<sub>2</sub> with the radius of 8 Å, the glass transition temperature of PMIA increases by 27.11 K, and its thermal conductivity increases from 0.249 W m<sup>-1</sup> K<sup>-1</sup> to 0.396 W m<sup>-1</sup> K<sup>-1</sup>. When SiO<sub>2</sub> is added to PMIA, the thermal expansion coefficient of PMIA will decrease in both glass and rubber states, and its thermal stability will improve. In terms of microstructure parameters, the free volume distribution of the SiO<sub>2</sub>/PMIA model is less easily dispersed than that of the PMIA model, indicating that the addition of SiO<sub>2</sub> can improve the related properties of PMIA by hindering the movement of molecular chains. Materials science ; Composite material ; Molecular dynamics ; Nano- ; Dynamics (music) ; Composite number ; Chemistry ; Computational chemistry Thermal properties of materials ; Polymer Nanocomposites and Properties ; Graphene research and applications
Authors: Peng Dong | Tingna Shao | Yu Zhao | Ji Dong | Yong Yang | Xinhong Zhao | Hongwei Li | Junying Tian | Dongliang Wang | Guixian Li
Source: Processes (article, 2022-04-29)
Field: Chemistry — Inorganic Chemistry

Progress of Methylation of C6-8~Arene with Methanol: Mechanism, Catalysts, Kinetic/Thermodynamics and Perspectives High-efficiency production of xylene/trimethylbenzene from benzene/toluene with methanol is a potential way to promote the implementation on the coupled cycle development strategy of petrochemical and coal chemical industry and optimize the resource structure. At the same time, relying on the innovation of catalytic new materials, physical chemistry, intelligent capture technology, process engineering, and other principles and methods, p-xylene (PX) and trimethylbenzene (TMB) with higher added value were prepared by C6-8~arene methylation, which could elevate the level of methylation field and improve the competitiveness of the industry. This paper focuses on the one-step methylation reaction of benzene with methanol or toluene with methanol to obtain high-purity p-xylene (BM-PX/TM-PX), and the one-step methylation reaction of xylene with methanol to obtain mesitylene (XM-TMB). The methylation reaction mechanism and the preparation strategy of a high-performance catalyst were reviewed. The high selectivity of PX obtained by precisely controlling the pore size and acid site distribution of zeolites was emphasized. Meanwhile, the current research progress of TMB, the kinetic/thermodynamics of the BM-PX/TM-PX, and XM-TMB methylation reaction were described. Based on a literature research and the conclusion of our research group, the mechanism of methylation reaction process was expounded. Finally, the new research direction of catalysts used and reaction process in methylation reaction were prospected in order to guide the rapid development of this field. Methanol ; Toluene ; Benzene ; Methylation ; Chemistry ; Catalysis ; Xylene ; p-Xylene ; Mesitylene ; Computational chemistry ; Organic chemistry ; Chemical engineering Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Gloria A Sulley | M. M. Montemore
Source: Current Opinion in Chemical Engineering (article, 2022-04-29)
Field: Materials Science — Materials Chemistry

Recent progress towards a universal machine learning model for reaction energetics in heterogeneous catalysis Generality ; Biochemical engineering ; Computer science ; Field (mathematics) ; Range (aeronautics) ; Nanotechnology ; Engineering ; Materials science ; Mathematics ; Aerospace engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Jiaqiang Yang | Zhaojie Wang | Zhang Liu | Qingbo Wang | Yanwei Wen | Aimin Zhang | Rong Chen | Bin Shan
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Rational ensemble design of alloy catalysts for selective ammonia oxidation based on machine learning High-throughput computation and machine learning studies are conducted for the rational design of ensembles of alloy catalysts for selective catalytic oxidation of NH 3 (NH 3 -SCO). Ammonia ; Alloy ; Rational design ; Catalysis ; Computer science ; Chemistry ; Materials science ; Metallurgy ; Nanotechnology ; Organic chemistry Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Machine Learning in Materials Science
Authors: Haoxuan Liu | Haile Yan | Nan Jia | Shuai Tang | Daoyong Cong | Bo Yang | Zongbin Li | Yudong Zhang | Claude Esling | Xiang Zhao | Liang Zuo
Source: Journal of Material Science and Technology (article, 2022-06-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloys Ductility (Earth science) ; Brittleness ; Materials science ; Alloy ; Structural engineering ; Metallurgy ; Engineering Heusler alloys: electronic and magnetic properties ; Intermetallics and Advanced Alloy Properties ; Titanium Alloys Microstructure and Properties
Authors: Xiaohua Tian | Qiu Zhao | Kun Zhang | Hong-Xing Li | Binglun Han | Dingding Shi | Zhou Li-wen | Tianyou Ma | Cheng Wang | Qinlong Wen | Changlong Tan
Source: Journal of Applied Physics (article, 2022-01-04)
Field: Materials Science — Materials Chemistry

Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning NiTi-based shape memory alloys (SMAs) are regarded as one of the most promising materials for engineering applications of elastocaloric refrigeration. A critical mission is to efficiently explore the new NiTi-based SMAs with large adiabatic temperature change (ΔTad). We proposed a new material design method that combines highly correlated microscale physical information (volume change, ΔV) into machine learning to predict ΔTad of NiTi-based alloys. First, we tightly coupled machine learning with first-principles calculations to accelerate receiving lattice parameters before and after the phase transformation and predict the ΔV, which shows excellent performance with the coefficient of determination R2 &amp;gt; 0.99. Then, relevant features, including the ΔV, are considered to predict the ΔTad in NiTi-based SMAs. Moreover, due to the small dataset, the principal component analysis and the independent component analysis are added. We evaluate the performance of three machine learning models [Lasso regression, support vector regression, and decision tree regression (DTR)]. Finally, the DTR model exhibits a high accuracy for predicting ΔTad (R2 &amp;gt; 0.9). Introducing the feature of ΔV into the machine learning process can improve the accuracy and efficiency of model design. Further, this work paves the way to accelerate the discovery of new excellent materials for practical applications of elastocaloric refrigeration. Machine learning ; Artificial intelligence ; Computer science ; Support vector machine ; Refrigeration ; Nickel titanium ; Principal component analysis ; Decision tree ; Shape-memory alloy ; Mechanical engineering ; Engineering Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience ; High Entropy Alloys Studies
Authors: Ibrahim Dubdub
Source: Polymers (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Pyrolysis Study of Mixed Polymers for Non-Isothermal TGA: Artificial Neural Networks Application Pure polymers of polystyrene (PS), low-density polyethylene (LDPE) and polypropylene (PP), are the main representative of plastic wastes. Thermal cracking of mixed polymers, consisting of PS, LDPE, and PP, was implemented by thermal analysis technique “thermogravimetric analyzer (TGA)” with heating rate range (5−40 K/min), with two groups of sets: (ratio 1:1) mixture of PS and PP, and (ratio 1:1:1) mixture of PS, LDPE, and PP. TGA data were utilized to implement one of the machine learning methods, “artificial neural network (ANN)”. A feed-forward ANN with Levenberg-Marquardt (LM) as learning algorithm in the backpropagation model was performed in both sets in order to predict the weight fraction of the mixed polymers. Temperature and the heating rate are the two input variables applied in the current ANN model. For both sets, 10-10 neurons in logsig-tansig transfer functions two hidden layers was concluded as the best architecture, with almost (R > 0.99999). Results approved a good coincidence between the actual with the predicted values. The model foresees very efficiently when it is simulated with new data. Low-density polyethylene ; Thermogravimetric analysis ; Materials science ; Polypropylene ; Polymer ; Isothermal process ; Artificial neural network ; Polyethylene ; Backpropagation ; Chemical engineering ; Composite material ; Computer science ; Thermodynamics ; Machine learning ; Engineering Thermal and Kinetic Analysis ; Polymer crystallization and properties ; Thermochemical Biomass Conversion Processes
Authors: Maciej P. Polak | Ryan Jacobs | Arun Mannodi‐Kanakkithodi | Maria K. Y. Chan | Dane Morgan
Source: The Journal of Chemical Physics (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II-VI and III-V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III-V and II-VI systems. Density functional theory ; Semiconductor ; Hybrid functional ; Computer science ; Fidelity ; Mean squared error ; Photovoltaics ; Materials science ; Computational physics ; Band gap ; Impurity ; Artificial intelligence ; Algorithm ; Machine learning ; Statistical physics ; Physics ; Optoelectronics ; Mathematics ; Quantum mechanics ; Engineering ; Statistics ; Photovoltaic system ; Electrical engineering Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Chalcogenide Semiconductor Thin Films
Authors: Co D. Quach | Justin Gilmer | Daniel Pert | Akanke Mason-Hogans | Christopher R. Iacovella | Peter T. Cummings | Clare McCabe
Source: The Journal of Chemical Physics (article, 2022-02-21)
Field: Materials Science — Materials Chemistry

High-throughput screening of tribological properties of monolayer films using molecular dynamics and machine learning Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion. Tribology ; Monolayer ; Chemical space ; Materials science ; chEMBL ; Molecular dynamics ; In silico ; High-throughput screening ; Workflow ; Computer science ; Nanotechnology ; Throughput ; Chemistry ; Drug discovery ; Composite material ; Computational chemistry Machine Learning in Materials Science ; Lubricants and Their Additives ; Molecular Junctions and Nanostructures