jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Chen | Qian Gao | Yuan Yuan | Tingyu Li | Qian Xi | Tingting Liu | Aitao Tang | Andy Watson | Fusheng Pan
Source: Journal of Magnesium and Alloys (article, 2021-07-23)
Field: Materials Science — Biomaterials

Coupling physics in machine learning to investigate the solution behavior of binary Mg alloys The solution behavior of a second element in the primary phase (α(Mg)) is important in the design of high-performance alloys. In this work, three sets of features have been collected: a) interaction features of solutes and Mg obtained from first-principles calculation, b) intrinsic physical properties of the pure elements and c) structural features. Based on the maximum solid solubility values, the solution behavior of elements in α(Mg) are classified into four types, e.g., miscible, soluble, sparingly-soluble and slightly-soluble. The machine learning approach, including random forest and decision tree algorithm methods, is performed and it has been found that four features, e.g., formation energy, electronegativity, non-bonded atomic radius, and work function, can together determine the classification of the solution behavior of an element in α(Mg). The mathematical correlations, as well as the physical relationships among the selected features have been analyzed. This model can also be applied to other systems following minor modifications of the defined features, if required. Electronegativity ; Solubility ; Coupling (piping) ; Binary number ; Atomic radius ; Materials science ; RADIUS ; Work (physics) ; Function (biology) ; Thermodynamics ; Phase diagram ; Element (criminal law) ; Energy (signal processing) ; Phase (matter) ; Minor (academic) ; Statistical physics ; Computer science ; Physics ; Physical chemistry ; Mathematics ; Chemistry ; Metallurgy ; Organic chemistry ; Statistics Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Hydrogen Storage and Materials
Authors: Riley Jackson | Wenyuan Zhang | Jason K. Pearson
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

TSNet: predicting transition state structures with tensor field networks and transfer learning Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function. However, transition states are very unstable, typically only existing on the order of femtoseconds. The transient nature of these structures makes them incredibly difficult to study, thus chemists often turn to simulation. Unfortunately, computer simulation of transition states is also challenging, as they are first-order saddle points on highly dimensional mathematical surfaces. Locating these points is resource intensive and unreliable, resulting in methods which can take very long to converge. Machine learning, a relatively novel class of algorithm, has led to radical changes in several fields of computation, including computer vision and natural language processing due to its aptitude for highly accurate function approximation. While machine learning has been widely adopted throughout computational chemistry as a lightweight alternative to costly quantum mechanical calculations, little research has been pursued which utilizes machine learning for transition state structure optimization. In this paper TSNet is presented, a new end-to-end Siamese message-passing neural network based on tensor field networks shown to be capable of predicting transition state geometries. Also presented is a small dataset of S<sub>N</sub>2 reactions which includes transition state structures - the first of its kind built specifically for machine learning. Finally, transfer learning, a low data remedial technique, is explored to understand the viability of pretraining TSNet on widely available chemical data may provide better starting points during training, faster convergence, and lower loss values. Aspects of the new dataset and model shall be discussed in detail, along with motivations and general outlook on the future of machine learning-based transition state prediction. Variety (cybernetics) ; Field (mathematics) ; Transition state ; Chemical physics ; Transition (genetics) ; Tensor (intrinsic definition) ; State (computer science) ; Kinetics ; Function (biology) ; Statistical physics ; Chemistry ; Catalysis ; Computational chemistry ; Materials science ; Computer science ; Physics ; Mathematics ; Artificial intelligence ; Organic chemistry ; Quantum mechanics ; Pure mathematics ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mingren Shen | Guanzhao Li | Dongxia Wu | Yuhan Liu | Jacob R.C. Greaves | Wei Hao | Nathaniel J. Krakauer | Leah Krudy | J. Muñoz Pérez | Varun Sreenivasan | Bryan Sanchez | Oigimer Torres-Velázquez | Wei Li | Kevin G. Field | Dane Morgan
Source: Computational Materials Science (article, 2021-08-02)
Field: Materials Science — Materials Chemistry

Multi defect detection and analysis of electron microscopy images with deep learning Deep learning ; Computer science ; Scalability ; Microscopy ; Artificial intelligence ; Electron microscope ; Pattern recognition (psychology) ; Materials science ; Machine learning ; Optics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques
Authors: Mehrshad Mehrpouya | Annamaria Gisario | Mohammadreza Nematollahi | Atabak Rahimzadeh | Keyvan Safaei Baghbaderani | Mohammad Elahinia
Source: Materials Today Communications (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy NiTi-based alloys are one of the most well-known alloys among shape memory alloys having a wide range of applications from biomedical to aerospace areas. Adding a third element to the binary alloys of NiTi changes the thermomechanical properties of the material remarkably. Two unique features of stability and high transformation temperature have turned NiTiHf as a suitable ternary shape memory alloys in various applications. Selective laser melting (SLM) as a layer-based fabrication method addresses the difficulties and limitations of conventional methods. Process parameters of SLM play a prominent role in the properties of the final parts so that by using the different sets of process parameters, different thermomechanical responses can be achieved. In this study, different sets of process parameters (PPs) including laser power, hatch space, and scanning speed were defined to fabricate the NiTiHf samples. Changing the PPs is a powerful tool for tailoring the thermomechanical response of the fabricated parts such as transformation temperature (TTs), density, and mechanical response. In this work, an artificial neural network (ANN) was developed to achieve a prediction tool for finding the effect of the PPs on the TTs and the size deviation of the printed parts. Materials science ; Shape-memory alloy ; Selective laser melting ; Ternary operation ; Nickel titanium ; Artificial neural network ; Fabrication ; Laser power scaling ; Process (computing) ; Composite material ; Laser ; Computer science ; Microstructure ; Artificial intelligence ; Optics Shape Memory Alloy Transformations
Authors: Majedeh Gheytanzadeh | Alireza Baghban | Sajjad Habibzadeh | Ahmad Mohaddespour | Otman Abida
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Insights into the estimation of capacitance for carbon-based supercapacitors Carbon-based materials are broadly used as the active component of electric double layer capacitors (EDLCs) in energy storage systems with a high power density. Most of the reported computational studies have investigated the electrochemical properties under equilibrium conditions, limiting the direct and practical use of the results to design electrochemical energy systems. In the present study, for the first time, the experimental data from more than 300 published papers have been extracted and then analyzed through an optimized support vector machine (SVM) by a grey wolf optimization (GWO) algorithm to obtain a correlation between carbon-based structural features and EDLC performance. Several structural features, including calculated pore size, specific surface area, N-doping level, <i>I</i> <sub>D</sub>/<i>I</i> <sub>G</sub> ratio, and applied potential window were selected as the input variables to determine their impact on the respective capacitances. Sensitivity analysis, which has only been performed in this study for approximating the EDLC capacitance, indicated that the specific surface area of the carbon-based supercapacitors is of the greatest effect on the corresponding capacitance. The proposed SVM-GWO, with an <i>R</i> <sup>2</sup> value of 0.92, showed more accuracy than all the other proposed machine learning (ML) models employed for this purpose. Supercapacitor ; Capacitance ; Carbon fibers ; Materials science ; Nanotechnology ; Chemistry ; Composite material ; Electrode ; Physical chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Zhiqiang Niu | Valerie J. Pinfield | Billy Wu | Huizhi Wang | Kui Jiao | Dennis Y.C. Leung | Jin Xuan
Source: Energy & Environmental Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design The digital transformation empowered by artificial intelligence will create huge opportunities for the porous energy materials research community. Digital transformation ; Porous medium ; Energy (signal processing) ; Transformation (genetics) ; Porosity ; Materials science ; Business ; Computer science ; World Wide Web ; Physics ; Composite material Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jonathan Amodeo | Laurent Pizzagalli
Source: Comptes Rendus Physique (review, 2021-05-21)
Field: Materials Science — Materials Chemistry

Modeling the mechanical properties of nanoparticles: a review Nanoparticles are commonly used in various fields of applications such as electronics, catalysis or engineering where they can be subjected to a certain amount of stress leading to structural instabilities or irreversible damages. In contrast with bulk materials, nanoparticles can sustain extremely high stresses (in the GPa range) and ductility, even in the case of originally brittle materials. This review article focuses on the modeling of the mechanical properties of nanoparticles, with an emphasis on elementary deformation processes. Various simulation methods are described, from classical molecular dynamics calculations, the best suited method when applied to the modeling the mechanics of nanoparticles, to dislocation dynamics based hybrid methodologies. We detail the mechanical behaviour of nanoparticles for a large array of material classes (metals, semi-conductors, ceramics, etc.), as well as their deformation processes. Regular crystalline nanoparticles are addressed, as well as more complex systems such as nanoporous or core-shell particles. In addition to the exhaustive review on the recent works published on the topic, challenges and future trends are proposed, providing solid foundations for forthcoming investigations. Nanoparticle ; Materials science ; Nanoporous ; Nanotechnology ; Brittleness ; Multiscale modeling ; Ceramic ; Electronics ; Ductility (Earth science) ; Dislocation ; Mechanical engineering ; Composite material ; Creep ; Engineering ; Chemistry Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Satoko Hiura | Shige Koseki | Kento Koyama
Source: Scientific Reports (article, 2021-05-19)
Field: Chemistry — Analytical Chemistry

Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database In predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 °C, were obtained from the ComBase database ( www.combase.cc ). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: 'time', 'temperature', 'pH', 'water activity', 'initial cell counts', 'whether the viable count is initial cell number', and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data. Listeria monocytogenes ; Population ; Listeria ; Food microbiology ; Computer science ; Database ; Biology ; Bacteria ; Medicine ; Environmental health Spectroscopy and Chemometric Analyses ; Artificial Intelligence in Healthcare ; Smart Agriculture and AI
Authors: Elyas Bayati | Raphaël Pestourie | Shane Colburn | Zin Lin | Steven G. Johnson | Arka Majumdar
Source: Nanophotonics (article, 2021-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse designed extended depth of focus meta‐optics for broadband imaging in the visible We report an inverse-designed, high numerical aperture (∼0.44), extended depth of focus (EDOF) meta-optic, which exhibits a lens-like point spread function (PSF). The EDOF meta-optic maintains a focusing efficiency comparable to that of a hyperboloid metalens throughout its depth of focus. Exploiting the extended depth of focus and computational post processing, we demonstrate broadband imaging across the full visible spectrum using a 1 mm, f/1 meta-optic. Unlike other canonical EDOF meta-optics, characterized by phase masks such as a log-asphere or cubic function, our design exhibits a highly invariant PSF across ∼290 nm optical bandwidth, which leads to significantly improved image quality, as quantified by structural similarity metrics. Optics ; Broadband ; Focus (optics) ; Point spread function ; Optical transfer function ; Lens (geology) ; Hyperboloid ; Geometrical optics ; Depth of focus (tectonics) ; Physics ; Geology Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic and Optical Devices
Authors: Jiabing Wang | Dongsheng Sun | Lili Huang | Shijian Wang | Yong Jin
Source: Oxidative Medicine and Cellular Longevity (review, 2021-01-01)
Field: Chemistry — Organic Chemistry

Targeting Reactive Oxygen Species Capacity of Tumor Cells with Repurposed Drug as an Anticancer Therapy Accumulating evidence shows that elevated levels of reactive oxygen species (ROS) are associated with cancer initiation, growth, and response to therapies. As concentrations increase, ROS influence cancer development in a paradoxical way, either triggering tumorigenesis and supporting the proliferation of cancer cells at moderate levels of ROS or causing cancer cell death at high levels of ROS. Thus, ROS can be considered an attractive target for therapy of cancer and two apparently contradictory but virtually complementary therapeutic strategies for the regulation of ROS to treat cancer. Despite tremendous resources being invested in prevention and treatment for cancer, cancer remains a leading cause of human deaths and brings a heavy burden to humans worldwide. Chemotherapy remains the key treatment for cancer therapy, but it produces harmful side effects. Meanwhile, the process of de novo development of new anticancer drugs generally needs increasing cost, long development cycle, and high risk of failure. The use of ROS-based repurposed drugs may be one of the promising ways to overcome current cancer treatment challenges. In this review, we briefly introduce the source and regulation of ROS and then focus on the status of repurposed drugs based on ROS regulation for cancer therapy and propose the challenges and direction of ROS-mediated cancer treatment. Reactive oxygen species ; Drug ; Cancer therapy ; Anticancer drug ; Tumor cells ; Drug discovery ; Chemistry ; Drug repositioning ; Pharmacology ; Cancer research ; Biology ; Medicine ; Cancer ; Biochemistry ; Internal medicine Synthesis and biological activity ; Synthesis and Biological Evaluation ; Free Radicals and Antioxidants
Authors: Guohua Zhao | Xiaoqing Li | Nik Petrinić
Source: npj Computational Materials (article, 2021-06-14)
Field: Materials Science — Materials Chemistry

Materials information and mechanical response of TRIP/TWIP Ti alloys Abstract Materials innovation calls for an integrated framework combining physics-based modelling and data-driven informatics. A dislocation-based constitutive model accounting for both transformation-induced plasticity (TRIP) and twinning-induced plasticity (TWIP) was built to interpret the mechanical characteristics of metastable titanium alloys. Particular attention was placed on quantitatively understanding the composition-sensitive phase stability and its influence on the underlying deformation mechanism. For this purpose, a pseudoelastic force balance incorporating thermodynamics and micromechanics was applied to calculate the energy landscapes of β → α ″ martensitic transformation, {332}〈113〉 twinning and dislocation slip. Extensive material data were probed, computed and fed to the model. Our results revealed that TRIP and TWIP may operate simultaneously because of the presence of a noticeably overlapped energy domain, and confirmed {332}〈113〉 twinning is an energetically favourable deformation mechanism. The model validation further unveiled that the activation of β → α ″ transition remarkably enhances the strain-hardening and plasticity, even though the dynamically formed α ″ volume fraction is much less than that of deformation twinning. Our work suggests that the synchronised physical metallurgy and data-driven strategy allows to identify the compositional scenarios for developing high-performance engineering alloys. Twip ; Crystal twinning ; Materials science ; Micromechanics ; Plasticity ; Deformation mechanism ; Diffusionless transformation ; Deformation (meteorology) ; Slip (aerodynamics) ; Dislocation ; Metallurgy ; Martensite ; Thermodynamics ; Composite material ; Microstructure ; Physics Titanium Alloys Microstructure and Properties ; Microstructure and mechanical properties ; Metal and Thin Film Mechanics
Authors: Guanghan Huang | Xin Wei | Yuheng Gu | Zhanxiao Kang | Lihong Lao | Li Li | Jintu Fan | Dahua Shou
Source: Cell Reports Physical Science (article, 2021-12-31)
Field: Materials Science — Surfaces, Coatings and Films

Heterogeneously engineered porous media for directional and asymmetric liquid transport Passive regulation of liquid transport in porous media in a directional or asymmetric manner has become increasingly critical to emerging applications, such as personal moisture management, water harvesting, and liquid separation. Over the past decade, heterogeneously engineering porous materials and structures for tunable liquid transport has triggered technological revolutions in those areas based on nature-inspired design, metamaterial development, and model-driven optimization. Herein, we discuss the latest developments in directional and asymmetric liquid movement created by material and structural heterogeneity, with a focus on mechanistic models, physical mechanisms, and engineering strategies to provide an improved understanding of the controllability of the directed liquid motion. We also explore the diverse applications of enhanced fluid directionality and asymmetry, from overviewing fabrication methods to analyzing significant affecting factors, including surface wettability, pore size, and flow path profile. Current challenges and research gaps are summarized to provide a road map for potential research opportunities. Controllability ; Porous medium ; Nanotechnology ; Materials science ; Wetting ; Metamaterial ; Porosity ; Computer science ; Composite material Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Fluid Dynamics Simulations and Interactions
Authors: Bogumiła Kumanek | Grzegorz Stando | Paweł Stando | Karolina Matuszek | Karolina Z. Milowska | Maciej Krzywiecki | Marta Gryglas-Borysiewicz | Zuzanna Ogorzałek | M. C. Payne | Douglas R. MacFarlane | Dawid Janas
Source: Scientific Reports (article, 2021-04-21)
Field: Materials Science — Materials Chemistry

Enhancing thermoelectric properties of single-walled carbon nanotubes using halide compounds at room temperature and above Carbon nanotubes (CNTs) are materials with exceptional electrical, thermal, mechanical, and optical properties. Ever since it was demonstrated that they also possess interesting thermoelectric properties, they have been considered a promising solution for thermal energy harvesting. In this study, we present a simple method to enhance their performance. For this purpose, thin films obtained from high-quality single-walled CNTs (SWCNTs) were doped with a spectrum of inorganic and organic halide compounds. We studied how incorporating various halide species affects the electrical conductivity, the Seebeck coefficient, and the Power Factor. Since thermoelectric devices operate under non-ambient conditions, we also evaluated these materials' performance at elevated temperatures. Our research shows that appropriate dopant selection can result in almost fivefold improvement to the Power Factor compared to the pristine material. We also demonstrate that the chemical potential of the starting CNT network determines its properties, which is important for deciphering the true impact of chemical and physical functionalization of such ensembles. Carbon nanotube ; Thermoelectric effect ; Materials science ; Seebeck coefficient ; Halide ; Dopant ; Thermal conductivity ; Nanotechnology ; Thermoelectric materials ; Carbon fibers ; Doping ; Optoelectronics ; Composite material ; Chemistry ; Inorganic chemistry ; Thermodynamics ; Composite number Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Transition Metal Oxide Nanomaterials