jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miguel Gallegos | Valentín Vassilev-Galindo | Igor Poltavsky | Ángel Martín Pendás | Alexandre Tkatchenko
Source: Nature Communications (article, 2024-05-21)
Field: Materials Science — Materials Chemistry

Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the origin of the reference data. Alternatively, interpretable real-space tools can be employed directly, but they are often expensive to compute. To address this dilemma between explainability and accuracy, we developed SchNet4AIM, a SchNet-based architecture capable of dealing with local one-body (atomic) and two-body (interatomic) descriptors. The performance of SchNet4AIM is tested by predicting a wide collection of real-space quantities ranging from atomic charges and delocalization indices to pairwise interaction energies. The accuracy and speed of SchNet4AIM breaks the bottleneck that has prevented the use of real-space chemical descriptors in complex systems. We show that the group delocalization indices, arising from our physically rigorous atomistic predictions, provide reliable indicators of supramolecular binding events, thus contributing to the development of Explainable Chemical Artificial Intelligence (XCAI) models. Chemical space ; Computer science ; Artificial intelligence ; Space (punctuation) ; Machine learning ; Bioinformatics ; Biology ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Muhammad Khuram Shahzad | Shoukat Hussain | Muhammad Khan | Muhammad Jehanzaib Aslam | Rawaa M. Mohammed | Vineet Tirth | Hassan Alqahtani | Ali Algahtani | Tawfiq Al‐Mughanam | Waqar Azeem
Source: Scientific Reports (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Computational insights of double perovskite Na2CaCdH6 hydride alloy for hydrogen storage applications: a DFT investigation Prospective use of perovskite hydride materials in H storage a crucial element of clean energy systems has drawn a lot of attention. The structural, electrical, mechanical, thermodynamic, and H storage qualities of Na<sub>2</sub>CaCdH<sub>6</sub> hydride alloys were examined in this work using DFT. According to the structural properties, Na<sub>2</sub>CaCdH<sub>6</sub> has space group 225 (Fm3m), and optimized lattice parameters and volume of Na<sub>2</sub>CaCdH<sub>6</sub> are 3.3485 Å and 593.764 Å<sup>3</sup>. The measured gravimetric H storage capacity of Na<sub>2</sub>CaCdH<sub>6</sub> hydrides is 2.956 wt%. The hydrides under research are semiconductors, as indicated by the computed electronic characteristics. Elastic constants, Pugh's ratio, modulus, Poisson's ratio, anisotropic, and microhardness of Na<sub>2</sub>CaCdH<sub>6</sub> are calculated under mechanical properties. The hydrides are dynamically stable, as indicated by the phonon dispersion curves, but mechanically stable according to the Born criterion for elastic constant (C<sub>ij</sub>). The Cauchy's pressure (C″ = 7.836) revealed the ductile behavior. The electronic and mechanical characteristics imply that Na<sub>2</sub>CaCdH<sub>6</sub> hydride can conduct electricity and is also mechanically stable. Our findings shed light on the possibilities of Na<sub>2</sub>CaCdH<sub>6</sub> perovskite hydride material for H storage utilization. Hydrogen storage ; Hydride ; Alloy ; Perovskite (structure) ; Computer science ; Hydrogen ; Materials science ; Chemistry ; Crystallography ; Metallurgy ; Organic chemistry Hydrogen Storage and Materials ; Thermal Expansion and Ionic Conductivity ; Phase Change Materials Research
Authors: Youheng Pan | Jing Zhang | Xin Guo | Yarou Li | Lanlan Li | Lijia Pan
Source: Polymers (article, 2024-06-04)
Field: Materials Science — Polymers and Plastics

Recent Advances in Conductive Polymers-Based Electrochemical Sensors for Biomedical and Environmental Applications Electrochemical sensors play a pivotal role in various fields, such as biomedicine and environmental detection, due to their exceptional sensitivity, selectivity, stability, rapid response time, user-friendly operation, and ease of miniaturization and integration. In addition to the research conducted in the application field, significant focus is placed on the selection and optimization of electrode interface materials for electrochemical sensors. The detection performance of these sensors can be significantly enhanced by modifying the interface of either inorganic metal electrodes or printed electrodes. Among numerous available modification materials, conductive polymers (CPs) possess not only excellent conductivity exhibited by inorganic conductors but also unique three-dimensional structural characteristics inherent to polymers. This distinctive combination allows CPs to increase active sites during the detection process while providing channels for rapid ion transmission and facilitating efficient electron transfer during reaction processes. This review article primarily highlights recent research progress concerning CPs as an ideal choice for modifying electrochemical sensors owing to their remarkable features that make them well-suited for biomedical and environmental applications. Nanotechnology ; Materials science ; Miniaturization ; Conductive polymer ; Electrode ; Electrical conductor ; Electrochemistry ; Polymer ; Computer science ; Chemistry Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Ifeanyi Onyedika Ekemezie | Wags Numoipiri Digitemie
Source: Engineering Science & Technology Journal (review, 2024-03-24)
Field: Materials Science — Materials Chemistry

CARBON CAPTURE AND UTILIZATION (CCU): A REVIEW OF EMERGING APPLICATIONS AND CHALLENGES As the global community grapples with the urgent need to mitigate the impacts of climate change, Carbon Capture and Utilization (CCU) has emerged as a promising and multifaceted strategy to address carbon emissions. This review paper provides a comprehensive examination of the current state of CCU technologies, focusing on both the emerging applications and the challenges inherent in their implementation. The first section offers an overview of diverse carbon capture methods, ranging from pre-combustion to direct air capture, highlighting key advancements in technologies such as chemical absorption, membrane separation, and adsorption. Subsequently, the paper delves into the various pathways of carbon utilization, exploring applications in the production of fuels, carbon-based materials, and chemical synthesis. Notable breakthroughs in power-to-gas technologies, utilization in construction materials, and industrial applications are discussed in detail. The core of the review is dedicated to unveiling the emerging applications of CCU, with a specific emphasis on its role in agriculture and soil enhancement, integration with renewable energy systems, and collaboration with other industries. The potential economic and environmental benefits of these applications are explored to underscore the transformative impact of CCU on sustainable development. However, the journey towards widespread CCU adoption is not without challenges. This paper identifies and analyzes technical hurdles related to efficiency, scalability, and cost-effectiveness. Additionally, it examines the regulatory, policy, and societal challenges that may hinder the seamless integration of CCU technologies. Case studies of successful CCU projects provide practical insights, emphasizing lessons learned and best practices. The paper concludes by outlining future prospects and opportunities in the field of CCU, emphasizing the need for continued research, development, and international collaboration. It calls for a concerted effort to overcome challenges and harness the full potential of CCU in contributing to global carbon reduction goals. This comprehensive review aims to serve as a valuable resource for researchers, policymakers, and industry professionals involved in climate change mitigation, providing a nuanced understanding of the current landscape, opportunities, and challenges associated with Carbon Capture and Utilization. Keywords: Carbon Capture and Utilization (CCU), Applications, Challenges. Data science ; Environmental science ; Computer science Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis ; Membrane Separation and Gas Transport
Authors: Yiting Luo | Rongkui Su
Source: International Journal of Molecular Sciences (article, 2024-09-12)
Field: Chemistry — Inorganic Chemistry

Preparation of NH2-MIL-101(Fe) Metal Organic Framework and Its Performance in Adsorbing and Removing Tetracycline Tetracycline's accumulation in the environment poses threats to human health and the ecological balance, necessitating efficient and rapid removal methods. Novel porous metal-organic framework (MOF) materials have garnered significant attention in academia due to their distinctive characteristics. This paper focuses on studying the adsorption and removal performance of amino-modified MIL-101(Fe) materials towards tetracycline, along with their adsorption mechanisms. The main research objectives and conclusions are as follows: (1) NH<sub>2</sub>-MIL-101(Fe) MOF materials were successfully synthesized via the solvothermal method, confirmed through various characterization techniques including XRD, FT-IR, SEM, EDS, XPS, BET, and TGA. (2) NH<sub>2</sub>-MIL-101(Fe) exhibited a 40% enhancement in tetracycline adsorption performance compared to MIL-101(Fe), primarily through chemical adsorption following pseudo-second-order kinetics. The adsorption process conformed well to Freundlich isotherm models, indicating multilayer and heterogeneous adsorption characteristics. Thermodynamic analysis revealed the adsorption process as a spontaneous endothermic reaction. (3) An increased adsorbent dosage and temperature correspondingly improved NH<sub>2</sub>-MIL-101(Fe)'s adsorption efficiency, with optimal performance observed under neutral pH conditions. These findings provide new strategies for the effective removal of tetracycline from the environment, thus holding significant implications for environmental protection. Tetracycline ; Metal-organic framework ; Environmental chemistry ; Chemistry ; Metal ; Tetracycline antibiotics ; Adsorption ; Biochemistry ; Organic chemistry ; Antibiotics Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Advanced Nanomaterials in Catalysis
Authors: Yangyang Si | Tianfu Zhang | Chenhan Liu | Sujit Das | Bin Xu | R. G. Burkovsky | Xian‐Kui Wei | Zuhuang Chen
Source: Progress in Materials Science (article, 2024-01-02)
Field: Materials Science — Materials Chemistry

Antiferroelectric oxide thin-films: Fundamentals, properties, and applications Materials science ; Thin film ; Antiferroelectricity ; Oxide ; Engineering physics ; Nanotechnology ; Phase transition ; Optoelectronics ; Transducer ; Electrical engineering ; Ferroelectricity ; Condensed matter physics ; Metallurgy Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Multiferroics and related materials
Authors: Esraa H. Abdel-Gawad | M.I. Sayyed | Taha A. Hanafy | Mohamed Elsafi
Source: Scientific Reports (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Experimental investigation of radiation shielding competence of B2O3-Na2O-Al2O3-BaO-CaO glass system Aiming to extend the scope of utilizing glass in radiation shielding, this work investigates the radiation interaction response of a borate-based glass system. Four borate-glass samples of different substituting concentrations of calcium oxide ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>70</mml:mn> <mml:mo>-</mml:mo> <mml:mi>x</mml:mi></mml:mrow> </mml:math> )B<sub>2</sub>O<sub>3</sub>: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>10</mml:mn></mml:mrow> </mml:math> Na<sub>2</sub>O <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>:</mml:mo> <mml:mn>5</mml:mn></mml:mrow> </mml:math> Al<sub>2</sub>O<sub>3</sub> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>:</mml:mo> <mml:mn>15</mml:mn></mml:mrow> </mml:math> BaO: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>x</mml:mi></mml:math> CaO were prepared. To assess the shielding performance of the prepared glass samples, a high-purity germanium detector and different radioactive sources (different energies) were used. Via the narrow beam method, the linear attenuation coefficients (LACs) were experimentally measured. So, the transmission factor (TF), the half-value layer (HVL), the tenth value layer (TVL), the mean free path (MFP), and the radiation protection efficiency (RPE) were calculated for all prepared samples. It was observed that the increase of the concentration of calcium oxide in the proposed borate-based glass samples leads to improve their performance in shielding against radiation. At low energy, the RPE of the samples is almost 100%. However, it was observed that as energy of the radiation source increases, the shielding performance of the samples will decrease. High energy dependence was found when calculating TF, HVL, TVL, and MFP. They were increased with the increase of the energy of the incident photons. At 0.662 MeV, the TF values are equal to 79.26, 79.00, 79.72, and 78.43% for BNABC-1, BNABC-2, BNABC-3, and BNABC-4 in the same oder, respectively. The application of the proposed composition of borate-based glass as a transparent shield against low-energy ionizing radiation was highlighted. Radiation shielding ; Electromagnetic shielding ; Radiation ; Materials science ; Competence (human resources) ; Radiochemistry ; Mineralogy ; Chemistry ; Composite material ; Physics ; Psychology ; Optics Radiation Shielding Materials Analysis ; Glass properties and applications ; Nuclear materials and radiation effects
Authors: Linfeng Chen | Xinzhi Ding | Zheren Wang | Shutao Xu | Qike Jiang | Chaochao Dun | Jeffrey J. Urban
Source: Surface Science and Technology (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Advances in in situ/operando techniques for catalysis research: enhancing insights and discoveries Abstract Catalysis research has witnessed remarkable progress with the advent of in situ and operando techniques. These methods enable the study of catalysts under actual operating conditions, providing unprecedented insights into catalytic mechanisms and dynamic catalyst behavior. This review discusses key in situ techniques and their applications in catalysis research. Advances in in situ electron microscopy allow direct visualization of catalysts at the atomic scale under reaction conditions. In situ spectroscopy techniques like X-ray absorption spectroscopy and nuclear magnetic resonance spectroscopy can track chemical states and reveal transient intermediates. Synchrotron-based techniques offer enhanced capabilities for in situ studies. The integration of in situ methods with machine learning and computational modeling provides a powerful approach to accelerate catalyst optimization. However, challenges remain regarding radiation damage, instrumentation limitations, and data interpretation. Overall, continued development of multi-modal in situ techniques is pivotal for addressing emerging challenges and opportunities in catalysis research and technology. In situ ; Catalysis ; Nanotechnology ; X-ray absorption spectroscopy ; Instrumentation (computer programming) ; Biochemical engineering ; Computer science ; Materials science ; Systems engineering ; Chemistry ; Absorption spectroscopy ; Engineering ; Physics ; Organic chemistry ; Optics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Abdulrahman Aldossary | Jorge A. Campos-Gonzalez-Angulo | Sergio Pablo‐García | Shi Xuan Leong | Ella Miray Rajaonson | Luca Thiede | Gary Tom | Andrew Z. Wang | Davide Avagliano | Alán Aspuru‐Guzik
Source: Advanced Materials (review, 2024-05-25)
Field: Materials Science — Materials Chemistry

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science. Chemical space ; In silico ; Computer science ; Scalability ; Transferability ; Artificial intelligence ; Computational model ; Machine learning ; Set (abstract data type) ; Multiscale modeling ; Quantitative structure–activity relationship ; Biochemical engineering ; Chemistry ; Computational chemistry ; Drug discovery ; Database ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Di Zhang | Peiyun Yi | Xinmin Lai | Linfa Peng | Hao Li
Source: Nature Communications (article, 2024-01-06)
Field: Materials Science — Materials Chemistry

Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface Substrate-catalyzed growth offers a highly promising approach for the controlled synthesis of carbon nanostructures. However, the growth mechanisms on dynamic catalytic surfaces and the development of more general design strategies remain ongoing challenges. Here we show how an active machine-learning model effectively reveals the microscopic processes involved in substrate-catalyzed growth. Utilizing a synergistic approach of molecular dynamics and time-stamped force-biased Monte Carlo methods, augmented by the Gaussian Approximation Potential, we perform fully dynamic simulations of graphene growth on Cu(111). Our findings accurately replicate essential subprocesses-from the preferred diffusion of carbon monomer/dimer, chain or ring formations to edge-passivated Cu-aided graphene growth and bond breaks by ion impacts. Extending our simulations to carbon deposition on metal surfaces like Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111), our results align closely with experimental observations, providing a practical and efficient approach for designing metallic or alloy substrates to achieve desired carbon nanostructures and explore further reaction possibilities. Graphene ; Substrate (aquarium) ; Materials science ; Carbon fibers ; Nanotechnology ; Molecular dynamics ; Diffusion ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Composite material ; Composite number Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Ion-surface interactions and analysis
Authors: Amir Omranpour | Pablo Montero de Hijes | Jörg Behler | Christoph Dellago
Source: The Journal of Chemical Physics (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing us to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective, we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid-liquid interfaces. Aqueous solution ; Perspective (graphical) ; Computer science ; Statistical physics ; Chemical physics ; Chemistry ; Physics ; Artificial intelligence ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shuhao Li | Chunqing Li | Feng Wang
Source: Materials Today Chemistry (review, 2024-03-11)
Field: Materials Science — Materials Chemistry

Computational experiments of metal corrosion studies: A review This review article underscores the critical role of Density Functional Theory (DFT) in the prediction of corrosion defect structures based on specific chemical compositions. By integrating DFT with Molecular Dynamics (MD) simulations, we gain a more nuanced understanding of corrosion processes. The article further explores how advanced computational approaches, encompassing DFT calculations, MD simulations, and the innovative application of Machine Learning (ML) and Artificial Intelligence (AI), are revolutionizing corrosion studies. These technologies enhance our ability to comprehend and predict the progression of corrosion defect depth across various environments. ML and AI algorithms are particularly noted for their capacity to identify complex patterns, thereby enabling the development of more accurate predictive models for corrosion behavior. As computational resources continue to evolve, leveraging high-performance computing has become pivotal for simulating larger systems and achieving more detailed insights. The convergence of quantum mechanics, molecular dynamics, and artificial intelligence marks a promising frontier for computational experiments in corrosion research, offering profound implications for maintenance strategies and the protection of critical infrastructure. Corrosion ; Computer science ; Molecular dynamics ; Density functional theory ; Computational model ; Computational simulation ; Artificial intelligence ; Computational intelligence ; Biochemical engineering ; Supercomputer ; Convergence (economics) ; Nanotechnology ; Materials science ; Computational science ; Engineering ; Chemistry ; Computational chemistry ; Parallel computing ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability