jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Heekyu Kim | Jaejung Park | Minseon Kim | Jaejun Lee | Inhyo Lee | Kyoungmin Min | Seung‐Chul Lee
Source: The Journal of Physical Chemistry C (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Active Learning Platform for Accelerating the Search for High-Voltage Cathode Materials in an Extensive Chemical Space Reliable databases for cathode materials are scarce and have limited scalability; this has hindered the development of a systematic methodology for exploring high-performance materials within the vast chemical space of cathode materials. Using 153,424 data points related to cathode materials, this study implemented an active learning platform that expedites the search for high-voltage materials. By exploring up to 20% of the total data, the proposed platform could discover 79.19% of samples with an average voltage within the top 1%, whereas when searching up to 50% of the total data, it could identify 99.35% of such samples. The proposed platform could effectively search for materials with high average voltages even when the available training data contained materials with a low average voltage. The predictive performance of the machine learning model trained through the active learning process exhibited a mean absolute error of 0.263 V and an R-squared value of 0.921 for the dataset not sampled during the process, demonstrating its capability for the rapid screening of high-voltage materials. Our platform can also handle extrapolation tasks, demonstrating its potential as a tool for discovering new materials outside of the training data. The proposed platform can be used for swiftly and efficiently finding materials with desirable properties, even in the presence of diverse and expansive chemical spaces for materials that are outside the available training data. Scalability ; Extrapolation ; Cathode ; Process (computing) ; Computer science ; Voltage ; Chemical space ; Expansive ; Active learning (machine learning) ; High voltage ; Machine learning ; Materials science ; Artificial intelligence ; Engineering ; Electrical engineering ; Database ; Chemistry ; Statistics ; Mathematics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Machine Learning and Algorithms
Authors: Yogesh Khatri | Rajesh Sharma | Ashutosh Shah | Arti Kashyap
Source: AIP Advances (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Magnetization in iron based compounds: A machine learning model analysis In material science domain, the data availability has made it possible to design and test machine learning models not only to strengthen our understanding of various properties of materials but also to give predictive capabilities through finding trends and patterns. Here, we report the insight into magnetization of Iron based compounds using the machine learning model and by doing the model interpretability analysis using SHapley Additive exPlanations. Most of the Iron based compounds are magnetic in nature and are well studied with abundant data available in different repositories. We have used data from Materials Project. Interpretability ; Magnetization ; Domain (mathematical analysis) ; Computer science ; Experimental data ; Machine learning ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Changsheng Li | Kaihang Mao | Shiye Wang | Ye Yuan | Guoren Wang
Source: IEEE Transactions on Artificial Intelligence (article, 2023-07-21)
Field: Materials Science — Materials Chemistry

Self-Supervised Graph Information Bottleneck for Multiview Molecular Embedding Learning In the field of computer-aided drug discovery, identifying promising drug candidates from small molecule libraries requires meaningful molecular embeddings for downstream tasks, such as property prediction. However, obtaining experimentally determined molecular property measurements is often expensive and time-consuming, making it challenging to train molecular encoders with limited supervision. In addition, molecules can be represented in two ways: as 2-D chemical-bond structures and 3-D geometry structures. Molecular embedding learning using only one of these representations can result in information loss, and effective fusion of the two views has not been fully explored. To address these challenges, we propose a new approach called the self-supervised multiview graph neural network (SMV-GNN) for molecular embedding learning. Our approach involves a self-supervised task that promotes the representation ability of the molecular encoder without requiring extra human-annotation data. Specifically, we use chemical-bond-based graph structures as inputs to predict interatom distances from the 2-D view and randomly shuffle a ratio of atoms in the 3-D coordinate-based graphs to predict atom rationality from the 3-D view. We further improve the representation ability of the molecular embedding by using information bottleneck to learn essential shared feature representations by discarding superfluous information from the 2-D/3-D views for downstream tasks. We evaluate our proposed SMV-GNN approach on seven benchmark datasets for molecule property-prediction tasks, and demonstrate that it outperforms the current state-of-the-art methods. Computer science ; Embedding ; Bottleneck ; Theoretical computer science ; Molecular graph ; Feature learning ; Encoder ; Artificial intelligence ; Graph ; Machine learning ; Representation (politics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pratik Dhakal | Wyatt Gassaway | Jindal K. Shah
Source: The Journal of Chemical Physics (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Mapping the frontier orbital energies of imidazolium-based cations using machine learning The knowledge of the frontier orbital, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), energies is vital for studying chemical and electrochemical stability of compounds, their corrosion inhibition potential, reactivity, etc. Density functional theory (DFT) calculations provide a direct route to estimate these energies either in the gas-phase or condensed phase. However, the application of DFT methods becomes computationally intensive when hundreds of thousands of compounds are to be screened. Such is the case when all the isomers for the 1-alkyl-3-alkylimidazolium cation [CnCmim]+ (n = 1-10, m = 1-10) are considered. Enumerating the isomer space of [CnCmim]+ yields close to 386 000 cation structures. Calculating frontier orbital energies for each would be computationally very expensive and time-consuming using DFT. In this article, we develop a machine learning model based on the extreme gradient boosting method using a small subset of the isomer space and predict the HOMO and LUMO energies. Using the model, the HOMO energies are predicted with a mean absolute error (MAE) of 0.4 eV and the LUMO energies are predicted with a MAE of 0.2 eV. Inferences are also drawn on the type of the descriptors deemed important for the HOMO and LUMO energy estimates. Application of the machine learning model results in a drastic reduction in computational time required for such calculations. HOMO/LUMO ; Density functional theory ; Molecular orbital ; Chemistry ; Computational chemistry ; Phase space ; Chemical space ; Physics ; Thermodynamics ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Miguel A. García‐Castro | Fausto Díaz-Sánchez | José Alberto Galicia-Aguilar | Esmeralda Vidal-Robles
Source: Revista Mexicana de Física (article, 2023-09-01)
Field: Chemistry — Organic Chemistry

Standard molar enthalpies of formation of 3-methylglutaric and 3,3-dimethylglutaric anhydrides In this research, both the standard molar enthalpy of formation in the crystalline phase and in the gas phase of 3-methylglutaric anhydride was calculated from experimental data. The temperature and enthalpy of fusion, as well as the molar heat capacity in solid phase was calculated by differential scanning calorimetry; the molar enthalpy of sublimation at 298.15 K by the Knudsen effusion method, the molar enthalpy of vaporization at 298.15 K by thermogravimetric analysis, and the standard massic combustion energy by combustion adiabatic calorimetry. Since 3,3-dimethylglutaric anhydride presented crystal transitions (with endothermic points at 352.76 K, 356.98 K and 397.15 K), some of its thermochemical properties were estimated from the functional group-contribution methods proposed by Benson, Gani and Naef and from application of Machine Learning based models. Enthalpy ; Standard enthalpy of formation ; Sublimation (psychology) ; Enthalpy of fusion ; Differential scanning calorimetry ; Thermodynamics ; Thermogravimetric analysis ; Chemistry ; Calorimetry ; Vaporization ; Endothermic process ; Standard molar entropy ; Enthalpy of vaporization ; Molar ; Enthalpy of sublimation ; Enthalpy of atomization ; Physical chemistry ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Crystallization and Solubility Studies
Authors: Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Jianjun Hu
Source: CrystEngComm (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Data-driven deep generative design of stable spintronic materials A computational framework that integrates generative adversarial networks and machine learning classifiers to enable the discovery of novel magnetic materials. Generative grammar ; Adversarial system ; Computer science ; Spintronics ; Generative adversarial network ; Artificial intelligence ; Deep learning ; Machine learning ; Ferromagnetism Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Block Copolymer Self-Assembly
Authors: Hayate Yamano | Alexander Kovacs | Johann Fischbacher | Katsunori Danno | Yusuke Umetani | Tetsuya Shoji | T. Schrefl
Source: Japanese Journal of Applied Physics (article, 2023-01-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient optimization approach for designing power device structure using machine learning Abstract Low power-loss semiconductor devices are necessary to achieve a carbon-neutral society. The optimization of device structures is known as a time-consuming process. In this work, we investigated an optimization approach with the help of machine learning. We applied an active learning scheme to optimize a gallium oxide Schottky barrier diode structure and demonstrated how this approach helps to reduce the number of time-consuming simulations for the optimization process. For the investigated work, the active learning strategy almost reduced the number of simulations by a factor of 2 in contrast to the conventional genetic optimization. In addition, we also demonstrated that machine learning models can be used to estimate the performance variations caused by process variations. This approach can also contribute to reducing the number of simulations and speeding up the structure design process. Computer science ; Process (computing) ; Bayesian optimization ; Schottky diode ; Active learning (machine learning) ; Genetic algorithm ; Power (physics) ; Process optimization ; Work (physics) ; Diode ; Artificial intelligence ; Machine learning ; Materials science ; Engineering ; Optoelectronics ; Physics ; Mechanical engineering Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; ZnO doping and properties
Authors: Jianqi Xi | Yeqi Shi | Vitaly Pronskikh | Frédérique Pellemoine | Dane Morgan | Izabela Szlufarska
Source: Journal of Nuclear Materials (article, 2023-01-07)
Field: Materials Science — Materials Chemistry

Atomistic simulations of He bubbles in Beryllium Bubble ; Beryllium ; Thermal diffusivity ; Diffusion ; Materials science ; Mechanics ; Thermodynamics ; Chemical physics ; Crystallography ; Chemistry ; Physics ; Nuclear physics Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Zeshi Wang | Wenhui Zhong | Jun Jiang | Song Wang
Source: The Journal of Physical Chemistry Letters (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

Decoupling Analysis of O<sub>2</sub> Adsorption on Metal–N–C Single-Atom Catalysts via Data-Driven Descriptors The adsorption energy of adsorbed molecules on single-atom catalysts is a key indicator of the catalytic activity of the catalysts. Developing a generic and interpretable structure-property prediction model from numerous influencing factors is a challenging task. In this work, we constructed a machine learning (ML) model from first-principles calculations of the adsorption energy data of O<sub>2</sub> on Ni(II), Co(II), Cu(II), Fe(II), Fe(III), and Mn(II) single-atom catalysts supported on 15 different N-C substrates under various spin states. A mathematic formula is proposed to predict the adsorption energy by a novel data-driven descriptor derived from physically meaningful factors such as geometric distances and atomic charges. This data-driven descriptor is relevant to only the geometrical configuration of the adsorbate, while the parameters in the linear formulas contain only substrate-specific information. This ML model with the ability to decouple variables will greatly advance the understanding of metal-N-C single-atom catalysts and help in the design of new substrates to modulate catalytic activity. Decoupling (probability) ; Catalysis ; Adsorption ; Atom (system on chip) ; Metal ; Materials science ; Chemistry ; Computer science ; Physical chemistry ; Engineering ; Metallurgy ; Parallel computing ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Noah T. Renella | Subodh Bhandari | Amar Raheja
Source:  (article, 2023-06-13)
Field: Chemistry — Analytical Chemistry

Machine learning models for detecting and isolating weeds from strawberry plants using UAVs This paper presents the development and validation of machine learning models for locating strawberry plants and weeds as well as determining the health of strawberry plants. TensorFlow Lite Model Maker was used for object detection and model training using a custom dataset of marked images. The data used in the dataset are images collected from an unmanned aerial vehicle (UAV) and are annotated using LabelImg, a popular tool for annotating bounding boxes over images. The locations of the weeds and strawberry plants were found in both latitude/longitude coordinates as well as Degrees, Meters, Seconds (DMS) format by using the ground sample distance formula (GSD). The greenness indices were found by using OpenCV image alignment on the multispectral sensors to calculate the corresponding greenness index. The developed machine learning models can well predict plant health, detect weeds, and determine their locations. The overall goal of the project is to use UAV-based remote sensing and machine learning techniques for precision farming that aims to optimize the use of water and chemicals using site-specific and optimal applications water and chemicals. Computer science ; Artificial intelligence ; Multispectral image ; Geographic coordinate system ; Machine learning ; Bounding overwatch ; Remote sensing ; Computer vision ; Cartography ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Ole‐Christian Galbo Engstrøm | Erik Schou Dreier | Birthe Møller Jespersen | Kim Steenstrup Pedersen
Source:  (article, 2023-10-02)
Field: Chemistry — Analytical Chemistry

Improving Deep Learning on Hyperspectral Images of Grain by Incorporating Domain Knowledge from Chemometrics We demonstrate how to design and apply domain-specific modifications to convolutional neural networks (CNNs) to improve model performance on hyperspectral images of grain kernels. We use hyperspectral images of grain kernels captured in the near-infrared wavelength range of 900 to 1700 nm as a case for supporting our argumentation. This part of the electromagnetic spectrum contains convoluted signals with chemical and physical information relevant to grain quality. For standard chemometric models, domain knowledge is used to select from a plethora of combinations of preprocessing techniques helpful in extracting relevant chemical and physical features for a given task. By incorporating domain-specific design modifications in the preexisting architectures of ResNet-18 and a simple CNN, we show that model performance can be increased significantly and that applying domain knowledge to CNNs is much more important than complexity is to their performance. Hyperspectral imaging ; Computer science ; Convolutional neural network ; Artificial intelligence ; Preprocessor ; Pattern recognition (psychology) ; Domain knowledge ; Chemometrics ; Domain (mathematical analysis) ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: M.J. Jiménez–Come | Francisco Javier González Gallero | Pascual Álvarez Gómez | Jesús Daniel Mena Baladés
Source: Metals (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

Corrosion Behaviour Modelling Using Artificial Neural Networks: A Case Study in Biogas Environment The main objective established in this work was to develop a model based on artificial neural networks (ANNs) to predict the corrosion status of stainless steel involved in biogas production, analyzing the influence of the material composition and the breakdown potential value. To achieve this objective, an ANN model capable of predicting the corrosion status of the material without the need to perform microscopic analysis on the material surface was proposed. The applicability of the corrosion models was verified via the experimental data considering different factors such as stainless steel composition, biogas environments simulated by artificial solution, temperature, surface finish, and the breakdown potential of the passive layer of stainless steel obtained from electrochemical tests. The optimal prediction performance shown by the model in terms of specificity and sensitivity values were 0.969 and 0.971, respectively, obtaining an accuracy of 0.966. Furthermore, analyzing the influence of the breakdown potential on corrosion modelling, an alternative model was presented capable of predicting the corrosion status automatically, without the need to resort to electrochemical tests for new conditions. The results demonstrated the utility of this technique to be considered in design and maintenance planning tasks for stainless steel structures subjected to localized corrosion in biogas production. Corrosion ; Artificial neural network ; Biogas ; Materials science ; Work (physics) ; Process engineering ; Metallurgy ; Biological system ; Environmental science ; Engineering ; Computer science ; Mechanical engineering ; Machine learning ; Waste management Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Machining and Optimization Techniques
Authors: Heather A. Murdoch | Daniel Field | Benjamin A. Szajewski | Levi D. McClenny | A. de la Garza | B.C. Rinderspacher | Mulugeta Haile | Krista R. Limmer
Source: Integrating materials and manufacturing innovation (article, 2023-10-26)
Field: Materials Science — Metals and Alloys

Tempered Hardness Optimization of Martensitic Alloy Steels Tempering ; Martensite ; Materials science ; Alloy ; Toughness ; Metallurgy ; Kriging ; Microstructure ; Computer science ; Machine learning Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Kensei Terashima | Pedro Baptista de Castro | Akiko T. Saito | Takafumi D. Yamamoto | Ryo Matsumoto | Hiroyuki Takeya | Yoshihiko Takano
Source: Science and Technology of Advanced Materials Methods (article, 2023-06-09)
Field: Materials Science — Materials Chemistry

Experimental exploration of ErB <sub>2</sub> and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design Stimulated by a recent report of a giant magnetocaloric effect in HoB2 found via machine-learning predictions, we have explored the magnetocaloric properties of a related compound ErB2 that has remained the last ferromagnetic material among the rare-earth diboride (REB2) family with unreported magnetic entropy change |ΔSM|. The evaluated |ΔSM| at field change of 5 T in ErB2 turned out to be as high as 26.1 J kg−1 K−1 around the ferromagnetic transition (TC) of 14 K. In this series, HoB2 is found to be the material with the largest |ΔSM| as the model predicted, while the predicted values showed a deviation with a systematic error compared to the experimental values. Through a coalition analysis using SHAP, we explore how this rare-earth dependence and the deviation in the prediction are deduced in the model. We further discuss how SHAP analysis can be useful in clarifying favorable combinations of constituent atoms through the machine-learned model with compositional descriptors. This analysis helps us to perform materials design with aid of machine learning of materials data. Magnetic refrigeration ; Ferromagnetism ; Rare earth ; Condensed matter physics ; Materials science ; Artificial intelligence ; Entropy (arrow of time) ; Machine learning ; Thermodynamics ; Magnetic field ; Computer science ; Physics ; Metallurgy ; Magnetization ; Quantum mechanics Machine Learning in Materials Science ; Rare-earth and actinide compounds ; Magnetic and transport properties of perovskites and related materials
Authors: Ruijin Qu | Yawei Lv | Zhihong Lu
Source: Journal of Electronic Materials (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

A Deep Neural Network Potential to Study the Thermal Conductivity of MnBi2Te4 and Bi2Te3/MnBi2Te4 Superlattice Thermal conductivity ; Phonon ; Condensed matter physics ; Superlattice ; Thermoelectric materials ; Thermoelectric effect ; Brillouin zone ; Materials science ; Solid-state physics ; Physics ; Thermodynamics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Dimitar Georgiev | Simon Vilms Pedersen | Ruoxiao Xie | A. Fernandez-Galiana | Molly M. Stevens | Mauricio Barahona
Source: ChemRxiv (preprint, 2023-07-05)
Field: Chemistry — Analytical Chemistry

RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python. Python (programming language) ; Workflow ; Raman spectroscopy ; Open source ; Computer science ; Data science ; Software ; Modular design ; Exploratory analysis ; Data analysis ; Software engineering ; World Wide Web ; Data mining ; Programming language ; Database ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies