jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaolin Liu | Yue Yan | Shihao Xu
Source:  (article, 2023-08-11)
Field: Chemistry — Analytical Chemistry

Multi-Scale Convolutional Neural Networks for the Quantitative Analysis of Multi-Component Gases In the realm of quantitative analysis of multi-component pollutant gases, the ability to accurately model complex non-linear relationships and construct effective predictive models is crucial. This study employed Convolutional Neural Networks (CNNs) in conjunction with Fourier Transform Infrared Spectroscopy (FTIR) to analyze these pollutant gases quantitatively. The efficacy of this approach was subsequently affirmed through a series of simulation experiments. Data for the study was sourced from the spectroscopic database of the US Environmental Protection Agency (EPA). Seven common pollutant gases, marked by similar molecular structures, volatility at room temperature, and varying specific gravities, were selected for the experiment. A deep learning-based continuous wavelength interval wavelength selection method was utilized to determine the optimal wave number range within the spectral wave number range of 4400 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> to 400 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . Single-scale CNN models, incorporating convolution kernel scales of 5, 15, and 25, were established. The Mean Square Errors (MSE) for the test sets were recorded at 0.485, 0.288, and 0.227 respectively. These findings verified the effective role of CNNs in multi-component pollutant gas quantitative analysis. In addition, under the condition of a constant sample data set, a multi-scale CNN model featuring convolution kernel scales of 5, 15, and 25 was designed. The resulting MSE for the test set was 0.038. This experiment demonstrated that, in comparison to its single-scale counterparts, a multi-scale CNN model offers superior capacity to learn spectral data features, higher prediction accuracy, and overall model stability. Convolutional neural network ; Kernel (algebra) ; Computer science ; Artificial intelligence ; Test set ; Convolution (computer science) ; Pattern recognition (psychology) ; Artificial neural network ; Mathematics ; Biological system ; Algorithm ; Discrete mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Panagiotis Krokidas | M.E. Kainourgiakis | Theodore Steriotis | George Giannakopoulos
Source: ChemRxiv (preprint, 2023-04-11)
Field: Chemistry — Inorganic Chemistry

Inverse Design of ZIFs through Artificial Intelligence Methods Artificial Intelligence (AI) benefits research on membrane separations by facilitating fast and accurate performance predictions of a given material. However, the potential of AI to work backwards, towards predicting/designing a finetuned material for a given separation, remains untapped. Recent works report the inverse design of functionalized materials, such as metal-organic frameworks (MOFs), but they are limited to targeted sorption properties, while diffusivity, D, which is the driving force in membrane-based separations, is omitted. Herein, we report a tool combining a biologically inspired evolutionary algorithm with machine learning to design fine-tuned Zeolitic-Imidazolate Frameworks (ZIFs), a sub-family of MOFs, for desired sets of diffusivities (Di, Di/Dj) values of any given mixture of species i and j. We moreover display the efficacy of our tool, by designing ZIFs that meet industrial performance criteria of permeability and selectivity, for CO2/CH4, O2/N2 and C3H6/C3H8 mixtures. We validate the designed ZIFs through appropriate simulations, confirming the suitability of the AI-suggested ZIF designs. Zeolitic imidazolate framework ; Computer science ; Inverse ; Artificial intelligence ; Membrane ; Metal-organic framework ; Chemistry ; Mathematics ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Carbon dioxide utilization in catalysis
Authors: Libing Qian | Mingyang Wu | Haodong Yang | Jefferson Zhe Liu | Yonghong Hu | Zhe Chen | Chunqing He | Zhiyuan Chen | Xiuqin Tang
Source: Journal of Materials Science Materials in Electronics (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Effect of the electron irradiation on the ion permeation through the GO membrane Irradiation ; Fourier transform infrared spectroscopy ; Permeation ; Raman spectroscopy ; Materials science ; X-ray photoelectron spectroscopy ; Membrane ; Scanning electron microscope ; Analytical Chemistry (journal) ; Graphene ; Ion ; Electron beam processing ; Spectroscopy ; Oxide ; Dielectric spectroscopy ; Electrochemistry ; Chemistry ; Chemical engineering ; Nanotechnology ; Optics ; Composite material ; Chromatography ; Physical chemistry ; Organic chemistry Graphene research and applications ; Advancements in Battery Materials ; Membrane Separation Technologies
Authors: Bianca Baldassarri | Jiangang He | Abhijith Gopakumar | Sean D. Griesemer | Adolfo J.A. Salgado-Casanova | Tzu-chen Liu | Steven B. Torrisi | Chris Wolverton
Source: arXiv (Cornell University) (preprint, 2023-09-03)
Field: Materials Science — Materials Chemistry

A dataset of DFT-computed oxygen vacancy formation energies of metal oxides The oxygen vacancy formation energy ($ΔE_{vf}$) governs defect dynamics and is a useful metric to perform materials selection for a variety of applications. However, density functional theory (DFT) calculations of $ΔE_{vf}$ come at a greater computational cost than the typical bulk calculations available in materials databases due to the involvement of multiple vacancy-containing supercells. As a result, available repositories of direct calculations of $ΔE_{vf}$ remain relatively scarce, and the development of machine learning models capable of delivering accurate predictions is of interest. In the present, work we address both such points. We first report the results of new high-throughput DFT calculations of oxygen vacancy formation energies of the different unique oxygen sites in over 1000 different oxide materials, which together form the largest dataset of directly computed oxygen vacancy formation energies to date, to our knowledge. We then utilize the resulting dataset of $\sim$2500 $ΔE_{vf}$ values to train random forest models with different sets of features, examining both novel features introduced in this work and ones previously employed in the literature. We demonstrate the benefits of including features that contain information specific to the vacancy site and account for both cation identity and oxidation state, and achieve a mean absolute error upon prediction of $\sim$0.3 eV/O, which is comparable to the accuracy observed upon comparison of DFT computations of oxygen vacancy formation energy and experimental results. Finally, we demonstrate the predictive power of the developed models in the search for new compounds for solar-thermochemical water-splitting applications, finding over 250 new AA$^{\prime}$BB$^{\prime}$O$_6$ double perovskite candidates. Throughput ; Oxygen ; Materials science ; Metal ; Computer science ; Nuclear engineering ; Metallurgy ; Chemistry ; Engineering ; Operating system Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Trushal Sardhara | Alexander Shkurmanov | Roland C. Aydin | Christian J. Cyron | Martin Ritter
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Towards an Accurate 3D Reconstruction of Nano-porous Structures using FIB Tomography and Monte Carlo Simulations with Machine Learning Journal Article Towards an Accurate 3D Reconstruction of Nano-porous Structures using FIB Tomography and Monte Carlo Simulations with Machine Learning Get access Trushal Sardhara, Trushal Sardhara Institute for Continuum and Material Mechanics, Hamburg University of Technology, Germany Search for other works by this author on: Oxford Academic Google Scholar Alexander Shkurmanov, Alexander Shkurmanov Electron Microscopy Unit, Hamburg University of Technology, Germany Search for other works by this author on: Oxford Academic Google Scholar Roland Aydin, Roland Aydin Helmholtz-Zentrum Hereon, Institute of Material Systems Modeling, Geesthacht, Germany Search for other works by this author on: Oxford Academic Google Scholar Christian J Cyron, Christian J Cyron Institute for Continuum and Material Mechanics, Hamburg University of Technology, GermanyHelmholtz-Zentrum Hereon, Institute of Material Systems Modeling, Geesthacht, Germany Search for other works by this author on: Oxford Academic Google Scholar Martin Ritter Martin Ritter Electron Microscopy Unit, Hamburg University of Technology, Germany Corresponding author: ritter@tuhh.de Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 545–546, https://doi.org/10.1093/micmic/ozad067.257 Published: 22 July 2023 Monte Carlo method ; Porosity ; Materials science ; Tomography ; Nano- ; Computer science ; Optics ; Physics ; Composite material ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: H. F. Feng | Bo Liu | Zhi‐Xin Guo | Guo, Zhi-Xin
Source: arXiv (Cornell University) (preprint, 2023-12-28)
Field: Materials Science — Materials Chemistry

Unusually Strong Four-Phonon Scattering Effects on Low-Temperature Thermal Conductivity in Two-Dimensional Materials First principles-based predictions of lattice thermal conductivity (TC) from perturbation theory have achieved significant success. Usually, it only included three-phonon (3ph) scattering processes, only recently four-phonon (4ph) scattering processes were found to have a comparable impact as 3ph scattering at medium and high temperatures in various materials. While the influence of 4ph scattering on TC at low temperatures was generally believed to be insignificant. By combining the first-principles calculations, machine learning techniques, and Boltzmann transport equation (BTE), we find that there are unusually strong 4ph processes even in the low-frequency range of two-dimensional (2D) materials such as h-XN (X = B, Al, Ga), which have a remarkable influence on the low-temperature TC. Such strong 4ph processes originated from the out-of-plane acoustic (ZA) phonon mode of 2D materials. Furthermore, we find that the intensity of 4ph scattering and thus TC can be effectively manipulated by changing the dispersion of ZA phonon mode, which can be easily achieved through strain engineering. The present study provides new insights into low-temperature phonon transport and its manipulation in 2D materials. Phonon ; Scattering ; Materials science ; Thermal conductivity ; Condensed matter physics ; Phonon scattering ; Boltzmann equation ; Physics ; Thermodynamics ; Optics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity
Authors: Dmitry G. Kvashnin | Viktor S. Baidyshev
Source: FlatChem (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Study of formation and properties of F4TCNQ/MoS2 heterostructures with point defects driven by DFT and neural network potential Heterojunction ; Materials science ; Monolayer ; Molecule ; Vacancy defect ; Halogen ; Chemical physics ; Nanotechnology ; Optoelectronics ; Alkyl ; Chemistry ; Crystallography ; Organic chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Shengrui Li | Xueting Han | Jing Bai
Source: arXiv (Cornell University) (preprint, 2023-04-19)
Field: Materials Science — Materials Chemistry

AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNs Fine-tuning pre-trained models has recently yielded remarkable performance gains in graph neural networks (GNNs). In addition to pre-training techniques, inspired by the latest work in the natural language fields, more recent work has shifted towards applying effective fine-tuning approaches, such as parameter-efficient fine-tuning (PEFT). However, given the substantial differences between GNNs and transformer-based models, applying such approaches directly to GNNs proved to be less effective. In this paper, we present a comprehensive comparison of PEFT techniques for GNNs and propose a novel PEFT method specifically designed for GNNs, called AdapterGNN. AdapterGNN preserves the knowledge of the large pre-trained model and leverages highly expressive adapters for GNNs, which can adapt to downstream tasks effectively with only a few parameters, while also improving the model's generalization ability. Extensive experiments show that AdapterGNN achieves higher performance than other PEFT methods and is the only one consistently surpassing full fine-tuning (outperforming it by 1.6% and 5.7% in the chemistry and biology domains respectively, with only 5% and 4% of its parameters tuned) with lower generalization gaps. Moreover, we empirically show that a larger GNN model can have a worse generalization ability, which differs from the trend observed in large transformer-based models. Building upon this, we provide a theoretical justification for PEFT can improve generalization of GNNs by applying generalization bounds. Our code is available at https://github.com/Lucius-lsr/AdapterGNN. Generalization ; Computer science ; Transformer ; Machine learning ; Artificial intelligence ; Fine-tuning ; Artificial neural network ; Source code ; Mathematics ; Voltage Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Memory and Neural Computing
Authors: Soma Datta | Jyoti Choudhury
Source: IET conference proceedings. (article, 2023-07-26)
Field: Chemistry — Analytical Chemistry

Exploring machine learning techniques on Yeast dataset classification Generating information from different datasets using machine learning technologies is one of the crucial and pivotal jobs in the area of data mining. In the recent years, application of these data mining is seen in healthcare, academics and different organizations. A massive application is identified in the area of healthcare. An inseparable part of healthcare organizations is pharmacological industry-i.e. industry related to producing, discovering drugs, their composition and their reaction to human body. Yeast is considered one of the major components in producing drugs for human, for its similarity in cell structure with human cell. In this proposed work, a thorough study of application of machine learning algorithms along with different validation and ensemble techniques have been explored on Yeast dataset. The models are evaluated with mean accuracy. Several other performance measures f-score, Recall, RMSE etc. also has been considered to decide the best model for the said dataset. Results show that ensemble method bagged decision tree works best with a mean accuracy of 61%.The performance of Bagged extra tree is also best with respect to other performance metrics like f1-score, precision etc. Computer science ; Machine learning ; Decision tree ; Artificial intelligence ; Health care ; Precision and recall ; Similarity (geometry) ; Recall ; Data mining ; F1 score ; Tree (set theory) ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Markus Scheidgen | Sebastian Brückner | Sándor Brockhauser | Luca M. Ghiringhelli | Felix Dietrich | Ahmed E. Mansour | J.A. Marquez | M. Albrecht | Heiko B. Weber | Silvana Botti | Martin Aeschlimann | Claudia Draxl
Source: Proceedings of the Conference on Research Data Infrastructure (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

FAIR Research Data With NOMAD Scientific research is becoming increasingly data centric, which requires more effort to manage, share, and publish data.NOMAD is a web-based platform that provides research data management (RDM) for materials-science data. In addition to core RDM functions like uploading and sharing files, NOMAD automatically extracts structured data from supported file formats, normalizes, and converts data from these formats. NOMAD provides an extendable framework for managing not just files, but structured machine-actionable harmonized and inter-operable data. This is the basis for a faceted search with domain-specific filters, a comprehensive API, structured data entry via customizable ELNs, integrated data-analysis and machine-learning tools. NOMAD is run as a free public service and can additionally be operated by research institutes. Connecting NOMAD installations through the public services will allow a federated data infrastructure to share data between research institutes and further harmonize RDM within a large research domain such as materials science. RDM ; Upload ; Computer science ; Public domain ; Data sharing ; Data management ; Data as a service ; World Wide Web ; Data management plan ; Domain (mathematical analysis) ; Research data ; Publication ; Database ; Service (business) ; Data curation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Yifan Sun | Masaya Kumagai | Mingzhou Jin | Eimei Sato | Masayo Aoki | Yuji Ohishi | Ken Kurosaki
Source: Journal of Nuclear Science and Technology (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

A multiclass classification model for predicting the thermal conductivity of uranium compounds ABSTRACTAdvanced nuclear fuels are designed to offer improved performance and accident tolerance, with an emphasis on achieving higher thermal conductivity. While promising fuel candidates like uranium nitrides, carbides, and silicides have been widely studied, the majority of uranium compounds remain unexplored. To search for potential candidates among these unexplored uranium compounds, we incorporated machine learning to accelerate the material discovery process. In this study, we trained a multiclass classification model to predict a compound's thermal conductivity based on 133 input features derived from element properties and temperature. The initial training data consist of over 160,000 processed thermal conductivity records from the Starrydata2 database, but a skewed data class distribution led the trained model to underestimate compound's thermal conductivity. Consequently, we addressed the issue of class imbalance by applying Synthetic Minority Oversampling TEchnique and Random UnderSampling, improving the recall for materials with thermal conductivity higher than 15 W/mK from 0.64 to 0.71. Finally, our best model is used to identify 119 potential advanced fuel candidates with high thermal conductivity among 774 stable uranium compounds. Our results underscore the potential of machine learning in the field of nuclear science, accelerating the discovery of advanced nuclear materials.KEYWORDS: Advanced nuclear fuelsmachine learningthermal conductivity Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available at https://github.com/AzarashiYifan/classification-uranium-thermal-conductivity.Additional informationFundingThis work was supported by MEXT Innovative Nuclear Research and Development Program Grant Number JPMXD0220354330 and JPMXD0222682541. Thermal conductivity ; Nuclear fuel ; Uranium ; Undersampling ; Artificial intelligence ; Oversampling ; Nuclear data ; Computer science ; Materials science ; Multiclass classification ; Machine learning ; Support vector machine ; Nuclear engineering ; Physics ; Nuclear physics ; Engineering ; Metallurgy ; Composite material ; Neutron Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Sugata Goswami | Silvan Käser | Raymond J. Bemish | Markus Meuwly
Source: Artificial Intelligence Chemistry (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Effects of aleatoric and epistemic errors in reference data on the learnability and quality of NN-based potential energy surfaces The effect of noise in the input data for learning potential energy surfaces (PESs) based on neural networks for chemical applications is assessed. Noise in energies and forces can result from aleatoric and epistemic errors in the quantum chemical reference calculations. Statistical (aleatoric) noise arises for example due to the need to set convergence thresholds in the self consistent field (SCF) iterations whereas systematic (epistemic) noise is due to, inter alia, particular choices of basis sets in the calculations. The two molecules considered here as proxies are H2CO and HONO which are examples for single- and multi-reference problems, respectively, for geometries around the minimum energy structure. For H2CO it is found that adding noise to energies and forces with magnitudes representative of single-point calculations does not deteriorate the quality of the final PESs whereas increasing the noise level commensurate with electronic structure calculations for more complicated, e.g. metal-containing, systems is expected to have a more notable effect. On the other hand, for HONO which requires a multi-reference treatment, a clear correlation between model quality and the degree of multi-reference character as measured by the T1 amplitude is found. It is concluded that for chemically “simple” cases the effect of aleatoric and epistemic errors is manageable without evident deterioration of the trained model, but more care needs to be exercised for situations in which multi-reference effects are present. Noise (video) ; Quality (philosophy) ; Computer science ; Statistical physics ; Energy (signal processing) ; Set (abstract data type) ; Algorithm ; Mathematics ; Artificial intelligence ; Physics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mengjiao Pan | Lijun Wang | Xinyi Lu | Jingxian Xu | Yehu Lu | Jiazhen He
Source: Clothing and Textiles Research Journal (article, 2023-06-20)
Field: Materials Science — Polymers and Plastics

Predicting the Thermal Protective Performance of Smart Fabric Systems With a Shape Memory Layer Using Machine Learning Models The utilization of shape memory alloy (SMA) in shape memory fabric (SMF) has revolutionized thermal protective clothing, significantly enhancing its thermal protection. However, the cost- and time-consuming process of SMA shape memory training and performance testing can be optimized for improved efficiency. This study addresses this challenge by developing machine learning models to predict the thermal protection of a smart fabric system (SFS) with a SMF. The training data was sourced from the previous experimental studies, and six features significantly impacting thermal protection were identified. Results demonstrated that gradient boosting regressor (GBR) model exhibited the highest accuracy, with the SMA interval emerging as the most critical feature in determining thermal protection. Moreover, the GBR model predicted that SFS presented the best thermal protection when the dry SMF was woven by SMA of 2 cm interval and aramid 1414 of 20 roots/cm density, located between the moisture barrier and thermal liner vertically. SMA* ; Shape-memory alloy ; Thermal ; Materials science ; Computer science ; Clothing ; Boosting (machine learning) ; Structural engineering ; Composite material ; Artificial intelligence ; Engineering Textile materials and evaluations ; Thermoregulation and physiological responses ; Polymer composites and self-healing