jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wilhelm Kirchgässner | Nikolas Förster | Till Piepenbrock | Oliver Schweins | Oliver Wallscheid
Source: IEEE Transactions on Power Electronics (article, 2024-10-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores The MagNet challenge 2023 called upon competitors to develop data-driven models for the material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferrite cores. The following HARDCORE (H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores) approach shows that a residual convolutional neural network with physics-informed extensions can serve this task efficiently when trained on observational data beforehand. One key element is an intermediate model layer, which first reconstructs the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$BH$</tex-math></inline-formula> curve, and then, estimates the power losses based on the curve's area rendering the proposed topology physically interpretable. In addition, emphasis was placed on expert-based feature engineering and information-rich inputs in order to enable a lean model architecture. A model is trained from scratch for each material, while the topology remains the same. A Pareto-style tradeoff between model size and estimation accuracy is demonstrated, which yields an optimum at as low as 906 parameters and down to below 8% for the average 95th percentile of the relative power loss error across diverse materials. This contribution has won the first place in the performance category of the MagNet challenge 2023, which further highlights the effectiveness of the proposed model. Residual ; Convolutional neural network ; Waveform ; Artificial neural network ; Power (physics) ; Computer science ; Electronic engineering ; Electrical engineering ; Artificial intelligence ; Algorithm ; Physics ; Engineering ; Voltage Magnetic Properties and Applications ; Power Systems Fault Detection
Authors: Hala M. Elshishini | Gehan M. El‐Subruiti | Zekry F. Ghatass | Nourhan H. Farag | Abdelazeem S. Eltaweil
Source: Journal of Water Process Engineering (article, 2024-12-02)
Field: Chemistry — Organic Chemistry

Assessing decision-based machine learning algorithms for predicting adsorption efficiency: a detailed study of Mn Fe LDH functionalized La(OH)₃@AC chitosan beads Chitosan ; Adsorption ; Algorithm ; Chemistry ; Computer science ; Materials science ; Organic chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Extraction and Separation Processes
Authors: Xiaomeng Wang | Tao Yang | Marcus Maeder | Steffen Marburg
Source: Nonlinear Dynamics (article, 2024-08-09)
Field: Materials Science — Polymers and Plastics

A novel fractional calculus modeling and physics-informed machine learning study on dynamic performance of hybrid flax/basalt fiber-reinforced composite Basalt fiber ; Interpretability ; Dynamic modulus ; Generalizability theory ; Nonlinear system ; Fiber ; Artificial neural network ; Computer science ; Fractional calculus ; Viscoelasticity ; Fiber-reinforced composite ; Dynamic mechanical analysis ; Algorithm ; Materials science ; Mathematics ; Machine learning ; Applied mathematics ; Composite material ; Physics ; Polymer Natural Fiber Reinforced Composites ; Vibration Control and Rheological Fluids ; Mechanical Engineering and Vibrations Research
Authors: Maya M. Martirossyan | Matthew Spellings | Hillary Pan | Julia Dshemuchadse
Source: ACS Nano (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Local Structural Features Elucidate Crystallization of Complex Structures Complex crystal structures are composed of multiple local environments, and how this type of order emerges spontaneously during crystal growth has yet to be fully understood. We study crystal growth across various structures and along different crystallization pathways, using self-assembly simulations of identical particles that interact via multiwell isotropic pair potentials. We apply an unsupervised machine learning method to features from bond-orientational order metrics to identify different local motifs present during a given structure's crystallization process. In this manner, we distinguish different crystallographic sites in highly complex structures. Tailoring this order parameter to structures of varying complexity and coordination number, we study the emergence of local order along a multistep crystal growth pathway─from a low-density fluid to a high-density, supercooled amorphous liquid droplet and to a bulk crystal. We find a consistent under-coordination of the liquid relative to the average coordination number in the bulk crystal. We use our order parameter to analyze the geometrically frustrated growth of a Frank-Kasper phase and discover how structural defects compete with the formation of crystallographic sites that are more high-coordinated than the liquid environments. The method presented here for classifying order on a particle-by-particle level has broad applicability to future studies of structural self-assembly and crystal growth, and they can aid in the design of building blocks and for targeting pathways of formation of soft-matter structures. Crystallization ; Materials science ; Nanotechnology ; Crystallography ; Chemistry ; Engineering ; Chemical engineering Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Etinosa Osaro | Yamil J. Colón
Source: AIChE Journal (article, 2024-09-12)
Field: Chemistry — Inorganic Chemistry

Optimizing the prediction of adsorption in metal–organic frameworks leveraging Q‐learning Abstract The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal–organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML has helped survey the vast material space. This study explores the integration of reinforcement learning (RL), specifically Q‐learning, within an active learning (AL) context, combined with Gaussian processes (GPs) for predictive modeling of adsorption in MOFs. We demonstrate the effectiveness of the RL‐driven framework in guiding the selection of training data points and optimizing predictive model performance for methane and carbon dioxide adsorption, using two different reward metrics. Our results highlight the integration of RL as an AL method for adsorption predictions in MFs, and how it compares to a previously implemented AL scheme. Metal-organic framework ; Adsorption ; Computer science ; Process engineering ; Materials science ; Chemistry ; Environmental science ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Zikang Guo | Rui Li | Xianfeng He | Jiang Guo | Shenghong Ju
Source: Materials Genome Engineering Advances (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Harnessing quantum power: Revolutionizing materials design through advanced quantum computation Abstract The design of advanced materials for applications in areas of photovoltaics, energy storage, and structural engineering has made significant strides. However, the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing, fabrication, and characterization. This review introduces a comprehensive methodology for materials design using cutting‐edge quantum computing, with a particular focus on quadratic unconstrained binary optimization (QUBO) and quantum machine learning (QML). We introduce the loop framework for QUBO‐empowered materials design, including constructing high‐quality datasets that capture critical material properties, employing tailored computational methods for precise material modeling, developing advanced figures of merit to evaluate performance metrics, and utilizing quantum optimization algorithms to discover optimal materials. In addition, we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations. The review also highlights advanced active learning strategies that integrate quantum artificial intelligence, offering a more efficient pathway to explore the vast, complex material design space. Finally, we discuss the key challenges and future opportunities for QML in material design, emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations. Quantum computer ; Quantum ; Computation ; Power (physics) ; Computer science ; Physics ; Quantum mechanics ; Programming language Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum-Dot Cellular Automata
Authors: Sheng Sun | Menghuan Wang | Hanqing Jiang | Ying Zhang | Hang Qiao | Tong‐Yi Zhang
Source: Journal of the Mechanics and Physics of Solids (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Optimizing nanoporous metallic actuators through multiscale calculations and machine learning Nanoporous ; Materials science ; Actuator ; Mechanical engineering ; Composite material ; Artificial intelligence ; Computer science ; Nanotechnology ; Engineering Nanoporous metals and alloys ; Anodic Oxide Films and Nanostructures ; Electrocatalysts for Energy Conversion
Authors: Uzair Sajjad | Aqeel Abbas | Imtiyaz Hussain | Muhammad Sultan | Hafız Muhammad Ali | Wei‐Mon Yan
Source: Results in Engineering (article, 2024-02-09)
Field: Materials Science — Biomaterials

Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions Magnesium (Mg) alloys have found potential applications in aeronautical, automotive, 3C industries, and the like owing to their good machinability, high specific strength, and low density. However, one of the main obstacles in impeding the Mg is weak corrosion resistance. Herein, the corrosion behavior of WS2/AZ91 composites and the effect of severe deformation through equal channel angular pressing was investigated experimentally and analytically via three-electrode system in a 3.5 wt% NaCl solution and data driven modelling. The experimental data of the current density and corrosion potentials of different composites at different deformation conditions was first correlated by Pearson, Spearman, and Kendall correlations. After that Bayesian surrogate Gaussian process (GP) assisted optimal neural network was developed to assess the corrosion behavior of different metal matrix composites at different deformation conditions. The correlation matrix showed that for different weight concentrations such as 0 wt %, 0.6 wt %, and 1 wt %, the Pearson correlation value becomes 0.77, 0.64, and 0.7, respectively. Similar to the Pearson correlation, the Kendall and Spearman correlations also showed relatively higher values for 0 wt % and 1 wt % compared to 0.6 wt % concentration. The proposed neural network model expressed a great accuracy in terms of correlation coefficient (R2 = 0.9668), mean absolute error (MAE = 0.0583), mean square error (MSE = 0.0405), and mean absolute percentage error (MAPE = 2.183). Although different concentrations and deformation conditions were included in the data, yet, the proposed DNN model was able to predict the current density data with a great accuracy. Finally, the explainable artificial intelligence was used to interpret the prediction of the developed model for different deformations and composite concentrations. Corrosion ; Materials science ; Composite material ; Deformation (meteorology) Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; MXene and MAX Phase Materials
Authors: Harpriya Minhas | Milan Kumar Jena | Rahul Kumar Sharma | Biswarup Pathak
Source: ACS Applied Electronic Materials (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Inverse Design and Role of Dopant for Tuning Thermoelectric Efficiency The synergy between a data-driven machine learning (ML) strategy and thermoelectrics (TE) has emerged as a powerful approach for accelerating the discovery of materials. In this study, we have trained an ML model using experimental datasets encompassing chalcogenides, skutterudite, and clathrates, at varying temperatures. With the trained ML model, we successfully predicted five crucial TE properties for 390 strategically doped, previously unexplored materials across different temperatures. ML explainability elucidated the impact of strategic doping, including lone-paired electrons, polarizability, and electronegativity, which is crucial for optimizing TE properties. We identified Bi-doped Bi0.1Sb1.9Te3 material as a top-performing candidate, exhibiting high ZT values ranging from 1.74 to 2.20, ultralow κL < 0.40 W m–1 K–1, and a decent power factor across a broad temperature range. Co-doping strategies involving Cd/Sb and Sb/In in GeTe, as well as Na/Cd/Se in PbTe, led to high ZT and κL, enabling the tuning of their electronic band structure to foster an alternative pathway with a high power factor. Leveraging insights from regression analysis, we categorized TE materials based on their performance, particularly emphasizing chalcogenide materials. This fusion of ML and experimental database methodologies presents a promising avenue for the inverse design of high-performance TE materials, achieved through strategic adjustments of dopant and composition ratios. Dopant ; Thermoelectric effect ; Inverse ; Materials science ; Engineering physics ; Mechanical engineering ; Computer science ; Nanotechnology ; Optoelectronics ; Doping ; Physics ; Engineering ; Mathematics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Silvan Käser | Markus Meuwly
Source: The Journal of Physical Chemistry Letters (article, 2024-03-20)
Field: Materials Science — Materials Chemistry

Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces The role of numerical accuracy in training and evaluating neural network-based potential energy surfaces is examined for different experimental observables. For observables that require third- and fourth-order derivatives of the potential energy with respect to Cartesian coordinates single-precision arithmetics as is typically used in ML-based approaches is insufficient and leads to roughness of the underlying PES as is explicitly demonstrated. Increasing the numerical accuracy to double-precision gives a smooth PES with higher-order derivatives that are numerically stable and yield meaningful anharmonic frequencies and tunneling splitting as is demonstrated for H<sub>2</sub>CO and malonaldehyde. For molecular dynamics simulations, which only require first-order derivatives, single-precision arithmetics appears to be sufficient, though. Anharmonicity ; Cartesian coordinate system ; Observable ; Energy (signal processing) ; Potential energy ; Molecular dynamics ; Computer science ; Potential energy surface ; Statistical physics ; Algorithm ; Mathematics ; Physics ; Classical mechanics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Harrison Fursman | Marie Morelato | Scott Chadwick | Florentin Coppey | Pierre Esseiva | Claude Roux | Natasha Stojanovska
Source: Forensic Science International (article, 2024-07-31)
Field: Chemistry — Analytical Chemistry

Development and evaluation of portable NIR technology for the identification and quantification of Australian illicit drugs The efficient and accurate analysis of illicit drugs remains a constant challenge in Australia given the high volume of drugs trafficked into and around the country. Portable drug testing technologies facilitate the decentralisation of the forensic laboratory and enable analytical data to be acted upon more efficiently. Near-infrared (NIR) spectroscopy combined with chemometric modelling (machine learning algorithms) has been highlighted as a portable drug testing technology that is rapid and accurate. However, its effectiveness depends upon a database of chemically relevant specimens that are representative of the market. There are chemical differences between drugs in different countries that need to be incorporated into the database to ensure accurate chemometric model prediction. This study aimed to optimise and assess the implementation of NIR spectroscopy combined with machine learning models to rapidly identify and quantify illicit drugs within an Australian context. The MicroNIR (Viavi Solutions Inc.) was used to scan 608 illicit drug specimens seized by the Australian Federal Police comprising of mainly crystalline methamphetamine hydrochloride (HCl), cocaine HCl, and heroin HCl. A number of other traditional drugs, new psychoactive substances and adulterants were also scanned to assess selectivity. The 3673 NIR scans were compared to the identity and quantification values obtained from a reference laboratory in order to assess the proficiency of the chemometric models. The identification of crystalline methamphetamine HCl, cocaine HCl, and heroin HCl specimens was highly accurate, with accuracy rates of 98.4 %, 97.5 %, and 99.2 %, respectively. The sensitivity of these three drugs was more varied with heroin HCl identification being the least sensitive (methamphetamine = 96.6 %, cocaine = 93.5 % and heroin = 91.3 %). For these three drugs, the NIR technology provided accurate quantification, with 99 % of values falling within the relative uncertainty of ±15 %. The MicroNIR with NIRLAB infrastructure has demonstrated to provide accurate results in real-time with clear operational applications. There is potential to improve informed decision-making, safety, efficiency and effectiveness of frontline and proactive policing within Australia. Context (archaeology) ; Illicit drug ; Drug detection ; Heroin ; Identification (biology) ; Drug ; Computer science ; Medicine ; Pharmacology ; Chromatography ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: P. D. Varuna S. Pathirage | Justin T. Phillips | Konstantinos D. Vogiatzis
Source: The Journal of Physical Chemistry A (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Exploration of the Two-Electron Excitation Space with Data-Driven Coupled Cluster Computational cost limits the applicability of post-Hartree-Fock methods such as coupled-cluster on larger molecular systems. The data-driven coupled-cluster (DDCC) method applies machine learning to predict the coupled-cluster two-electron amplitudes (<i>t</i><sub>2</sub>) using data from second-order perturbation theory (MP2). One major limitation of the DDCC models is the size of training sets that increases exponentially with the system size. Effective sampling of the amplitude space can resolve this issue. Five different amplitude selection techniques that reduce the amount of data used for training were evaluated, an approach that also prevents model overfitting and increases the portability of data-driven coupled-cluster singles and doubles to more complex molecules or larger basis sets. In combination with a localized orbital formalism to predict the CCSD <i>t</i><sub>2</sub> amplitudes, we have achieved a 10-fold error reduction for energy calculations. Cluster (spacecraft) ; Excitation ; Space (punctuation) ; Computer science ; Physics ; Quantum mechanics ; Computer network ; Operating system Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Bo Chen | Dong Xie | Yongjie Jiang | Yankun Wang | Changsheng Su | Zhitong Yao | Di Cai | Hui Cao | Ian Watson
Source: The Science of The Total Environment (article, 2024-12-01)
Field: Materials Science — Polymers and Plastics

Co-pyrolysis of corn stalk and high-density polyethylene with emphasis on the fibrous tissue difference on thermal behavior and kinetics High-density polyethylene ; Pyrolysis ; Stalk ; Polyethylene ; Materials science ; Hemicellulose ; Thermogravimetric analysis ; Husk ; Thermal decomposition ; Chemical engineering ; Composite material ; Cellulose ; Chemistry ; Organic chemistry ; Botany ; Horticulture ; Biology Natural Fiber Reinforced Composites ; Thermochemical Biomass Conversion Processes ; Flame retardant materials and properties
Authors: Ying He | Fang Liu | Weicui Min | Guohong Liu | Yinbao Wu | Yan Wang | Xiliang Yan | Bing Yan
Source: ACS Applied Materials & Interfaces (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

<i>De novo</i> Design of Biocompatible Nanomaterials Using Quasi-SMILES and Recurrent Neural Networks Screening nanomaterials (NMs) with desired properties from the extensive chemical space presents significant challenges. The potential toxicity of NMs further limits their applications in biological systems. Traditional methods struggle with these complexities, but generative models offer a possible solution to producing new molecules without prior knowledge. However, converting complex 3D nanostructures into computer-readable formats remains a critical prerequisite. To overcome these challenges, we proposed an innovative deep-learning framework for the <i>de novo</i> design of biocompatible NMs. This framework comprises two predictive models and a generative model, utilizing a Quasi-SMILES representation to encode three-dimensional structural information on NMs. Our generative model successfully created 289 new NMs not previously seen in the training set. The predictive models identified a particularly promising NM characterized by high cellular uptake and low toxicity. This NM was successfully synthesized, and its predicted properties were experimentally validated. Our approach advances the application of artificial intelligence in NM design and provides a practical solution for balancing functionality and toxicity in NMs. Generative grammar ; Biocompatible material ; Nanomaterials ; Nanotechnology ; Computer science ; Materials science ; Set (abstract data type) ; Artificial neural network ; Artificial intelligence ; Biochemical engineering ; Engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; 3D Printing in Biomedical Research