jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunchi Zhou | Ying Wang | Willie J.G.M. Peijnenburg | Martina G. Vijver | Surendra Balraadjsing | Zhaomin Dong | Xiaoli Zhao | Kmy Leung | Holly M. Mortensen | Zhenyu Wang | Iseult Lynch | Antreas Afantitis | Yunsong Mu | Fengchang Wu | Wenhong Fan
Source: Environmental Science & Technology (review, 2024-08-07)
Field: Materials Science — Materials Chemistry

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, <i>in silico</i> methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an <i>in silico</i> predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs. Pace ; Nanotoxicology ; Perspective (graphical) ; Human health ; Risk analysis (engineering) ; Computer science ; Nanotechnology ; Medicine ; Environmental health ; Artificial intelligence ; Nanoparticle Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods
Authors: Shuyao Lin | Luis Casillas‐Trujillo | Ferenc Tasnádi | Lars Hultman | P.H. Mayrhofer | Davide G. Sangiovanni | Nikola Koutná
Source: npj Computational Materials (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics Abstract Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since extended defects—governing plasticity and crack nucleation in most materials—are too large to be included in the training set. Using TiB 2 as a model ceramic material, we propose a training strategy for MLIPs suitable to simulate mechanical response of monocrystals until failure. Our MLIP accurately reproduces ab initio stresses and fracture mechanisms during room-temperature uniaxial tensile deformation of TiB 2 at the atomic scale ( ≈ 10 3 atoms). More realistic tensile tests (low strain rate, Poisson’s contraction) at the nanoscale ( ≈ 10 4 –10 6 atoms) require MLIP up-fitting, i.e., learning from additional ab initio configurations. Consequently, we elucidate trends in theoretical strength, toughness, and crack initiation patterns under different loading directions. As our MLIP is specifically trained to modelling tensile deformation, we discuss its limitations for description of different loading conditions and lattice structures with various Ti/B stoichiometries. Finally, we show that our MLIP training procedure is applicable to diverse ceramic systems. This is demonstrated by developing MLIPs which are subsequently validated by simulations of uniaxial strain and fracture in TaB 2 , WB 2 , ReB 2 , TiN, and Ti 2 AlB 2 . Materials science ; Nanoscopic scale ; Deformation (meteorology) ; Ceramic ; Fracture (geology) ; Ultimate tensile strength ; Composite material ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced materials and composites
Authors: Zewei Lyu | Yaodong Liu | Anna Ściążko | Yosuke Komatsu | Junyi Tao | A. Nakamura | Toru Hara | Kaihua Sun | Naoki Shikazono | Minfang Han
Source: Advanced Energy Materials (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Co‐Generation of Electricity and Chemicals From Methane Using Direct Internal Reforming Solid Oxide Fuel Cells Abstract The co‐generation of electricity and chemicals via direct internal reforming solid oxide fuel cells (DIR‐SOFCs) offers a promising route to carbon‐neutral energy solutions. However, challenges such as inadequate performance and fast degradation, particularly when using hydrocarbon fuels like CH 4 , hinder the deployment of DIR‐SOFC technology. This study addresses three critical issues: the effect of fuel composition on electrochemical properties, the mechanisms and microstructural impacts of carbon deposition, and the practical feasibility of DIR‐SOFCs at an industrial scale. First, comprehensive polarization and impedance analyses are conducted to assess the impact of varying fuel compositions—specifically p (CH 4 ) and p (H 2 O)—on DIR‐SOFC performance. Second, advanced morphological characterization, machine learning‐assisted 3D reconstructions, and numerical simulations are utilized to reveal carbon deposition behavior and its effects on anode microstructures. Quantitative analysis of carbon's impact on pores, Ni, and YSZ phases provides novel insights into carbon‐induced microstructural changes. Finally, the industrial‐scale co‐generation of electricity and chemicals is validated, emphasizing both energy efficiency and operational stability. This study enhances the understanding of electrochemical and microstructural mechanisms, offering crucial insights for optimizing DIR‐SOFC design and operation, and laying the groundwork for their broader adoption in a carbon‐neutral future. Materials science ; Methane ; Electricity ; Chemical engineering ; Oxide ; Fuel cells ; Methane reformer ; Electricity generation ; Waste management ; Steam reforming ; Process engineering ; Catalysis ; Hydrogen production ; Metallurgy ; Power (physics) ; Thermodynamics ; Organic chemistry ; Chemistry Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Chemical Looping and Thermochemical Processes
Authors: Jacob Wekalao | Ngaira Mandela
Source: Plasmonics (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Graphene Metasurface-Based Biosensor for Direct Dopamine Detection Utilizing Surface Plasmon Resonance in the Terahertz Regime with Machine Learning Optimization via K-Nearest Neighbors Regression Terahertz radiation ; Biosensor ; Surface plasmon resonance ; Materials science ; Graphene ; Plasmon ; Optoelectronics ; Resonance (particle physics) ; Nanotechnology ; Physics ; Nanoparticle ; Atomic physics Graphene research and applications ; Advanced biosensing and bioanalysis techniques ; 2D Materials and Applications
Authors: Nicolas Bertin | Vasily V. Bulatov | Fei Zhou
Source: npj Computational Materials (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

Learning dislocation dynamics mobility laws from large-scale MD simulations By dispensing with all the atoms and only focusing on dislocation lines, the computational method of Discrete Dislocation Dynamics (DDD) gains greatly over Molecular Dynamics (MD) in simulation efficiency of metal plasticity. But whereas in MD dislocations follow natural dynamics of atomic motion, DDD must rely on a dislocation mobility function to prescribe how a dislocation line should respond to the driving force exerted on it. However, reflecting our still incomplete understanding of ways in which dislocations move, mobility functions presently employed in DDD simulations entail simplifications and approximations of limited or, worse still, unknown accuracy and applicability. Here we introduce a data-driven approach in which the dislocation mobility function is modeled as a graph neural network (GNN) trained on large-scale MD simulations of crystal plasticity. We apply our proposed approach to predicting plastic strength of body-centered-cubic (BCC) metal tungsten and show that, once implemented in a DDD model, our GNN dislocation mobility function accurately reproduces the challenging tension/compression asymmetry of plastic flow observed both in ground-truth MD simulations and in experiment. Furthermore, subsequently validated by MD simulations, the same function accurately predicts plastic response of tungsten under conditions not previously seen in training. By demonstrating its ability to learn relevant physics of dislocation motion, our DDD+ML approach opens a promising avenue to bringing fidelity of DDD models closer in line with direct MD simulations at a much reduced computational cost. Dislocation ; Molecular dynamics ; Plasticity ; Statistical physics ; Tungsten ; Peierls stress ; Function (biology) ; Asymmetry ; Computer science ; Materials science ; Physics ; Condensed matter physics ; Dislocation creep ; Chemistry ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties
Authors: Sheng Ye | Kai Zhong | Yan Huang | Guozhen Zhang | Changyin Sun | Jun Jiang
Source: Journal of the American Chemical Society (article, 2024-01-19)
Field: Chemistry — Spectroscopy

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy. Chemistry ; Hydrogen bond ; Amide ; Molecular dynamics ; Protein dynamics ; Infrared spectroscopy ; Protein secondary structure ; Protein folding ; Folding (DSP implementation) ; Infrared ; Spectroscopy ; Computational chemistry ; Chemical physics ; Molecule ; Organic chemistry ; Biochemistry Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Zhipeng Ding | Wei Su | Yinlong Luo | Lipengan Ye | Wenlong Li | Yuanhang Zhou | Jianfei Zou | Bin Tang | Hongbing Yao
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface inverse designed by deep learning for quasi-entire terahertz wave absorption Ultra-broadband and efficient terahertz (THz) absorption is of paramount importance for the development of high-performance detectors. These detectors find applications in next-generation wireless communications, military radar systems, security detection, medical imaging, and various other domains. In this study, we present an ultra-wideband THz wave metasurface absorber (UTWMA) featuring a composite surface microstructure and a multilayer absorbing material (graphene). This UTWMA demonstrates remarkable capabilities by achieving highly efficient absorption levels, reaching 96.33%, within the 0.5-10 THz frequency range. To enhance the efficiency and precision of the design process, we have incorporated artificial neural networks, which enable rapid and accurate parameter selection. Moreover, we have conducted a comprehensive analysis of the absorption mechanism exhibited by the UTWMA at different frequencies. This analysis combines insights from the electric field distribution and effective medium theory. The findings presented in this paper are expected to catalyze further research in the domain of broadband THz technology, particularly in the context of metasurfaces and related fields. Additionally, this work paves the way for the development of compact, supercontinuous THz photovoltaic or photothermal electrical devices. Terahertz radiation ; Inverse ; Absorption (acoustics) ; Materials science ; Optics ; Optoelectronics ; Physics ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Yiming Chen | Chi Chen | Inhui Hwang | Michael J. Davis | Wanli Yang | Cheng‐Jun Sun | Gi‐Hyeok Lee | Dylan McReynolds | Daniel Allan | Juan Marulanda Arias | Shyue Ping Ong | Maria K. Y. Chan
Source: Chemistry of Materials (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. In this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community. XANES ; X-ray absorption spectroscopy ; Computer science ; Spectroscopy ; Inference ; Artificial intelligence ; Dimensionality reduction ; Absorption (acoustics) ; Spectral line ; Absorption spectroscopy ; Benchmark (surveying) ; Machine learning ; Curse of dimensionality ; Pattern recognition (psychology) ; Biological system ; Materials science ; Physics ; Optics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced X-ray and CT Imaging
Authors: Tianqi Yu | Dong Wang | Min Liu | Wei Lei | Suhaidi Shafie | Mohd Nazim Mohtar | Nattha Jindapetch | Paphavee van Dommelen | Zhiwei Zhao
Source: ACS Materials Letters (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Exploiting Carbon Quantum Dots Synthesized by Electrochemical Exfoliation for Flexible Resistance Switching Wearable nonvolatile memory based on flexible materials has shown an unstoppable development trend in the field of portable artificial intelligence technology. In this work, flexible electronic synapses based on carbon quantum dots (CQDs) synthesized by electrochemical exfoliation are exploited. The CQDs-based devices exhibit similar and stable current–voltage (I–V) characteristics to the initial state after undergoing multiple bending, including I–V curves, retention characteristic for 100 h, endurance characteristic for 106 bending cycles, and classical synaptic functions can be performed faster than the human brain. This is attributed to the extremely small number of defects on the surface of CQDs synthesized by electrochemical exfoliation, which ensures the density and uniformity of the carbon-based film. Interestingly, the artificial neural network model based on the Crosimm platform is used to iteratively train and recognize Modified National Institute of Standards and Technology (MNIST) handwriting, and the simulation results are between 94.2% and 94.9%, further verifying the reliability of silver (Ag)/CQDs/indium tin oxide (ITO)-based devices. Therefore, a reliable decimal arithmetic based on synaptic weight changes is proposed, and addition, multiplication, subtraction, and division can all be performed by applying high-speed pulses. This provides a path for the development of a new generation of high-speed, multifunctional memories. Quantum dot ; Exfoliation joint ; Electrochemistry ; Materials science ; Carbon quantum dots ; Nanotechnology ; Carbon fibers ; Optoelectronics ; Electrode ; Composite material ; Chemistry ; Graphene ; Composite number Carbon and Quantum Dots Applications ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Vital Fernández | R. Amorín | V. Firpo | C. Morisset
Source: Astronomy and Astrophysics (preprint, 2024-06-17)
Field: Chemistry — Spectroscopy

LIME: A LIne MEasuring library for large and complex spectroscopic data sets Context . The upcoming generation of telescopes, instruments, and surveys is poised to usher in an unprecedented “Big Data” era in the field of astronomy. Within this context, even seemingly modest tasks such as spectral line analyses could become increasingly challenging for astronomers. Aims . In this paper, we announce the release of L I M E . This package is tailored for multidisciplinary observations with long-slit and integral field spectroscopy (IFS) support. L I M E functions encompass the reading of observational files, detecting lines, conditioned line fitting, and the plotting and storage of results. Most importantly, these measurements are structured to support the subsequent chemical and kinematic analyses. Methods . To reduce the coding effort required from users, we introduced a notation system for atomic transitions that is accessible to humans and machine-readable. Along with this system, we present an extensive database of line bands, spanning from the ultraviolet to the infrared wavelength range. Additionally, we propose a model designed to train machine learning algorithms in line detection. L I M E features a comprehensive online documentation, which details the command attributes and includes several tutorials. These tutorials range from measuring a single line to analyzing an entire IFS data cube. Results . This library functions and measurements are showcased in an online virtual observatory. The data in this interactive website come from the JWST NIRSpec observations of the CEERs survey. In this regard, L I M E offers improvements related to the dissemination and accessibility of astronomical spectra. Virtual observatory ; Context (archaeology) ; Computer science ; Observatory ; Physics ; Documentation ; Line (geometry) ; Information retrieval ; Astronomy ; Geography ; Operating system Spectroscopy and Laser Applications ; Spectroscopy and Chemometric Analyses ; SAS software applications and methods
Authors: Hong Xu | Liberty L. Mguni | Yali Yao | Diane Hildebrandt | Linda L. Jewell | Xinying Liu
Source: Journal of Cleaner Production (article, 2024-05-20)
Field: Chemistry — Inorganic Chemistry

Machine learning-assisted high-throughput screening of MOFs for efficient adsorption and separation of CF4/N2 Throughput ; Separation (statistics) ; Adsorption ; High-throughput screening ; Nanotechnology ; Materials science ; Process engineering ; Computer science ; Chemistry ; Engineering ; Machine learning ; Telecommunications ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Inorganic Fluorides and Related Compounds
Authors: Jiawang Hu | Nanlin Hu | Donglei Pan | Yan Zhu | Xuan Jin | Shikai Wu | Yuan Lu
Source: Biosensors and Bioelectronics (article, 2024-07-10)
Field: Materials Science — Materials Chemistry

Smell cancer by machine learning-assisted peptide/MXene bioelectronic array Biosensor ; Odor ; Cancer ; Digestive tract ; Cancer detection ; Nanotechnology ; Gas phase ; Computer science ; Computational biology ; Materials science ; Chemistry ; Biology ; Medicine ; Internal medicine ; Organic chemistry MXene and MAX Phase Materials ; Advanced Chemical Sensor Technologies ; Polydiacetylene-based materials and applications
Authors: Yalan Mo | Xiaohong Guan | Shaobin Wang | Xiaoguang Duan
Source: Chemical Science (review, 2024-12-27)
Field: Materials Science — Materials Chemistry

Oriented catalysis through chaos: high-entropy spinels in heterogeneous reactions High-entropy spinel (HES) compounds, as a typical class of high-entropy materials (HEMs), represent a novel frontier in the search for next-generation catalysts. Their unique blend of high entropy, compositional diversity, and structural complexity offers unprecedented opportunities to tailor catalyst properties for enhanced performance (<i>i.e.</i>, activity, selectivity, and stability) in heterogeneous reactions. However, there is a gap in a critical review of the catalytic applications of HESs, especially focusing on an in-depth discussion of the structure-property-performance relationships. Therefore, this review aims to provide a comprehensive overview of the development of HESs in catalysis, including definition, structural features, synthesis, characterization, and catalytic regimes. The relationships between the unique structure, favorable properties, and improved performance of HES-driven catalysis are highlighted. Finally, an outlook is presented which provides guidance for unveiling the complexities of HESs and advancing the field toward the rational design of efficient energy and environmental materials. Catalysis ; CHAOS (operating system) ; Statistical physics ; Entropy (arrow of time) ; Chemistry ; Thermodynamics ; Computer science ; Physics ; Computer security ; Organic chemistry Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides ; nanoparticles nucleation surface interactions
Authors: Łukasz Łach | Dmytro Svyetlichnyy
Source: Energies (article, 2024-07-03)
Field: Materials Science — Materials Chemistry

Recent Progress in Heat and Mass Transfer Modeling for Chemical Vapor Deposition Processes Chemical vapor deposition (CVD) is a vital process for deposit of thin films of various materials with precise control over the thickness, composition, and properties. Understanding the mechanisms of heat and mass transfer during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling. Mass transfer ; Chemical vapor deposition ; Heat transfer ; Materials science ; Chemical engineering ; Chemistry ; Environmental science ; Process engineering ; Environmental chemistry ; Thermodynamics ; Nanotechnology ; Physics ; Engineering Catalytic Processes in Materials Science ; Gas Dynamics and Kinetic Theory ; Electrohydrodynamics and Fluid Dynamics