jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wentong Lu | Hao Tian | Yan Liu | Yiyao Zhu | Peilong Zhou | Jincheng Wang | Long Li | Jianhua Xiao
Source: ACS Applied Polymer Materials (article, 2024-05-30)
Field: Materials Science — Biomaterials

Water Absorption Behavior of Dual-Sponge Structure Sealing Elastomers Assisted by Machine Learning Water-absorbing expanded elastomers hold significant importance in the fields of engineering and construction. However, traditional expanded elastomers exhibit common characteristics such as slow swelling rates, leakage after water absorption, and low strength. This research report proposed an approach for developing high-strength water-absorbing expanded elastomers with a dual-sponge structure. The elastomers were prepared by incorporating a composite water-absorbing resin with a porous structure into a fluoroelastomer matrix. Additionally, this research validates this research under the background of machine learning using a random forest model. The water absorption rate of this research material can reach 30 times its own weight with an extremely rapid water absorption response. Its strength can reach 17.37 MPa, retaining more than 50% of moisture and maintaining environmental humidity between 50 and 60%. The R2 value of the machine learning model reaches 0.998, proving the strong guidance significance of the random forest model. Furthermore, the simplicity of the treatment method employed in this research ensures low economic costs and ease of industrial application. The aim of this study is to improve sealing in water-related environments in infrastructure with strengths up to 3–4 times higher than those of seals commonly used at this stage and with greatly improved water absorption response rates. This makes it possible to completely replace the seals commonly used in shield machines today, and medical devices in the field of hemostatic dressings can be developed by using the strategy of this research as a blueprint. Absorption of water ; Elastomer ; Materials science ; Composite material ; Environmental science ; Process engineering ; Computer science ; Engineering Electrospun Nanofibers in Biomedical Applications ; Polymer composites and self-healing ; Dielectric materials and actuators
Authors: Phillip M. Maffettone | William J. K. Fletcher | Thomas C. Nicholas | Volker L. Deringer | Jane R. Allison | Lorna J. Smith | Andrew L. Goodwin
Source: Faraday Discussions (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

When can we trust structural models derived from pair distribution function measurements? The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies-one concerning the structure of a small peptide and the other amorphous calcium carbonate-we show how consideration of model likelihood can help drive robust structure solution, even in cases where the PDF is particularly information-poor. The obvious thread of these individual case studies is the potential role for machine-learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines. Statistical physics ; Biasing ; Computer science ; Function (biology) ; Distribution function ; Likelihood function ; Econometrics ; Algorithm ; Mathematics ; Physics ; Estimation theory ; Thermodynamics ; Voltage Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Zhen Chen | Ziqi Zhang | Yixin Yu | Yu Guo | Jing Liu | Zhiling Zhu
Source: Journal of Colloid and Interface Science (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Carbocation charge as an interpretable descriptor for the catalytic activity of hydrolytic nanozymes A universal theory for predicting the catalytic activity of hydrolytic nanozymes has yet to be developed. Herein, by investigating the polarization and hydrolysis mechanisms of nanomaterials towards amide bonds, carbocation charge was identified as a key electronic descriptor for predicting catalytic activity in amide hydrolysis. Through machine learning correlation analysis and the Sure Independence Screening and Sparsifying Operator (SISSO) algorithm, this descriptor was interpreted to associate with the d-band center and Lewis acidity on the nanomaterial surface. On this basis, copper nanoparticles (Cu NPs) were discovered to exhibit significant hydrolytic activity. Further, peptidomic analysis and molecular dynamics simulations showed that Cu NPs demonstrated substrate selectivity. In the presence of water molecules, hydrophobic amino acid residues were driven towards the nanomaterial surface by hydrophobic groups of proteins, leading to the preferential hydrolysis of peptide bonds linked to these residues. This study provided a theoretic framework for predicting highly efficient hydrolytic nanozymes with broad potential applications. Carbocation ; Charge (physics) ; Catalysis ; Hydrolysis ; Chemistry ; Computational chemistry ; Medicinal chemistry ; Physics ; Organic chemistry ; Particle physics Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Nuo Yang | Rongkun Chen | Yinong Liu | Weina Ren | Shiqian Hu
Source: Physical review. B./Physical review. B (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Numerical evaluation of the effect of the twist angle on phonon hydrodynamics in twisted bilayer graphene The influence of twist angles on the thermal transport properties of bilayer graphene has attracted considerable attention within the scientific community. However, the relationship between twist angle and phonon hydrodynamics remains insufficiently explored. Additionally, the limitations of empirical potential fields have often restricted the accuracy of previous calculations. In this study, we systematically examine the effect of twist angle on phonon hydrodynamics in twisted bilayer graphene using a machine learned neuroevolution potential combined with the Boltzmann transport equation. Our results reveal a distinctive ``$\mathsf{V}$-shaped'' trend in thermal conductivity, driven by variations in N-type and U-type phonon scattering rates, underscoring the significant influence of twist angle on phonon hydrodynamics. Particularly, the flexural mode plays a critical role, with its contribution to thermal conductivity decreasing as the twist angle changes. This analysis highlights the importance of machine learning potentials in accurately capturing interlayer interactions, providing a more precise understanding of thermal transport in two-dimensional materials and offering alternative possibilities for material manipulation. Bilayer graphene ; Twist ; Phonon ; Graphene ; Condensed matter physics ; Bilayer ; Materials science ; Physics ; Nanotechnology ; Geometry ; Mathematics ; Chemistry ; Membrane Thermal properties of materials ; Graphene research and applications ; Advancements in Semiconductor Devices and Circuit Design
Authors: Martin Priessner | Richard J. Lewis | Magnus J. Johansson | Jonathan M. Goodman | Jon Paul Janet | Anna Tomberg
Source: Journal of Chemical Information and Modeling (article, 2024-03-27)
Field: Chemistry — Spectroscopy

HSQC Spectra Simulation and Matching for Molecular Identification In the pursuit of improved compound identification and database search tasks, this study explores heteronuclear single quantum coherence (HSQC) spectra simulation and matching methodologies. HSQC spectra serve as unique molecular fingerprints, enabling a valuable balance of data collection time and information richness. We conducted a comprehensive evaluation of the following four HSQC simulation techniques: ACD/Labs (ACD), MestReNova (MNova), Gaussian NMR calculations (DFT), and a graph-based neural network (ML). For the latter two techniques, we developed a reconstruction logic to combine proton and carbon 1D spectra into HSQC spectra. The methodology involved the implementation of three peak-matching strategies (minimum-sum, Euclidean-distance, and Hungarian distance) combined with three padding strategies (zero-padding, peak-truncated, and nearest-neighbor double assignment). We found that coupling these strategies with a robust simulation technique facilitates the accurate identification of correct molecules from similar analogues (regio- and stereoisomers) and allows for fast and accurate large database searches. Furthermore, we demonstrated the efficacy of the best-performing methodology by rectifying the structures of a set of previously misidentified molecules. This research indicates that effective HSQC spectral simulation and matching methodologies significantly facilitate molecular structure elucidation. Furthermore, we offer a Google Colab notebook for researchers to use our methods on their own data (https://github.com/AstraZeneca/hsqc_structure_elucidation.git). Heteronuclear single quantum coherence spectroscopy ; Computer science ; Matching (statistics) ; Identification (biology) ; Gaussian ; Pattern recognition (psychology) ; Algorithm ; Data mining ; Artificial intelligence ; Two-dimensional nuclear magnetic resonance spectroscopy ; Chemistry ; Mathematics ; Physics ; Computational chemistry ; Nuclear magnetic resonance ; Statistics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: John F. Gamble | Hisham Al-Obaidi
Source: Journal of Pharmaceutical Sciences (review, 2024-08-15)
Field: Materials Science — Materials Chemistry

Past, Current, and Future: Application of Image Analysis in Small Molecule Pharmaceutical Development The often-perceived limitations of image analysis have for many years impeded the widespread application of such systems as first line characterisation tools. Image analysis has, however, undergone a notable resurgence in the pharmaceutical industry fuelled by developments system capabilities and the desire of scientists to characterize the morphological nature of their particles more adequately. The importance of particle shape as well as size is now widely acknowledged. With the increasing use of modelling and simulations, and ongoing developments though the integration of machine learning and artificial intelligence, the utility of image analysis is increasing significantly driven by the richness of the data obtained. Such datasets provide means to circumvent the requirement to rely on less informative descriptors and enable the move towards the use of whole distributions. Combining the improved particle size and shape measurement and description with advances in modelling and simulations is enabling improved means to elucidate the link between particle and bulk powder properties. In addition to improved capabilities to describe input materials, approaches to characterize single components within multicomponent systems are providing scientists means to understand how their material may change during manufacture thus providing a means to link the behaviour of final dosage forms with the particle properties at the point of action. The aim is to provide an overview of image analysis and update readers with innovations and capabilities to other methods in the small molecule arena. We will also describe the use of AI for the improved analysis using image analysis. Computer science ; Data science ; Image (mathematics) ; Point (geometry) ; Artificial intelligence ; Biochemical engineering ; Engineering ; Mathematics Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Mineral Processing and Grinding
Authors: Umapathi Krishnamoorthy | Sukanya Balasubramani
Source: Advanced Theory and Simulations (review, 2024-10-30)
Field: Materials Science — Materials Chemistry

Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience Abstract Artificial Intelligence (AI) is pivotal in advancing science, including nanomaterial studies. This review explores AI‐based image processing in nanoscience, focusing on algorithms to enhance characterization results from instruments like scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, atomic force microscopy etc. It addresses the significance of AI in nanoscience, challenges in advancing AI‐based image processing for nano material characterization, and AI's role in structural analysis, property prediction, deriving structure‐property relations, dataset augmentation, and improving model robustness. Key AI techniques such as Graph Neural Networks, adversarial training, transfer learning, generative models, attention mechanisms, and federated learning are highlighted for their contributions to nano science studies. The review concludes by outlining persisting challenges and thrust areas for future research, aiming to propel nanoscience with AI. This comprehensive analysis underscores the importance of AI‐powered image processing in nanomaterial characterization, offering valuable insights for researchers. Nanomaterials ; Nanotechnology ; Materials science ; Computer science ; Systems engineering ; Engineering Machine Learning in Materials Science ; Brain Tumor Detection and Classification ; Advanced X-ray and CT Imaging
Authors: Mohamadali Tofigh | M. Fakouri Hasanabadi | Daniel J. Smith | A. Kharazmi | Amir Reza Hanifi | Charles Robert Koch | Mahdi Shahbakhti
Source: Journal of Dynamic Systems Measurement and Control (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Control-Oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling Using a Novel Deep Learning Approach Abstract A solid oxide fuel cell (SOFC) is a multiphysics system that involves heat transfer, mass transport, and electrochemical reactions to produce electrical power. Reduction and re-oxidation (Redox) cycling is a destructive reaction that can occur during SOFC operation. Redox induces various degradation mechanisms, such as electrode delamination, nickel agglomeration, and microstructural changes, which should be mitigated. The interplay of these mechanisms makes a post-Redox SOFC a nonlinear, time-varying, nonstationary dynamic system. Physics-based modeling of these complexities often leads to computationally expensive equations that are not suitable for the control and diagnostics of SOFCs. Here, a data-driven approach based on dilated convolutions and a self-attention mechanism is introduced to effectively capture the dynamics underlying SOFCs affected by Redox. Controlled Redox cycles are designed to collect appropriate experimental data for developing deep learning models, which are lacking in the current literature. The performance of the proposed model is validated on diverse unseen data sets gathered from different fuel cells and benchmarked against state-of-the-art models, in terms of prediction accuracy and computation complexity. The results indicate 31% accuracy improvement and 27% computation speed enhancement compared to the benchmarks. Cycling ; Redox ; Fuel cells ; Solid oxide fuel cell ; Chemical engineering ; Oxide ; Computer science ; Control (management) ; Materials science ; Biological system ; Nanotechnology ; Process engineering ; Artificial intelligence ; Chemistry ; Electrode ; Engineering ; Metallurgy ; Anode ; Biology Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Fault Detection and Control Systems
Authors: Lingling Kuang | Xuecong Tian | Ying Hua Su | Chen Chen | Lu Zhao | Xuan Ma | Lei Han | Cheng Chen | Jianjie Zhang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-12-30)
Field: Chemistry — Analytical Chemistry

Rapid identification of horse oil adulteration based on deep learning infrared spectroscopy detection method Chemistry ; Chromatography ; Infrared spectroscopy ; Identification (biology) ; Horse ; Infrared ; Analytical Chemistry (journal) ; Organic chemistry ; Botany ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Shuai Qi | Wenliang Li | Chen Zhao | Jiamei Yu
Source: Materials Today Communications (article, 2024-08-01)
Field: Chemistry — Inorganic Chemistry

Recent advances of computational simulations on carbon capture in MOFs Metal-organic framework ; Materials science ; Computational model ; Nanotechnology ; Process (computing) ; Biochemical engineering ; Gas separation ; Systems engineering ; Membrane ; Computer science ; Adsorption ; Artificial intelligence ; Engineering ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Chenglong Li | Chengqing Yuan | Tianqi Yang | Hao Luo | Richard Chahine | Liang Tong | Yupeng Yuan | Jinsheng Xiao
Source: Separation and Purification Technology (article, 2024-09-03)
Field: Chemistry — Inorganic Chemistry

Breakthrough curves of H2/CO2 adsorptions on CuBTC and MIL-125(Ti)_NH2 predicted by empirical correlations and deep neural networks Artificial neural network ; Thermodynamics ; Chemistry ; Materials science ; Chemical engineering ; Computer science ; Artificial intelligence ; Engineering ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Industrial Gas Emission Control
Authors: Bálint Máté | François Fleuret | Tristan Bereau
Source: The Journal of Physical Chemistry Letters (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Neural Thermodynamic Integration: Free Energies from Energy-Based Diffusion Models Thermodynamic integration (TI) offers a rigorous method for estimating free-energy differences by integrating over a sequence of interpolating conformational ensembles. However, TI calculations are computationally expensive and typically limited to coupling a small number of degrees of freedom due to the need to sample numerous intermediate ensembles with sufficient conformational-space overlap. In this work, we propose to perform TI along an alchemical pathway represented by a trainable neural network, which we term Neural TI. Critically, we parametrize a time-dependent Hamiltonian interpolating between the interacting and noninteracting systems and optimize its gradient using a score matching objective. The ability of the resulting energy-based diffusion model to sample all intermediate ensembles allows us to perform TI from a single reference calculation. We apply our method to Lennard-Jones fluids, where we report accurate calculations of the excess chemical potential, demonstrating that Neural TI reproduces the underlying changes in free energy without the need for simulations at interpolating Hamiltonians. Diffusion ; Statistical physics ; Energy (signal processing) ; Thermodynamic integration ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Phase Equilibria and Thermodynamics
Authors: Mark DelloStritto | Michael L. Klein
Source: The Journal of Physical Chemistry Letters (article, 2024-08-28)
Field: Materials Science — Materials Chemistry

Understanding Strain and Failure of a Knot in Polyethylene Using Molecular Dynamics with Machine-Learned Potentials A neural network potential (NNP) has been developed by fitting to ab initio electronic structure data on hydrocarbons and is used to study failure of linear and knotted polyethylene (PE) chains. A linear PE chain must be highly strained before breaking as the stress is equally distributed across the chain. In contrast, the stress in a PE chain with a 3<sub>1</sub> or overhand knot, accumulates at the knot's entrance/exit. We find the strain energy is greatest when the bond length and angle are strained simultaneously, and that the knot weakens the chain by increasing the variance of the C-C-C angle, thereby allowing rupture at lower bond strains. We extend our analysis to both 5<sub>1</sub> and 5<sub>2</sub> knots and find that both break at the entrance/exit of a loop. Notably, molecular scale PE knots exhibit many of the same characteristics as knots in a macroscopic rope, with stick-slip phenomena upon tightening and similar points of failure. Knot (papermaking) ; Polyethylene ; Molecular dynamics ; Dynamics (music) ; Strain (injury) ; Biological system ; Materials science ; Computer science ; Chemistry ; Computational chemistry ; Composite material ; Physics ; Biology ; Anatomy Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Junwei Li | Chengfu yang | A Qinhua | Qiusong Lan | Lijun Yun | Yuelong Xia
Source: ACS Applied Materials & Interfaces (article, 2024-09-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On-Demand Design of Metasurfaces through Multineural Network Fusion In this paper, a multineural network fusion freestyle metasurface on-demand design method is proposed. The on-demand design method involves rapidly generating corresponding metasurface patterns based on the user-defined spectrum. The generated patterns are then input into a simulator to predict their corresponding S-parameter spectrogram, which is subsequently analyzed against the real S-parameter spectrogram to verify whether the generated metasurface patterns meet the desired requirements. The methodology is based on three neural network models: a Wasserstein Generative Adversarial Network model with a U-net architecture (U-WGAN) for inverse structural design, a Variational Autoencoder (VAE) model for compression, and an LSTM + Attention model for forward S-parameter spectrum prediction validation. The U-WGAN is utilized for on-demand reverse structural design, aiming to rapidly discover high-fidelity metasurface patterns that meet specific electromagnetic spectrum responses. The VAE, as a probabilistic generation model, serves as a bridge, mapping input data to latent space and transforming it into latent variable data, providing crucial input for a forward S-parameter spectrum prediction model. The LSTM + Attention network, acting as a forward S-parameter spectrum prediction model, can accurately and efficiently predict the S-parameter spectrum corresponding to the latent variable data and compare it with the real spectrum. In addition, the digits "0" and "1" are used in the design to represent vacuum and metallic materials, respectively, and a 10 × 10 cell array of freestyle metasurface patterns is constructed. The significance of the research method proposed in this paper lies in the following: (1) The freestyle metasurface design significantly expands the possibility of metamaterial design, enabling the creation of diverse metasurface structures that are difficult to achieve with traditional methods. (2) The on-demand design approach can generate high-fidelity metasurface patterns that meet the expected electromagnetic characteristics and responses. (3) The fusion of multiple neural networks demonstrates high flexibility, allowing for the adjustment of network structures and training methods based on specific design requirements and data characteristics, thus better accommodating different design problems and optimization objectives. Autoencoder ; Computer science ; Spectrogram ; Latent variable ; Variable (mathematics) ; Artificial neural network ; Generative model ; Algorithm ; Artificial intelligence ; Pattern recognition (psychology) ; Generative grammar ; Mathematics Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Advanced Antenna and Metasurface Technologies