jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhenfei Guo | Ruixiang Bai | Zhenkun Lei | Hao Jiang | Da Liu | Jianchao Zou | Cheng Yan
Source: European Journal of Mechanics - A/Solids (article, 2021-05-29)
Field: Materials Science — Materials Chemistry

CPINet: Parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM Constitutive equation ; Artificial intelligence ; Noise reduction ; Convolutional neural network ; Computer science ; Artificial neural network ; Pattern recognition (psychology) ; Algorithm ; Finite element method ; Engineering ; Structural engineering Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals ; Ultrasonics and Acoustic Wave Propagation
Authors: Defang Tu | Jianqi Yan | Yunbo Xie | Jun Li | Shuo Feng | Mingxu Xia | Jianguo Li | Alex Po Leung
Source: Journal of Material Science and Technology (article, 2021-06-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerated design for magnetocaloric performance in Mn-Fe-P-Si compounds using machine learning Magnetic refrigeration ; Materials science ; Curie temperature ; Artificial neural network ; Machine learning ; Thermodynamics ; Magnetic field ; Artificial intelligence ; Metallurgy ; Computer science ; Condensed matter physics ; Magnetization ; Ferromagnetism ; Physics Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; High Entropy Alloys Studies
Authors: Juan C. Verduzco | Ernesto E. Marinero | Alejandro Strachan
Source: Integrating materials and manufacturing innovation (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

An Active Learning Approach for the Design of Doped LLZO Ceramic Garnets for Battery Applications Ionic conductivity ; Materials science ; Ceramic ; Electrolyte ; Conductivity ; Battery (electricity) ; Ionic bonding ; Computer science ; Lithium (medication) ; Fast ion conductor ; Electrical conductor ; Nanotechnology ; Zirconium ; Engineering physics ; Process engineering ; Ion ; Chemistry ; Engineering ; Physics ; Metallurgy ; Thermodynamics ; Composite material ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Solid Oxide Fuel Cells
Authors: Anna Lucia de Marco | Davide Bochicchio | Andrea Gardin | Giovanni Doni | Giovanni M. Pavan
Source: ACS Nano (article, 2021-09-02)
Field: Materials Science — Biomaterials

Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects Supramolecular fibers composed of monomers that self-assemble directionally <i>via</i> noncovalent interactions are ubiquitous in nature, and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a nontrivial challenge. Here, we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional <i>vs.</i> nondirectional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or from all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies. Supramolecular chemistry ; Supramolecular polymers ; Monomer ; Non-covalent interactions ; Nanotechnology ; Polymer ; Delocalized electron ; Chemical physics ; Chemistry ; Materials science ; Molecule ; Hydrogen bond ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polymer composites and self-healing ; Supramolecular Chemistry and Complexes
Authors: Heather J. Kulik | Matthew S. Sigman
Source: Accounts of Chemical Research (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Advancing Discovery in Chemistry with Artificial Intelligence: From Reaction Outcomes to New Materials and Catalysts ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAdvancing Discovery in Chemistry with Artificial Intelligence: From Reaction Outcomes to New Materials and CatalystsHeather J. Kulik*Heather J. KulikMassachusetts Institute of TechnologyMore by Heather J. Kulikhttp://orcid.org/0000-0001-9342-0191 and Matthew S. SigmanMatthew S. SigmanUniversity of UtahMore by Matthew S. Sigmanhttp://orcid.org/0000-0002-5746-8830Cite this: Acc. Chem. Res. 2021, 54, 10, 2335–2336Publication Date (Web):May 18, 2021Publication History Published online18 May 2021Published inissue 18 May 2021https://pubs.acs.org/doi/10.1021/acs.accounts.1c00232https://doi.org/10.1021/acs.accounts.1c00232editorialACS PublicationsCopyright © Published 2021 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views6593Altmetric-Citations16LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (614 KB) Get e-AlertscloseSUBJECTS:Catalysts,Computational modeling,Machine learning,Materials,Molecules Get e-Alerts Citation ; Social media ; Altmetrics ; Computer science ; Chemistry ; Library science ; World Wide Web ; Information retrieval ; Artificial intelligence Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Chemistry and Chemical Engineering
Authors: Junwei Zhao | Wei Xu | Zhengfei Kuang | Rui Long | Zhichun Liu | Wei Liu
Source: Energy Conversion and Management (article, 2021-09-21)
Field: Materials Science — Materials Chemistry

Segmental material design in thermoelectric devices to boost heat-to-electricity performance Energy conversion efficiency ; Thermoelectric generator ; Voltage ; Electricity ; Electric power ; Efficient energy use ; Power (physics) ; Maximum power principle ; Computer science ; Energy transformation ; Optimal design ; Materials science ; Energy (signal processing) ; Thermoelectric effect ; Automotive engineering ; Electrical engineering ; Engineering ; Optoelectronics ; Mathematics ; Physics ; Statistics ; Machine learning ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Thermal properties of materials
Authors: Francesca Venturini
Source: MDPI (MDPI AG) (article, 2021-05-06)
Field: Chemistry — Analytical Chemistry

Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis. Olive oil ; Organoleptic ; Quality (philosophy) ; Process engineering ; Computer science ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Environmental science ; Pulp and paper industry ; Agricultural engineering ; Food science ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Mas Ira Syafila Mohd Hilmi Tan | Mohd Faizal Jamlos | Ahmad Omar | Fatimah Dzaharudin | Suramate Chalermwisutkul | Prayoot Akkaraekthalin
Source: Sensors (review, 2021-04-27)
Field: Chemistry — Analytical Chemistry

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future. Palm oil ; Biotechnology ; Computer science ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Essential Oils and Antimicrobial Activity
Authors: Yu Pang | Puqing Jiang | Ronggui Yang
Source: Journal of Applied Physics (article, 2021-08-23)
Field: Materials Science — Materials Chemistry

Machine learning-based data processing technique for time-domain thermoreflectance (TDTR) measurements Machine learning (ML) has emerged as an increasingly important research tool and has shown great potential for efficient and high-throughput experimental data processing. Meanwhile, ultrafast laser-based time-domain thermoreflectance (TDTR) has been developed into a powerful thermal characterization technique and has been widely applied to measure thermal properties of both bulk and thin-film materials. In this work, artificial neural network-based ML models have been trained for data processing in TDTR experiments. One generally applicable ML model could be trained to process the experimental data of different samples measured using different modulation frequencies and laser spot sizes. Our results suggest that ML is not only fast and efficient in data processing but also accurate and powerful, capable of detecting minute features in the experimental signals and thus enabling extraction of multiple (three or more) parameters simultaneously from the experimental data. The ML model also enables high-speed estimation of the uncertainties of multiple parameters using the Monte Carlo method. Computer science ; Experimental data ; Artificial neural network ; Characterization (materials science) ; Process (computing) ; Artificial intelligence ; Throughput ; Data processing ; Thermal ; Monte Carlo method ; Materials science ; Electronic engineering ; Nanotechnology ; Engineering ; Physics Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Advanced Sensor Technologies Research
Authors: G. Lambard | Taisuke Sasaki | Keitaro Sodeyama | Tadakatsu Ohkubo | K. Hono
Source: Scripta Materialia (article, 2021-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of direct extrusion process for Nd-Fe-B magnets using active learning assisted by machine learning and Bayesian optimization Process optimization of permanent magnet is time-consuming as the microstructure that depends on alloy compositions and process parameters must be optimized to achieve high coercivity. Given a raw material of fixed composition, the optimization of process involves the refinement of grains size, the alignment of crystallographic orientation and the formation of intergranular phase. In this paper, we implemented an active learning pipeline assisted by machine learning and Bayesian optimization (ALMLBO) for predicting magnetic properties from process parameters and propose optimum conditions leading to high coercivity and remanence in Nd-Fe-B anisotropic magnets fabricated by direct hot extrusion. ALMLBO allowed us to optimize the process to exhibit high coercivity, μ0Hc–1.7 T, and remanence, μ0Br–1.4 T, simultaneously, resulting in an excellent maximum energy product, (BH)max–380 kJ/m3. We show that an ALMLBO pipeline is an effective tool for optimizing process for Nd-Fe-B anisotropic magnets. Coercivity ; Materials science ; Remanence ; Bayesian optimization ; Magnet ; Pipeline (software) ; Extrusion ; Anisotropy ; Microstructure ; Process optimization ; Metallurgy ; Mechanical engineering ; Machine learning ; Computer science ; Condensed matter physics ; Magnetization ; Magnetic field ; Chemical engineering ; Optics ; Physics Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Superconducting Materials and Applications
Authors: F. Getman | Maksim Makarenko | Arturo Burguete‐Lopez | Andrea Fratalocchi
Source: Light Science & Applications (article, 2021-03-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband vectorial ultrathin optics with experimental efficiency up to 99% in the visible region via universal approximators Integrating conventional optics into compact nanostructured surfaces is the goal of flat optics. Despite the enormous progress in this technology, there are still critical challenges for real-world applications due to the limited operational efficiency in the visible region, on average lower than 60%, which originates from absorption losses in wavelength-thick (≈ 500 nm) structures. Another issue is the realization of on-demand optical components for controlling vectorial light at visible frequencies simultaneously in both reflection and transmission and with a predetermined wavefront shape. In this work, we developed an inverse design approach that allows the realization of highly efficient (up to 99%) ultrathin (down to 50 nm thick) optics for vectorial light control with broadband input-output responses in the visible and near-IR regions with a desired wavefront shape. The approach leverages suitably engineered semiconductor nanostructures, which behave as a neural network that can approximate a user-defined input-output function. Near-unity performance results from the ultrathin nature of these surfaces, which reduces absorption losses to near-negligible values. Experimentally, we discuss polarizing beam splitters, comparing their performance with the best results obtained from both direct and inverse design techniques, and new flat-optics components represented by dichroic mirrors and the basic unit of a flat-optics display that creates full colours by using only two subpixels, overcoming the limitations of conventional LCD/OLED technologies that require three subpixels for each composite colour. Our devices can be manufactured with a complementary metal-oxide-semiconductor (CMOS)-compatible process, making them scalable for mass production at low cost. Optics ; Wavefront ; Dichroic glass ; Broadband ; Optoelectronics ; Materials science ; Photonics ; Beam splitter ; Microlens ; Lens (geology) ; Computer science ; Physics ; Laser Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Optical Coatings and Gratings
Authors: Amir Hajibabaei | Miran Ha | Saeed Pourasad | Junu Kim | Kwang S. Kim
Source: The Journal of Physical Chemistry A (article, 2021-10-18)
Field: Materials Science — Materials Chemistry

Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules. It shows excellent transferability to long alkanes and closely describes the ab inito potential energy surface for polyenes. Simulation of liquid ethane also shows reasonable agreement with experimental reports. This is a promising initiative toward a universal ab initio quality force-field for hydrocarbons and organic molecules. Polyene ; Alkane ; Chemistry ; Computational chemistry ; Density functional theory ; Force field (fiction) ; Intermolecular force ; Molecule ; Ab initio ; Chemical physics ; Physics ; Hydrocarbon ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xiaoxiao Wang | Huixin Qi | Xiaoyong Hu | Zixuan Yu | Shaoqi Ding | Zhuochen Du | Qihuang Gong
Source: Molecules (review, 2021-05-10)
Field: Materials Science — Materials Chemistry

Advances in Photonic Devices Based on Optical Phase-Change Materials Phase-change materials (PCMs) are important photonic materials that have the advantages of a rapid and reversible phase change, a great difference in the optical properties between the crystalline and amorphous states, scalability, and nonvolatility. With the constant development in the PCM platform and integration of multiple material platforms, more and more reconfigurable photonic devices and their dynamic regulation have been theoretically proposed and experimentally demonstrated, showing the great potential of PCMs in integrated photonic chips. Here, we review the recent developments in PCMs and discuss their potential for photonic devices. A universal overview of the mechanism of the phase transition and models of PCMs is presented. PCMs have injected new life into on-chip photonic integrated circuits, which generally contain an optical switch, an optical logical gate, and an optical modulator. Photonic neural networks based on PCMs are another interesting application of PCMs. Finally, the future development prospects and problems that need to be solved are discussed. PCMs are likely to have wide applications in future intelligent photonic systems. Photonics ; Materials science ; Scalability ; Nanotechnology ; Phase change ; Photonic integrated circuit ; Optoelectronics ; Optical switch ; Computer science ; Electronic engineering ; Engineering ; Engineering physics Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Transition Metal Oxide Nanomaterials
Authors: Yang Li | Jianguo Zhu | Yongjian Li | Lihua Zhu
Source: Journal of Magnetism and Magnetic Materials (article, 2021-10-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A hybrid Jiles–Atherton and Preisach model of dynamic magnetic hysteresis based on backpropagation neural networks Eddy current ; Hysteresis ; Backpropagation ; Magnetic hysteresis ; Artificial neural network ; Transformer ; Inductor ; Ferrite core ; Magnetic core ; Materials science ; Magnetic field ; Magnetization ; Physics ; Computer science ; Nuclear magnetic resonance ; Condensed matter physics ; Electromagnetic coil ; Voltage Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: Han Liu | Siqi Xiao | Longwen Tang | Enigma Bao | Emily Li | Caroline Yang | Zhangji Zhao | Gaurav Sant | Morten M. Smedskjær | Lijie Guo | Mathieu Bauchy
Source: Acta Materialia (article, 2021-03-23)
Field: Materials Science — Materials Chemistry

Predicting the early-stage creep dynamics of gels from their static structure by machine learning Creep ; Materials science ; Calcium silicate hydrate ; Molecular dynamics ; Dynamics (music) ; Chemical physics ; Work (physics) ; Colloid ; Mechanics ; Feature (linguistics) ; Thermodynamics ; Composite material ; Statistical physics ; Physics ; Physical chemistry ; Computational chemistry ; Cement ; Chemistry Material Dynamics and Properties ; Hydrocarbon exploration and reservoir analysis ; Soil and Unsaturated Flow
Authors: Ericsson Tetteh Chenebuah | Michel Nganbe | Alain Tchagang
Source: Materials Today Communications (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

Comparative analysis of machine learning approaches on the prediction of the electronic properties of perovskites: A case study of ABX3 and A2BB’X6 Materials science ; Artificial intelligence ; Machine learning ; Computer science Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films