jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prince Jain | Himanshu Chhabra | Urvashi Chauhan | Krishna Prakash | Piyush Samant | Dhiraj Kumar Singh | Mohamed S. Soliman | Mohammad Tariqul Islam
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Techniques for Predicting Metamaterial Microwave Absorption Performance: A Comparison This work presents a metamaterial absorber (MMA) for X- and Ku-bands with a metallic resonating patch on top and a ground plane separated by substrate FR-4 with a thickness of 0.053 λ at the lowest resonance frequency. The proposed MMA demonstrates perfect absorption of 99.42, 98.48, 98.92, and 99.34 % at 9.948, 13.26, 14.92, and 15.80 GHz, respectively at normal incidence. The proposed MMA demonstrates perfect absorption for a polarization and incident angle over a wide range of angles up to 45°. To understand the fundamental EM behavior of the metamaterial structure, equivalent circuit analysis was carried out, and the circuit outputs accorded with the simulation results. This article also compares various machine learning (ML) methods for optimizing the design and predictive modeling of MMAs, such as decision trees, K-nearest neighbors, random forests, extra trees (ET), bagging, LightGBM, XGBoost, hist gradient boosting, cat boost, and gradient boosting regressors. The primary objective is to assess the usefulness of each regressor technique in estimating the performance of MMAs using multiple tests ranging from TC-40 to TC-80 and performance metrics such as adjusted R-squared score, MSE, RMSE, and MAE, in which the ET regressor excels. Simulation results suggest that ML-based techniques can save simulation resources and time while still being an efficient tool for predicting absorber behavior at intermediate and subsequent frequencies. Microwave ; Metamaterial ; Absorption (acoustics) ; Computer science ; Materials science ; Optoelectronics ; Telecommunications ; Composite material Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Dan Li | Danni Dai | Gege Xiong | Shuquan Lan | Chao Zhang
Source: Advanced Science (review, 2023-04-25)
Field: Materials Science — Biomaterials

Composite Nanocoatings of Biomedical Magnesium Alloy Implants: Advantages, Mechanisms, and Design Strategies The rapid degradation of magnesium (Mg) alloy implants erodes mechanical performance and interfacial bioactivity, thereby limiting their clinical utility. Surface modification is among the solutions to improve corrosion resistance and bioefficacy of Mg alloys. Novel composite coatings that incorporate nanostructures create new opportunities for their expanded use. Particle size dominance and impermeability may increase corrosion resistance and thereby prolong implant service time. Nanoparticles with specific biological effects may be released into the peri-implant microenvironment during the degradation of coatings to promote healing. Composite nanocoatings provide nanoscale surfaces to promote cell adhesion and proliferation. Nanoparticles may activate cellular signaling pathways, while those with porous or core-shell structures may carry antibacterial or immunomodulatory drugs. Composite nanocoatings may promote vascular reendothelialization and osteogenesis, attenuate inflammation, and inhibit bacterial growth, thus increasing their applicability in complex clinical microenvironments such as those of atherosclerosis and open fractures. This review combines the physicochemical properties and biological efficiency of Mg-based alloy biomedical implants to summarize the advantages of composite nanocoatings, analyzes their mechanisms of action, and proposes design and construction strategies, with the purpose of providing a reference for promoting the clinical application of Mg alloy implants and to further the design of nanocoatings. Materials science ; Composite number ; Corrosion ; Nanotechnology ; Alloy ; Magnesium alloy ; Limiting ; Metallurgy ; Composite material ; Mechanical engineering Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Orthopaedic implants and arthroplasty
Authors: Gisle Øye | Sébastien Simon | Turid Rustad | Kristofer Paso
Source: Current Opinion in Food Science (article, 2023-01-31)
Field: Materials Science — Materials Chemistry

Trends in food emulsion technology: Pickering, nano-, and double emulsions Natural and industrial foods are often structured materials that impart texture, flavor, visual appearance, and nutritional value to the product, such as liquid–liquid emulsions stabilized by complex interfaces. This article discusses emerging trends in food emulsion technology considering modern clean label, health, and sustainability goals. Recent scientific research has focused on Pickering emulsions, nanoemulsions, and double emulsions. Tailoring interfacial properties is essential for ensured stability of these systems. Application of green ingredients such as nanocellulose and other biopolymers is increasing in prevalence. However, interfacial characterization is specialized, expensive, and time-consuming. Microfluidic and imaging-coupled artificial intelligence methods are proposed to simplify and accelerate the characterization to food products, while nuclear magnetic resonance spectroscopy (NMR) is proposed to study the structure of multiple emulsions, facilitating the ongoing shift from largely phenomenological food emulsion approaches to interfacial engineering approaches. Pickering emulsion ; Emulsion ; Characterization (materials science) ; Materials science ; Nanotechnology ; Nanocellulose ; Process engineering ; Chemical engineering ; Engineering ; Cellulose Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Advanced Cellulose Research Studies
Authors: Mohit Saraf | Prateek Prateek | Rahul Ranjan | B. Bhuvaneshwari | Vijay Kumar Thakur | Raju Kumar Gupta
Source: Advanced Materials Interfaces (article, 2023-12-24)
Field: Materials Science — Surfaces, Coatings and Films

Polydopamine‐Enabled Biomimetic Surface Engineering of Materials: New Insights and Promising Applications Abstract Surface modification is an important approach to modify the properties of materials. Numerous approaches have been adopted to tailor the properties of such materials, which have been proven successful at many scales and parameters. However, most of these techniques are often tedious, poorly adhesive, costly, sometimes hazardous, and surface‐specific, hence cannot be extended on a large scale and all kinds of surfaces. These shortcomings have led to the emergence of new dopamine (DA) based green surface modification technique where a thin polydopamine (PDA) layer is deposited on surfaces through a facile polymerization of DA under alkaline conditions to enable the surface for various applications. This surface modification strategy has several advantages over other techniques in deposition processing under mild conditions, cost‐effective and straightforward ingredients, and applicability to all kinds of surfaces regardless of their sizes, shapes, and types. Moreover, the PDA layer enhances the surface functionality. Therefore, it can serve as a versatile platform for various secondary reactions for a wide range of applications. Herein, the chemistry of DA is summarized and its polymerized form PDA for the modification of different families of materials’ surfaces with an emphasis on energy, environmental and biological applications. Materials science ; Nanotechnology ; Surface modification ; Surface engineering ; Polymerization ; Layer (electronics) ; Deposition (geology) ; Polymer ; Chemical engineering ; Composite material ; Engineering Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials ; Quantum Dots Synthesis And Properties
Authors: Li Xie | Ming Chen | Qingfeng Song | Chao Wang | Jincheng Liao | Lei Wang | Chenxi Zhu | Fangfang Xu | Yi‐Yang Sun | Shengqiang Bai | Lidong Chen
Source: Science Advances (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Lead-free and scalable GeTe-based thermoelectric module with an efficiency of 12% GeTe-based materials with superior thermoelectric properties promise great potential for waste heat recovery. However, the lack of appropriate diffusion barrier materials (DBMs) limits not only the energy conversion efficiency but also the service reliability of the thermoelectric devices. Here, we propose a design strategy based on phase equilibria diagrams from first-principles calculations and identify transition metal germanides (e.g., NiGe and FeGe<sub>2</sub>) as the DBMs. Our validation experiment confirms the excellent chemical and mechanical stabilities of the interfaces between the germanides and GeTe. We also develop a process for scaling up the GeTe production. Combining with module geometry optimization, we fabricate an eight-pair module using mass-produced p-type Ge<sub>0.89</sub>Cu<sub>0.06</sub>Sb<sub>0.08</sub>Te and n-type Yb<sub>0.3</sub>Co<sub>4</sub>Sb<sub>12</sub> and achieve a record-high efficiency of 12% among all reported single-stage thermoelectric modules. Our work thus paves the way for waste heat recovery based on completely lead-free thermoelectric technology. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Scalability ; Thermoelectric generator ; Work (physics) ; Scaling ; Optoelectronics ; Process engineering ; Engineering physics ; Computer science ; Thermodynamics ; Database ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Phase-change materials and chalcogenides
Authors: Juanjuan Xue | Yao Wang | Guo‐Ping Yang | Yao‐Yu Wang
Source: Journal of Rare Earths (article, 2023-02-21)
Field: Chemistry — Inorganic Chemistry

Energy transfer, anticounterfeiting, white light emission and sensing in fine-regulating series of lanthanide metal-organic frameworks Lanthanide ; Bimetallic strip ; Isostructural ; Materials science ; Metal-organic framework ; Biphenyl ; Phosphor ; Photochemistry ; Metal ; Chemistry ; Ion ; Physical chemistry ; Optoelectronics ; Crystallography ; Organic chemistry ; Adsorption ; Crystal structure Metal-Organic Frameworks: Synthesis and Applications ; Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes
Authors: Benxiang Zhang | Jiayan He | Yang Gao | Laura S. Levy | Martins S. Oderinde | Maximilian D. Palkowitz | T. G. Murali Dhar | Michael D. Mandler | Michael R. Collins | Daniel C. Schmitt | Philippe N. Bolduc | TeYu Chen | Sebastian Clementson | Nadia Nasser Petersen | Gabriele Laudadio | Cheng Bi | Yu Kawamata | Phil S. Baran
Source: Nature (article, 2023-10-03)
Field: Chemistry — Organic Chemistry

Complex molecule synthesis by electrocatalytic decarboxylative cross-coupling Retrosynthetic analysis ; Chemoselectivity ; Nucleophile ; Combinatorial chemistry ; Molecule ; Electrophile ; Chemistry ; Reactivity (psychology) ; Functional group ; Organic chemistry ; Catalysis ; Total synthesis ; Polymer Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Esther Heid | Charles J. McGill | Florence H. Vermeire | William H. Green
Source: Journal of Chemical Information and Modeling (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

Characterizing Uncertainty in Machine Learning for Chemistry Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further dividing epistemic uncertainty into model bias and variance contributions. We systematically address the influence of noise, model bias, and model variance in the context of chemical property predictions, where the diverse nature of target properties and the vast chemical chemical space give rise to many different distinct sources of prediction error. We demonstrate that different sources of error can each be significant in different contexts and must be individually addressed during model development. Through controlled experiments on data sets of molecular properties, we show important trends in model performance associated with the level of noise in the data set, size of the data set, model architecture, molecule representation, ensemble size, and data set splitting. In particular, we show that 1) noise in the test set can limit a model's observed performance when the actual performance is much better, 2) using size-extensive model aggregation structures is crucial for extensive property prediction, and 3) ensembling is a reliable tool for uncertainty quantification and improvement specifically for the contribution of model variance. We develop general guidelines on how to improve an underperforming model when falling into different uncertainty contexts. Computer science ; Robustness (evolution) ; Variance (accounting) ; Uncertainty quantification ; Machine learning ; Context (archaeology) ; Noise (video) ; Artificial intelligence ; Representation (politics) ; Property (philosophy) ; Ensemble learning ; Set (abstract data type) ; Synthetic data ; Data mining ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Cheng Yan | Guoqiang Li
Source: Advanced Intelligent Systems (article, 2023-01-31)
Field: Materials Science — Materials Chemistry

The Rise of Machine Learning in Polymer Discovery In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field. Business process discovery ; Process (computing) ; Field (mathematics) ; Computer science ; Selection (genetic algorithm) ; Data science ; Service discovery ; Artificial intelligence ; Machine learning ; Engineering ; Work in process ; World Wide Web ; Web service ; Mathematics ; Operations management Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Yanzhou Wang | Zheyong Fan | Ping Qian | A. Miguel | Tapio Ala-Nissilä
Source: Physical review. B./Physical review. B (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of temperatures and sample sizes is still a challenge. Herein we present a systematic investigation of the thermal transport properties of a-Si by employing large-scale molecular dynamics (MD) simulations with an accurate and efficient machine learned neuroevolution potential (NEP) trained against abundant reference data calculated at the quantum-mechanical density-functional-theory level. The high efficiency of NEP allows us to study the effects of finite size and quenching rate in the formation of a-Si in great detail. We find that a simulation cell up to $64\phantom{\rule{0.16em}{0ex}}000$ atoms (a cubic cell with a linear size of 11 nm) and a quenching rate down to ${10}^{11}$ K ${\mathrm{s}}^{\ensuremath{-}1}$ are required for almost convergent thermal conductivity. Structural properties, including short- and medium-range order as characterized by the pair-correlation function, angular-distribution function, coordination number, ring statistics, and structure factor are studied to demonstrate the accuracy of NEP and to further evaluate the role of quenching rate. Using both the heterogeneous and homogeneous nonequilibrium MD methods and the related spectral decomposition techniques, we calculate the temperature- and thickness-dependent thermal conductivity values of a-Si and show that they agree well with available experimental results from 10 K to room temperature. Our results also highlight the importance of quantum effects in the calculated thermal conductivity and support the quantum-correction method based on the spectral thermal conductivity. Thermal conductivity ; Quenching (fluorescence) ; Materials science ; Amorphous solid ; Molecular dynamics ; Amorphous silicon ; Atmospheric temperature range ; Silicon ; Condensed matter physics ; Thermodynamics ; Physics ; Chemistry ; Optics ; Crystallography ; Crystalline silicon ; Composite material ; Quantum mechanics ; Optoelectronics Thermal properties of materials ; Machine Learning in Materials Science ; Thin-Film Transistor Technologies
Authors: Gino Cassella | Halvard Sutterud | Sam Azadi | N. D. Drummond | David Pfau | James S. Spencer | W. M. C. Foulkes
Source: Physical Review Letters (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Discovering Quantum Phase Transitions with Fermionic Neural Networks Deep neural networks have been very successful as highly accurate wave function Ansätze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such Ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density, despite being given no a priori knowledge that a phase transition exists. Quantum Monte Carlo ; Diffusion Monte Carlo ; Ansatz ; Ground state ; Physics ; Wave function ; Wigner crystal ; Delocalized electron ; Fermi gas ; Variational Monte Carlo ; Monte Carlo method ; Quantum phase transition ; Statistical physics ; Condensed matter physics ; Electron ; Phase transition ; Quantum mechanics ; Monte Carlo molecular modeling ; Hubbard model Machine Learning in Materials Science ; Quantum many-body systems ; Neural Networks and Applications
Authors: Yunchi Zhou | Ying Wang | Willie J.G.M. Peijnenburg | Martina G. Vijver | Surendra Balraadjsing | Wenhong Fan
Source: Environmental Science & Technology (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an <i>in silico</i> model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs. Ecotoxicity ; Aquatic toxicology ; Aquatic ecosystem ; Random forest ; Environmental science ; Machine learning ; Ecotoxicology ; Predictive modelling ; Biochemical engineering ; Computer science ; Artificial intelligence ; Biological system ; Ecology ; Toxicity ; Biology ; Chemistry ; Engineering Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications
Authors: Ruben Goeminne | Louis Vanduyfhuys | Véronique Van Speybroeck | Toon Verstraelen
Source: Journal of Chemical Theory and Computation (article, 2023-08-29)
Field: Chemistry — Inorganic Chemistry

DFT-Quality Adsorption Simulations in Metal–Organic Frameworks Enabled by Machine Learning Potentials Nanoporous materials such as metal-organic frameworks (MOFs) have been extensively studied for their potential for adsorption and separation applications. In this respect, grand canonical Monte Carlo (GCMC) simulations have become a well-established tool for computational screenings of the adsorption properties of large sets of MOFs. However, their reliance on empirical force field potentials has limited the accuracy with which this tool can be applied to MOFs with challenging chemical environments such as open-metal sites. On the other hand, density-functional theory (DFT) is too computationally demanding to be routinely employed in GCMC simulations due to the excessive number of required function evaluations. Therefore, we propose in this paper a protocol for training machine learning potentials (MLPs) on a limited set of DFT intermolecular interaction energies (and forces) of CO<sub>2</sub> in ZIF-8 and the open-metal site containing Mg-MOF-74, and use the MLPs to derive adsorption isotherms from first principles. We make use of the equivariant NequIP model which has demonstrated excellent data efficiency, and as such an error on the interaction energies below 0.2 kJ mol<sup>-1</sup> per adsorbate in ZIF-8 was attained. Its use in GCMC simulations results in highly accurate adsorption isotherms and heats of adsorption. For Mg-MOF-74, a large dependence of the obtained results on the used dispersion correction was observed, where PBE-MBD performs the best. Lastly, to test the transferability of the MLP trained on ZIF-8, it was applied to ZIF-3, ZIF-4, and ZIF-6, which resulted in large deviations in the predicted adsorption isotherms and heats of adsorption. Only when explicitly training on data for all ZIFs, accurate adsorption properties were obtained. As the proposed methodology is widely applicable to guest adsorption in nanoporous materials, it opens up the possibility for training general-purpose MLPs to perform highly accurate investigations of guest adsorption. Nanoporous ; Adsorption ; Density functional theory ; Metal-organic framework ; Force field (fiction) ; Materials science ; Computer science ; Intermolecular force ; Dispersion (optics) ; Molecule ; Computational chemistry ; Thermodynamics ; Chemistry ; Nanotechnology ; Physical chemistry ; Physics ; Artificial intelligence ; Quantum mechanics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography