jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anastasiia Tukova | Yihan Nie | Mohammad Tavakkoli Yaraki | Ngoc Thanh Tran | Jiaqiu Wang | Alison Rodger | Yuantong Gu | Yuling Wang
Source: Aggregate (article, 2023-02-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Shape dependent protein‐induced stabilization of gold nanoparticles: From a protein corona perspective Abstract Abstract Gold nanoparticles (AuNPs) are promising materials for many bioapplications. However, upon contacting with biological media, AuNPs undergo changes. The interaction with proteins results in the so‐called protein corona (PC) around AuNPs, leading to the new bioidentity and optical properties. Understanding the mechanisms of PC formation and its functions can help us to utilise its benefits and avoid its drawbacks. To date, most of the previous works aimed to understand the mechanisms governing PC formation and focused on the spherical nanoparticles, although non‐spherical nanoparticles are designed for a wide range of applications in biosensing. In this work, we investigated the differences in PC formation on spherical and anisotropic AuNPs (nanostars in particular) from the joint experimental (extinction spectroscopy, zeta potential and surface‐enhanced Raman scattering [SERS]) and computational methods (the finite element method and molecular dynamics [MD] simulations). We discovered that protein does not fully cover the surface of anisotropic nanoparticles, leaving SERS hot‐spots at the tips and high curvature edges ‘available’ for analyte binding (no SERS signal after pre‐incubation with protein) while providing protein‐induced stabilization (indicated by extinction spectroscopy) of the AuNPs by providing a protein layer around the particle's core. The findings are confirmed from our MD simulations, the adsorption energy significantly decreases with the increased radius of curvature, so that tips (adsorption energy: 2762.334 kJ/mol) would be the least preferential binding site compared to core (adsorption energy: 11819.263 kJ/mol). These observations will help the development of new nanostructures with improved sensing and targeting ability. Colloidal gold ; Nanoparticle ; Protein adsorption ; Nanotechnology ; Adsorption ; Materials science ; Nanomedicine ; Biosensor ; Nanostructure ; Spectroscopy ; Chemical physics ; Chemistry ; Physical chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Protein Interaction Studies and Fluorescence Analysis ; Nanoparticle-Based Drug Delivery
Authors: Dalila Di Francesco | Alexa Pigliafreddo | Simona Casarella | Luca Di Nunno | Diego Mantovani | Francesca Boccafoschi
Source: Biomolecules (review, 2023-09-14)
Field: Materials Science — Biomaterials

Biological Materials for Tissue-Engineered Vascular Grafts: Overview of Recent Advancements The clinical demand for tissue-engineered vascular grafts is still rising, and there are many challenges that need to be overcome, in particular, to obtain functional small-diameter grafts. The many advances made in cell culture, biomaterials, manufacturing techniques, and tissue engineering methods have led to various promising solutions for vascular graft production, with available options able to recapitulate both biological and mechanical properties of native blood vessels. Due to the rising interest in materials with bioactive potentials, materials from natural sources have also recently gained more attention for vascular tissue engineering, and new strategies have been developed to solve the disadvantages related to their use. In this review, the progress made in tissue-engineered vascular graft production is discussed. We highlight, in particular, the use of natural materials as scaffolds for vascular tissue engineering. Tissue engineering ; Vascular graft ; Vascular tissue ; Biomedical engineering ; Tissue repair ; Biological tissue ; Biological materials ; Biochemical engineering ; Medicine ; Engineering ; Biology Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Bone Tissue Engineering Materials
Authors: Zulbirri Faizol | Farid Zubir | Norhafezaidi Mat Saman | Mohd Hafizi Ahmad | M. K. A. Rahim | Osman Ayop | Muzammil Jusoh | Huda A. Majid | Zubaida Yusoff
Source: Applied Sciences (review, 2023-08-24)
Field: Materials Science — Materials Chemistry

Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review The detection of partial discharge (PD) activities in high-voltage equipment can be conducted according to several mechanisms of signal detection, including electromagnetic wave signal detection, acoustic signal detection, chemical reactions, electrical signal detection, and optical emission detection. Recently, multiple methods of detection and localization of partial discharge activities, which occurred in power transformers and gas-insulated switchgear (GIS), have been proposed to monitor the health condition of high-voltage equipment, especially when the awareness regarding preventive maintenance has been emphasized at the industrial level and among electrical providers. In aligning the needs of the industrial sector and the improvement of PD-detection methods, this manuscript focuses on reviewing the current practice methods for the detection and localization of PD signals in high-voltage equipment, comparing their efficacy, and summarizing the future direction of research work-related methods of PD detection. The comparative reviews are discussed in terms of the mechanism of PD signal detection, indication parameters, calibration techniques, and the advantages and limitations of each method of PD measurement in detail. Switchgear ; Partial discharge ; Transformer ; High voltage ; Detection theory ; SIGNAL (programming language) ; Voltage ; Electrical engineering ; Circuit breaker ; Computer science ; Electronic engineering ; Engineering ; Reliability engineering ; Detector High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Viktor Zaverkin | David Holzmüller | Luca Bonfirraro | Johannes Kästner
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Transfer learning for chemically accurate interatomic neural network potentials Developing machine learning-based interatomic potentials from <i>ab initio</i> electronic structure methods remains a challenging task for computational chemistry and materials science. This work studies the capability of transfer learning, in particular discriminative fine-tuning, for efficiently generating chemically accurate interatomic neural network potentials on organic molecules from the MD17 and ANI data sets. We show that pre-training the network parameters on data obtained from density functional calculations considerably improves the sample efficiency of models trained on more accurate <i>ab initio</i> data. Additionally, we show that fine-tuning with energy labels alone can suffice to obtain accurate atomic forces and run large-scale atomistic simulations, provided a well-designed fine-tuning data set. We also investigate possible limitations of transfer learning, especially regarding the design and size of the pre-training and fine-tuning data sets. Finally, we provide GM-NN potentials pre-trained and fine-tuned on the ANI-1x and ANI-1ccx data sets, which can easily be fine-tuned on and applied to organic molecules. Artificial neural network ; Computer science ; Discriminative model ; Transfer of learning ; Set (abstract data type) ; Ab initio ; Interatomic potential ; Data set ; Work (physics) ; Artificial intelligence ; Machine learning ; Biological system ; Chemistry ; Molecular dynamics ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Lipichanda Goswami | Manoj Kumar Deka | Mohendra Roy
Source: Advanced Engineering Materials (review, 2023-04-11)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high‐performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)‐based methods are faster and more accurate than DFT‐based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed. Artificial intelligence ; Generative grammar ; Artificial neural network ; Computer science ; Deep learning ; Machine learning ; Property (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Vincenzo Barone | Marco Fusè | Federico Lazzari | Giordano Mancini
Source: Journal of Chemical Theory and Computation (article, 2023-02-02)
Field: Chemistry — Spectroscopy

Benchmark Structures and Conformational Landscapes of Amino Acids in the Gas Phase: A Joint Venture of Machine Learning, Quantum Chemistry, and Rotational Spectroscopy The accurate characterization of prototypical bricks of life can strongly benefit from the integration of high resolution spectroscopy and quantum mechanical computations. We have selected a number of representative amino acids (glycine, alanine, serine, cysteine, threonine, aspartic acid and asparagine) to validate a new computational setup rooted in quantum-chemical computations of increasing accuracy guided by machine learning tools. Together with low-lying energy minima, the barriers ruling their interconversion are evaluated in order to unravel possible fast relaxation paths. Vibrational and thermal effects are also included in order to estimate relative free energies at the temperature of interest in the experiment. The spectroscopic parameters of all the most stable conformers predicted by this computational strategy, which do not have low-energy relaxation paths available, closely match those of the species detected in microwave experiments. Together with their intrinsic interest, these accurate results represent ideal benchmarks for more approximate methods. Conformational isomerism ; Maxima and minima ; Quantum ; Quantum chemistry ; Spectroscopy ; Alanine ; Asparagine ; Physics ; Amino acid ; Computational chemistry ; Chemistry ; Computer science ; Molecule ; Mathematics ; Quantum mechanics Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Yulan Han | Jiayan Xu | Wenbo Xie | Zhuozheng Wang | P. Hu
Source: ACS Catalysis (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H<sub>2</sub> Activation Using Machine Learning-Accelerated First-Principles Simulations Oxygen vacancies (OVs) play important roles on any oxide catalysts. In this work, using an investigation of the OV effects on ZnO(101̅0) for CO and H<sub>2</sub> activation as an example, we demonstrate, via machine learning potentials (MLPs), genetic algorithm (GA)-based global optimization, and density functional theory (DFT) validations, that the ZnO(101̅0) surface with 0.33 ML OVs is the most likely surface configuration under experimental conditions (673 K and 2.5 MPa syngas (H<sub>2</sub>:CO = 1.5)). It is found that a surface reconstruction from the wurtzite structure to a body-centered-tetragonal one would occur in the presence of OVs. We show that the OVs create a Zn<sub>3</sub> cluster site, allowing H<sub>2</sub> homolysis and C-O bond cleavage to occur. Furthermore, the activity of intrinsic sites (Zn<sub>3c</sub> and O<sub>3c</sub> sites) is almost invariable, while the activity of the generated OV sites is strongly dependent on the concentration of the OVs. It is also found that OV distributions on the surface can considerably affect the reactions; the barrier of C-O bond dissociation is significantly reduced when the OVs are aligned along the [12̅10] direction. These findings may be general in the systems with metal oxides in heterogeneous catalysis and may have significant impacts on the field of catalyst design by regulating the concentration and distribution of the OVs. Catalysis ; Density functional theory ; Homolysis ; Dissociation (chemistry) ; Bond cleavage ; Tetragonal crystal system ; Oxygen ; Oxide ; Wurtzite crystal structure ; Cluster (spacecraft) ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Physical chemistry ; Crystallography ; Computer science ; Crystal structure ; Radical Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Xuecheng Shao | Lukas Paetow | Mark E. Tuckerman | Michele Pavanello
Source: Nature Communications (article, 2023-10-07)
Field: Materials Science — Materials Chemistry

Machine learning electronic structure methods based on the one-electron reduced density matrix The theorems of density functional theory (DFT) establish bijective maps between the local external potential of a many-body system and its electron density, wavefunction and, therefore, one-particle reduced density matrix. Building on this foundation, we show that machine learning models based on the one-electron reduced density matrix can be used to generate surrogate electronic structure methods. We generate surrogates of local and hybrid DFT, Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like benzene and propanol. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. The surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and infrared spectra, which account for anharmonicity and thermal effects, without the need to employ computationally expensive algorithms such as self-consistent field theory. The algorithms are packaged in an efficient and easy to use Python code, QMLearn, accessible on popular platforms. Density functional theory ; Density matrix ; Electronic structure ; Fock matrix ; Orbital-free density functional theory ; Wave function ; Ab initio ; Statistical physics ; Atomic orbital ; Physics ; Computer science ; Quantum ; Quantum mechanics ; Time-dependent density functional theory ; Electron ; Fock space Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Tsz Wai Ko | Jonas A. Finkler | Stefan Goedecker | Jörg Behler
Source: Journal of Chemical Theory and Computation (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions, the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors. In this work we show that including─in addition to structural information─the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows current limitations of two- and three-body based feature vectors to be overcome regarding artificially degenerate atomic environments. The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt. Computer science ; Pairwise comparison ; Electrostatics ; Benchmark (surveying) ; Embedding ; Transferability ; Charge (physics) ; Artificial neural network ; Statistical physics ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Chemical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Abir Boublia | Zahir Guezzout | N. Haddaoui | Michaël Badawi | Ahmad S. Darwish | Tarek Lemaoui | Fawzi Banat | Krishna Kumar Yadav | Byong‐Hun Jeon | Noureddine Elboughdiri | Yacine Benguerba | Inas M. AlNashef
Source: Journal of Materials Chemistry A (article, 2023-12-11)
Field: Materials Science — Polymers and Plastics

Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical conductivity and gas sensing performance This study employs various machine learning algorithms to model the electrical conductivity and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a comprehensive dataset gathered from over 100 references. Polyaniline ; Nanocomposite ; Graphene ; Outlier ; Resilience (materials science) ; Anomaly detection ; Conductivity ; Materials science ; Electrical resistivity and conductivity ; Computer science ; Artificial intelligence ; Nanotechnology ; Composite material ; Physics ; Electrical engineering ; Engineering ; Polymer Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Xiao Shang | Zhiying Liu | Jiahui Zhang | Tianyi Lyu | Yu Zou
Source: Materials Today (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Tailoring the mechanical properties of 3D microstructures: A deep learning and genetic algorithm inverse optimization framework Inverse ; Microstructure ; Genetic algorithm ; Computer science ; Materials science ; Process (computing) ; Transferability ; Range (aeronautics) ; Limiting ; Algorithm ; Mathematical optimization ; Mechanical engineering ; Machine learning ; Mathematics ; Engineering ; Composite material Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Additive Manufacturing Materials and Processes
Authors: Changping Zhang | Maoliang Wei | Jun Zheng | Shujun Liu | Hongyuan Cao | Yishu Huang | Ying Tan | Ming Zhang | Yiwei Xie | Zejie Yu | Junying Li | Hui Ye | Lan Li | Hongtao Lin | Huan Li | Yaocheng Shi | Liu Liu | Daoxin Dai
Source: Advanced Optical Materials (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

Nonvolatile Multilevel Switching of Silicon Photonic Devices with In<sub>2</sub>O<sub>3</sub>/GST Segmented Structures Abstract Reconfigurable silicon photonic devices are widely used in numerous emerging fields such as optical interconnects, photonic neural networks, quantum computing, and microwave photonics. Currently, phase change materials (PCMs) have been extensively investigated as promising candidates for building switching units due to their strong refractive index modulation. Here, nonvolatile multilevel switching of silicon photonic devices with Ge 2 Sb 2 Te 5 (GST) is demonstrated with In 2 O 3 transparent microheaters that are compatible with diverse material platforms. With GST integrated on the silicon photonic waveguides and Mach‐Zehnder interferometers (MZIs), repeatable and reversible multilevel modulation of GST is achieved by electro‐thermally induced phase transitions. Particularly, the segmented switching unit of In 2 O 3 and GST is proposed and demonstrated to be capable of producing about one order of magnitude larger temperature gradient than that of the nonsegmented unit, resulting in up to 64 distinguishable switching levels of 6‐bit precision, and fine‐tuning of the switching voltage pulses is promising to push the precision even further, to 7‐bit, or 128 distinguishable switching levels. The capability of precise multilevel phase‐change modulation is crucial to further facilitate the development of nonvolatile reconfigurable switches and variable attenuation devices as building blocks in large‐scale programmable optoelectronic systems. Photonics ; Materials science ; Silicon photonics ; Optoelectronics ; Modulation (music) ; Optical switch ; Phase modulation ; Silicon ; Phase (matter) ; Physics Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Hannes Kneiding | Ruslan Lukin | Lucas Lang | Simen Reine | Thomas Bondo Pedersen | Riccardo De Bin | David Balcells
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Deep learning metal complex properties with natural quantum graphs Deep graph learning based on electronic structure can contribute to the accelerated discovery of transition metal complexes. Deep learning ; Computer science ; Artificial intelligence ; Quantum ; Metal ; Materials science ; Physics ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts