jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrіі Slabkyі | Serhii Kotyk
Source: Journal of Mechanical Engineering and Transport (article, 2024-11-26)
Field: Materials Science — General Materials Science

Innovative technologies in the study of mechanical properties of materials The article provides an overview of modern innovative technologies used to study the mechanical properties of materials using universal breaking machines. The focus is on the integration of machine vision technologies, machine learning algorithms and high-speed stereoscopy, which provide new capabilities for analysing the behaviour of materials during static and dynamic loads. An important aspect of the research is the improvement of traditional test methods through the introduction of innovative components: high-speed cameras that allow recording material deformations with microsecond precision; stereoscopic systems that create three-dimensional models of material behaviour under load; as well as lighting devices that provide uniform illumination of the test object. These technologies are synchronized with powerful computing modules capable of analysing large amounts of data in real time. The authors emphasise that the integration of computer vision and machine learning algorithms can significantly reduce the impact of the human factor on research results. Automated analysis systems are able to accurately detect and classify defects, simulate types of failures and predict the behaviour of materials under various loading conditions. Examples of the use of such systems in the study of complex composites, metal alloys and the latest polymers are also given. Additionally, innovative approaches to the creation of dynamic loads that allow testing for impact strength and endurance are considered. The article shows how automated test process management systems increase research efficiency by providing greater accuracy, repeatability of results, and faster data processing. The practical significance of the described developments lies in their wide application in industries where the study of the mechanical properties of materials is critical, such as aircraft construction, mechanical engineering, energy and construction. A high level of automation and accuracy of tests, provided by the introduction of modern technologies, allows setting new standards for the quality of research and creates prospects for the further development of materials science. Thus, the presented approach to improving universal breaking machines contributes to increasing the accuracy of measurements, expanding the functionality of traditional test systems and creating more efficient methods for studying materials Materials science ; Engineering Material Properties and Applications
Authors: Yoonsu Shim | Jacob Choe | Hyeonmuk Kang | Ji Su Park | Jungjae Park | Junpyo Hur | Han Beom Jeong | Namgyu Noh | Sanghyeon Ji | Jung Hyeon Moon | Junho Lee | EunAe Cho | Seungbum Hong | Jong Min Yuk | Chan‐Woo Lee
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Unraveling Structure–Property Relationship in Lithium-Ion Cathodes Through Transmission Electron Microscopy Via Deep Learning Transmission electron microscopy ; Lithium (medication) ; Property (philosophy) ; Ion ; Cathode ; Materials science ; Electron ; Nanotechnology ; Chemical physics ; Optoelectronics ; Engineering physics ; Chemistry ; Physics ; Psychology ; Nuclear physics ; Quantum mechanics ; Physical chemistry Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Battery Materials
Authors: Rebecca Birolo | Rıza Özçelik | Andrea Aramini | Roberto Gobetto | Michele R. Chierotti | Francesca Grisoni
Source: ChemRxiv (preprint, 2024-05-29)
Field: Materials Science — Materials Chemistry

Leveraging Deep Chemical Language Processing for Co-crystal Prediction Most hits identified in the drug discovery pipelines and even 40% of marketed drugs suffer from suboptimal pharmacokinetic profiles. Co- crystallization, wherein a drug (or drug candidate) and another organic molecule form a multi- component crystal, can optimize physicochemical properties of those molecules without hampering their pharmacological activity. However, finding promising co-crystal pairs is resource-intensive due to the vast search space. Here we propose DeepCrystal, a deep learning model based on chemical language to predict co-crystallization. We rigorously validate DeepCrystal and find that it achieves 78% accuracy on realistic settings and displays superior performance to existing models. Leveraging the chemical language to represent molecules, DeepCrystal can estimate uncertainty in its predictions. We exploit this capability in a challenging prospective study and discover two novel co-crystal of diflunisal, an antiinflammatory drug. This prospective study exemplifies a successful application of deep learning to accelerate the co-crystallization process in the lab, highlighting its potential, in both academic and industrial settings. Computer science ; Natural language processing ; Artificial intelligence ; Crystal (programming language) ; Linguistics ; Programming language ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; History and advancements in chemistry
Authors: Zhenyu Han | Yongli Zhang | Guangfei Zhu | Xuan Liu | Hao Tang | Tailin Han
Source:  (article, 2024-08-15)
Field: Chemistry — Analytical Chemistry

Non-destructive Detection of Moldy Camellia Oleifera Seeds Based on Deep Learning and Hyperspectral Imaging Camellia oleifera, a woody oil plant of high economic value, faces potential mold growth during storage and transportation, which could significantly impact its sales and processing. Therefore, it is crucial to identify and select moldy camellia oleifera seeds. Due to the fact that the mold growth of camellia oleifera seeds typically originates from the inside of the seeds, the mold is not visibly apparent on the surface of the shells in the early stages of mold growth. The existing chemical methods are time-consuming, labor-intensive, and result in sample damage. To address this challenge, this study introduces a non-destructive detection method for assessing the extent of mold growth in camellia oleifera seeds efficiently and effectively. The method is based on deep learning and hyperspectral imaging (HSI) technology. A hyperspectral imaging system was utilized to capture hyperspectral images of camellia oleifera seeds at three degrees of mold. In response to the limitations of traditional machine learning methods in hyperspectral classification, which only focus on learning spectral features and yield unsatisfactory results, this study introduces a deep learning approach based on three-dimensional convolution. This algorithm is capable of extracting features from both spatial and spectral information. The classification performance of the proposed model is compared with machine learning methods that rely solely on spectral information and deep learning methods that use only spatial information from visual images. Based on the dataset constructed in this study, experimental results demonstrate that the proposed method achieves an accuracy of 88.99%, surpassing other methods by at least 18.6%. Hyperspectral imaging ; Camellia oleifera ; Artificial intelligence ; Computer science ; Remote sensing ; Biology ; Botany ; Geography Spectroscopy and Chemometric Analyses
Authors: Dennis Delali Kwesi Wayo | V. I. Kudryashov | Mirat Karibayev | Gertrude Ellen Fynn | Khadichakhan Rafikova | Camila Martins Saporetti | Leonardo Goliatt | Nurxat Nuraje
Source: arXiv (Cornell University) (preprint, 2024-12-31)
Field: Materials Science — Materials Chemistry

Exploring Quantum-Dot Engineered Solid-State Photon Upconversion in PbS:$Yb^{3+},Er^{3+}$/CuBiO Using Density Functional Theory and Machine Learning Methods for Water Splitting This study presents a comprehensive numerical analysis of a quantum-dot-engineered heterostructure, PbS:$Yb^{3+},Er^{3+}$/CuBiO, optimized for water splitting applications. Using density functional theory (DFT) coupled with machine learning, the study explores the electronic, optical, and catalytic properties of the material. The optimized PbS structure exhibited a direct bandgap of 1.191 eV, while co-doping with Yb and Er transitioned the material to a metallic state, enhancing charge carrier mobility and electron-hole separation. The final heterostructure displayed an indirect bandgap of 0.431 eV, favorable for visible-light absorption. Key findings include an internal electric field strength of 6.3 Debye, efficient charge transfer confirmed by Bader analysis, and strong optical absorption at 2.4 eV. Machine learning models, including DNN and LSTM, were employed to predict photon absorption rates, achieving mean squared errors as low as 0.0004. The synergistic effects of enhanced internal electric fields, optimized band structures, and strong photon absorption underscore the materials' potential for efficient hydrogen production. This study bridges advanced computational methods and machine learning, offering a framework for designing high-performance photocatalysts in renewable energy applications. Photon upconversion ; Quantum dot ; Photon ; Density functional theory ; Optoelectronics ; Materials science ; Solid-state ; Quantum ; Nanotechnology ; Physics ; Quantum mechanics ; Engineering physics ; Doping Quantum Dots Synthesis And Properties ; Electronic and Structural Properties of Oxides
Authors: Reihaneh Maarefdoust | Xin Zhang | Behrooz Mansouri | Yuqi Song
Source:  (article, 2024-10-26)
Field: Materials Science — Materials Chemistry

Material Visions: Advancing Crystal Structure Prediction with Powerful Text Generation Models The discovery of new materials has been a protracted and labor-intensive endeavor, relying on iterative trial-and-error methodologies. Recently, materials informatics has been transforming this process by employing advanced data science and computational tools to expedite the discovery of novel materials, such as generative design material formulas, and predict material properties. However, predicting crystal three-dimensional structures remains a challenging task rooted in both the fundamental nature of materials and the limitations of current computational methods. Inspired by the power and success of artificial intelligence (AI) models, especially the deep learning techniques and natural language processing (NLP) algorithms, we consider capturing complex atom descriptions and relationships as text information and explore whether we can use the ability of language models to predict atomic coordinates. In this work, we explore multiple text generation models and employ the Longformer-Encoder-Decoder (LED) model to construct preliminary crystal structures based on detailed atom descriptions. Subsequently, these structures are further refined by a random forest regressor, which generates the final crystal configurations. Our experiments show this method excels in capturing the intricate atom relationships and effectively translating these associations into the specified crystal formats. We also focus on optimizing data representation for both atom descriptions and crystal structures and use clear metrics to evaluate accuracy and stability. Our results indicate that this method has promising potential and could improve the prediction of material crystal structures. Our source code can be accessed freely at https://github.com/RMaarefdoust/Crystal-Structure-Prediction. Vision ; Computer science ; Crystal structure prediction ; Crystal (programming language) ; Artificial intelligence ; Crystal structure ; Natural language processing ; Crystallography ; Programming language ; Chemistry ; Sociology Machine Learning in Materials Science
Authors: Ethan P. Shapera | Dejan-Krešimir Buč̌ar | Rohit P. Prasankumar | Christoph Heil
Source: arXiv (Cornell University) (preprint, 2024-02-14)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning assisted prediction of organic salt structure properties We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to describe a crystal by more than an order of magnitude compared to the full crystal graph representation. We construct machine learning models using the crystal graph singular value representations in order to predict the volume, enthalpy per atom, and metal versus semiconducting phase of DFT-relaxed organic salt crystals based on randomly generated unrelaxed crystal structures. Initial base models are trained to relate 89,949 randomly generated structures of salts formed by varying ratios of 1,3,5-triazine and HCl with the corresponding volumes, enthalpies per atom, and phase of the DFT-relaxed structures. We further demonstrate that the base model is able to extrapolate to new chemical systems with the inclusion of 2,000 to 10,000 crystal structures from the new system. After training a single model with a large number of data points, extension can be done at significantly lower cost. The constructed machine learning models can be used to rapidly screen large sets of randomly generated organic salt crystal structures and efficiently downselect the structures most likely to be experimentally realizable. The models can be used either as a stand-alone crystal structure predictor or incorporated into more sophisticated workflows as a filtering step. Salt (chemistry) ; Artificial intelligence ; Computer science ; Environmental science ; Machine learning ; Chemistry ; Organic chemistry Various Chemistry Research Topics
Authors: Haosheng Xu | Dongheng Qian | Jing Wang
Source: arXiv (Cornell University) (preprint, 2024-05-29)
Field: Materials Science — Materials Chemistry

Predicting Many Crystal Properties via an Adaptive Transformer-based Framework Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell information. This novel structure can seamlessly combine diverse features and accurately predict various physical properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT provides insightful interpretations of features influencing target properties. Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties, underscoring their intricate nature. By incorporating these features, we achieve 91\% accuracy in topological classification, surpassing prior studies and identifying previously misclassified materials. This research demonstrates that integrating diverse material information enhances the prediction of complex material properties, paving the way for more accurate and interpretable machine learning models in materials science. Deep learning ; Artificial intelligence ; Econometrics ; Computer science ; Materials science ; Economics Machine Learning in Materials Science
Authors: Tae‐Won Kim | Hyunjin Seo | Sung Soo Ahn | Eunho Yang
Source: arXiv (Cornell University) (preprint, 2024-10-04)
Field: Materials Science — Materials Chemistry

REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring Predicting the ground-state 3D molecular conformations from 2D molecular graphs is critical in computational chemistry due to its profound impact on molecular properties. Deep learning (DL) approaches have recently emerged as promising alternatives to computationally-heavy classical methods such as density functional theory (DFT). However, we discover that existing DL methods inadequately model inter-atomic forces, particularly for non-bonded atomic pairs, due to their naive usage of bonds and pairwise distances. Consequently, significant prediction errors occur for atoms with low degree (i.e., low coordination numbers) whose conformations are primarily influenced by non-bonded interactions. To address this, we propose REBIND, a novel framework that rewires molecular graphs by adding edges based on the Lennard-Jones potential to capture non-bonded interactions for low-degree atoms. Experimental results demonstrate that REBIND significantly outperforms state-of-the-art methods across various molecular sizes, achieving up to a 20\% reduction in prediction error. Ground state ; Graph ; Common ground ; State (computer science) ; Computer science ; Chemistry ; Physics ; Theoretical computer science ; Quantum mechanics ; Communication ; Algorithm ; Psychology Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Gaurav Goel | Jannat ul Firdaus
Source: International Journal of Pharma Professional’s Research (IJPPR) (article, 2024-10-30)
Field: Chemistry — Organic Chemistry

A Review on Structure-Activity Relationship Studies of New Anti-microbial Compounds The increasing prevalence of antimicrobial resistance (AMR) has prompted urgent efforts to discover and develop new antimicrobial compounds.Structure-Activity Relationship (SAR) studies are crucial in the design of these compounds, helping to elucidate the link between molecular structure and biological activity.This review provides an in-depth analysis of SAR studies in the context of antibiotics, antifungals, antivirals, and antiparasitics.It highlights the role of SAR in optimizing the potency, selectivity, and pharmacokinetic properties of antimicrobial agents.Key molecular features influencing activity, including functional groups, lipophilicity, hydrogen bonding, and steric factors, are examined for their impact on antimicrobial efficacy.The review also explores various SAR methodologies, including experimental approaches such as chemical modifications and analog synthesis, alongside computational techniques like Quantitative SAR (QSAR) modeling and molecular docking.Furthermore, case studies on -lactam antibiotics, azole antifungals, and natural product derivatives demonstrate the practical applications of SAR in identifying promising antimicrobial candidates.Advances in SAR, particularly through the use of AI, machine learning, and highthroughput screening, are also discussed.Despite significant progress, challenges remain, such as the complexity of biological systems, multi-target interactions, and the predictive accuracy of computational tools.The review concludes by emphasizing the need for multidisciplinary approaches to overcome these challenges and accelerate the development of new, more effective antimicrobial therapies. Interpretation (philosophy) ; Context (archaeology) ; Relation (database) ; Natural (archaeology) Synthesis and biological activity ; Phenothiazines and Benzothiazines Synthesis and Activities ; Pharmacology and Nanomedicine Research
Authors: Elin Dypvik Sødahl | Jesús Carrete | Georg K. H. Madsen | Kristian Berland
Source: arXiv (Cornell University) (preprint, 2024-10-21)
Field: Materials Science — Materials Chemistry

Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to properties such as room temperature ferroelectricity, low-energy synthesis, malleability, and potential for multiaxial polarization. The ferroelectric molecular salt HdabcoClO4 is of particular interest due to its ultrafast ferroelectric room-temperature switching. However, so far, there is limited understanding of the nature of dynamical disorder arising in these compounds. Here, we employ the neural network NeuralIL to train a machine-learned force field (MLFF) with training data generated using density functional theory. The resulting MLFF-MD simulations exhibit phase transitions and thermal expansion in line with earlier reported experimental results, for both a low-temperature phasetransition coinciding with the orientational disorder of ClO4- molecules and the onset of rotation of Hdabco+ and ClO4- molecules in a high-temperature phase transition. We also find proton transfer even in the low-temperature phase, which increases with temperature and leads to associated proton disorder as well as the onset of disorder in the direction of the hydrogen-bonded chains. Mesophase ; Ferroelectricity ; Field (mathematics) ; Force field (fiction) ; Condensed matter physics ; Materials science ; Nanotechnology ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Optoelectronics ; Liquid crystal ; Pure mathematics Solid-state spectroscopy and crystallography
Authors: Zachary T. P. Fried | Brett A. McGuire
Source: arXiv (Cornell University) (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Automated Mixture Analysis via Structural Evaluation The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes spectroscopic methods are employed to decipher the composition of these mixtures. However, the sheer density of spectral features present in spectroscopic databases can make unambiguous assignment to individual species challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, analysis of the chemical relevance of a molecule is important when determining which species are present in a mixture. In this paper, we combine machine-learning molecular embedding methods with a graph-based ranking system to determine the likelihood of a molecule being present in a mixture based on the other known species and/or chemical priors. By incorporating this metric in a rotational spectroscopy mixture analysis algorithm, we demonstrate that the mixture components can be identified with extremely high accuracy (&gt;97%) in an efficient manner. Computer science Crystallization and Solubility Studies