jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bohayra Mortazavi | Evgeny V. Podryabinkin | Ivan S. Novikov | Stephan Roche | Timon Rabczuk | Xiaoying Zhuang | Alexander V. Shapeev
Source: Journal of Physics Materials (article, 2020-03-04)
Field: Materials Science — Materials Chemistry

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials Abstract It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline C 3 N nanosheets. C 3 N monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK −1 for C 3 N monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity. Interatomic potential ; Thermal conductivity ; Molecular dynamics ; Statistical physics ; Materials science ; Graphene ; Thermal ; Ab initio ; Lattice (music) ; Density functional theory ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics ; Nanotechnology ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Markus Stricker | Binglun Yin | Eleanor Mak | W.A. Curtin
Source: Physical Review Materials (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Machine learning for metallurgy II. A neural-network potential for magnesium Interatomic potentials are essential for studying fundamental mechanisms of deformation and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are far above the scales accessible to first-principles studies. Existing potentials for non-fcc metals and nearly all alloys are, however, not sufficiently quantitative for many crucial phenomena. Here machine learning in the Behler-Parrinello neural-network framework is used to create a broadly applicable potential for pure hcp magnesium (Mg). Lightweight Mg and its alloys are technologically important while presenting a diverse range of slip systems and crystal surfaces relevant to both plasticity and fracture that present a significant challenge for any potential. The machine learning potential is trained on first-principles density-functional theory (DFT) computable metallurgically relevant properties and is then shown to well predict metallurgically crucial dislocation and crack structures and competing phenomena. Extensive comparisons to an existing very good modified embedded atom method potential are made. These results demonstrate that a single machine learning potential can represent the wide scope of phenomena required for metallurgical studies. The DFT database is openly available for use in any other machine learning method. The method is naturally extendable to alloys, which are necessary for engineering applications but where ductility and fracture are controlled by complex atomic-scale mechanisms that are not well predicted by existing potentials. Materials science ; Ductility (Earth science) ; Atomic units ; Dislocation ; Deformation mechanism ; Scope (computer science) ; Atom (system on chip) ; Magnesium ; Deformation (meteorology) ; Artificial neural network ; Density functional theory ; Artificial intelligence ; Machine learning ; Nanotechnology ; Metallurgy ; Computer science ; Microstructure ; Computational chemistry ; Composite material ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Wennan Hu | Zhe Sheng | Xianghui Hou | Huawei Chen | Zengxing Zhang | David Wei Zhang | Peng Zhou
Source: Small Methods (review, 2020-11-17)
Field: Materials Science — Materials Chemistry

Ambipolar 2D Semiconductors and Emerging Device Applications With the rise of 2D materials, new physics and new processing techniques have emerged, triggering possibilities for the innovation of electronic and optoelectronic devices. Among them, ambipolar 2D semiconductors are of excellent gate-controlled capability and distinctive physical characteristic that the major charge carriers can be dynamically, reversibly and rapidly tuned between holes and electrons by electrostatic field. Based on such properties, novel devices, like ambipolar field-effect transistors, light-emitting transistors, electrostatic-field-charging PN diodes, are developed and show great advantages in logic and reconfigurable circuits, integrated optoelectronic circuits, and artificial neural network image sensors, enriching the functions of conventional devices and bringing breakthroughs to build new architectures. This review first focuses on the basic knowledge including fundamental principle of ambipolar semiconductors, basic material preparation techniques, and how to obtain the ambipolar behavior through electrical contact engineering. Then, the current ambipolar 2D semiconductors and their preparation approaches and main properties are summarized. Finally, the emerging new device structures are overviewed in detail, along with their novel electronic and optoelectronic applications. It is expected to shed light on the future development of ambipolar 2D semiconductors, exploring more new devices with novel functions and promoting the applications of 2D materials. Ambipolar diffusion ; Semiconductor ; Materials science ; Optoelectronics ; Computer science ; Nanotechnology ; Physics 2D Materials and Applications ; Graphene research and applications ; Nanowire Synthesis and Applications
Authors: A. Miguel | Gábor Cśanyi | Tomi Laurila | Volker L. Deringer
Source: Physical review. B./Physical review. B (article, 2020-11-02)
Field: Materials Science — Materials Chemistry

Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on extensive atomistic simulations of the deposition and growth of a-C films, describing interatomic interactions using a machine learning (ML) based Gaussian approximation potential model. We expand widely on our initial work [M. A. Caro et al., Phys. Rev. Lett. 120, 166101 (2018)] by now considering a broad range of incident ion energies, thus modeling samples that span the entire range from low-density ($s{p}^{2}$-rich) to high-density ($s{p}^{3}$-rich, ``diamondlike'') amorphous forms of carbon. Two different mechanisms are observed in these simulations, depending on the impact energy: low-energy impacts induce $sp$- and $s{p}^{2}$-dominated growth directly around the impact site, whereas high-energy impacts induce peening. Furthermore, we propose and apply a scheme for computing the anisotropic elastic properties of the a-C films. Our work provides fundamental insight into this intriguing class of disordered solids, as well as a conceptual and methodological blueprint for simulating the atomic-scale deposition of other materials with ML driven molecular dynamics. Carbon film ; Carbon fibers ; Materials science ; Deposition (geology) ; Amorphous carbon ; Amorphous solid ; Chemical engineering ; Diamond-like carbon ; Nanotechnology ; Thin film ; Engineering physics ; Composite material ; Geology ; Engineering ; Chemistry ; Composite number ; Crystallography Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Semiconductor materials and devices
Authors: Nahid Sultana | S. M. Zakir Hossain | Md Shah Alam | Mohammad S. Islam | Mahmoud Ahmed Al Abtah
Source: Advances in Engineering Software (article, 2020-08-11)
Field: Materials Science — Polymers and Plastics

Soft computing approaches for comparative prediction of the mechanical properties of jute fiber reinforced concrete Ultimate tensile strength ; Response surface methodology ; Compressive strength ; Artificial neural network ; Mean squared error ; Soft computing ; Coefficient of determination ; MATLAB ; Fiber ; Materials science ; Support vector machine ; Natural fiber ; Design of experiments ; Computer science ; Composite material ; Mathematics ; Machine learning ; Statistics Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Structural Behavior of Reinforced Concrete
Authors: Han Xiao | Ziyang Fan | Zeyang Liu | Chao Li | L. Jay Guo
Source: InfoMat (article, 2020-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of metasurface optical filters using deep neural network with high degrees of freedom Abstract In order to obtain a metasurface structure capable of filtering light of a specific wavelength range in the visible band, the traditional methods usually traverse the space consisting of possible designs, searching for a potentially satisfactory structure by performing iterative calculations to solve Maxwell's equations. In this article, we propose a systematic method based on neural networks that can complete an inverse design process to solve the problem. Compared with the traditional methods, our method is much faster while competent to encompass a high degree of freedom to generate device structures, which can ensure that the spectra of generated structures resemble the desired ones. image Traverse ; Artificial neural network ; Computer science ; Degrees of freedom (physics and chemistry) ; Inverse ; Process (computing) ; Inverse problem ; Algorithm ; Optics ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis ; Geometry Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Optical Wireless Communication Technologies
Authors: Ali Shakeri‐Zadeh | Hajar Zareyi | Roghayeh Sheervalilou | Sophie Laurent | Habib Ghaznavi | Hadi Samadian
Source: Journal of Controlled Release (review, 2020-12-16)
Field: Materials Science — Materials Chemistry

Gold nanoparticle-mediated bubbles in cancer nanotechnology Nanotechnology ; Microbubbles ; Laser ; Colloidal gold ; Materials science ; Nanoparticle ; Cancer cell ; Bubble ; Cancer ; Computer science ; Optics ; Ultrasound ; Medicine ; Physics Ultrasound and Cavitation Phenomena ; Laser-Ablation Synthesis of Nanoparticles ; Nanoparticle-Based Drug Delivery
Authors: Jikuo Wang | Changchun Liu | Liping Li | Wang Li | Lianke Yao | Han Li | Huan Zhang
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease Coronary arteriongraphy (CAG) is an accurate invasive technique for the diagnosis of coronary heart disease (CHD). However, its invasive procedure is not appropriate for the detection of CHD in the annual physical examination. With the successful application of machine learning (ML) in various fields, our goal is to perform selective integration of multiple ML algorithms and verify the validity of feature selection methods with personal clinical information commonly seen in the annual physical examination. In this study, a two level stacking based model is designed in which level 1 is base-level and level 2 is meta-level. The predictions of base-level classifiers is selected as the input of meta-level. The pearson correlation coefficient and maximum information coefficient are first calculated to find the classifier with the lowest correlation. Then enumeration algorithm is used to find the best combining classifiers which acquire the best result in the end. The Z-Alizadeh Sani CHD dataset which we use consists of 303 cases verified by CAG. Experimental results demonstrate that the proposed model obtains an accuracy, sensitivity and specificity of 95.43%, 95.84%, 94.44%, respectively for the detection of CHD. The proposed method can effectively aid clinicians to detect those with normal coronary arteries from those with CHD. Stacking ; Computer science ; Cardiology ; Disease ; Internal medicine ; Medicine ; Physics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Image and Signal Denoising Methods
Authors: Daniel J. Baillache | Asier Unciti‐Broceta
Source: RSC Medicinal Chemistry (review, 2020-01-01)
Field: Chemistry — Organic Chemistry

Recent developments in anticancer kinase inhibitors based on the pyrazolo[3,4-<i>d</i>]pyrimidine scaffold Pyrazolo[3,4-<i>d</i>]pyrimidines have become of significant interest for the medicinal chemistry community as a privileged scaffold for the development of kinase inhibitors to treat a range of diseases, including cancer. This fused nitrogen-containing heterocycle is an isostere of the adenine ring of ATP, allowing the molecules to mimic hinge region binding interactions in kinase active sites. Similarities in kinase ATP sites can be exploited to direct the activity and selectivity of pyrazolo[3,4-<i>d</i>]pyrimidines to multiple oncogenic targets through focussed chemical modification. As a result, pharma and academic efforts have succeeded in progressing several pyrazolo[3,4-<i>d</i>]pyrimidines to clinical trials, including the BTK inhibitor ibrutinib, which has been approved for the treatment of several B-cell cancers. In this review, we examine the pyrazolo[3,4-<i>d</i>]pyrimidines currently in clinical trials for oncology patients, as well as those published in the literature during the last 5 years for different anticancer indications. Scaffold ; Pyrimidine ; Kinase ; Chemistry ; Cancer research ; Medicine ; Stereochemistry ; Biochemistry ; Biomedical engineering Quinazolinone synthesis and applications ; Biochemical and Molecular Research ; Chronic Lymphocytic Leukemia Research
Authors: Hichem Maslah | Charles Skarbek | Stéphanie Pèthe | Raphaël Labruère
Source: European Journal of Medicinal Chemistry (review, 2020-08-05)
Field: Chemistry — Organic Chemistry

Anticancer boron-containing prodrugs responsive to oxidative stress from the tumor microenvironment Prodrug ; Chemistry ; Reactive oxygen species ; Tumor microenvironment ; In vivo ; Oxidative stress ; Boronic acid ; Small molecule ; In vitro ; Biochemistry ; Combinatorial chemistry ; Pharmacology ; Tumor cells ; Cancer research Organoboron and organosilicon chemistry ; Synthetic Organic Chemistry Methods ; Histone Deacetylase Inhibitors Research
Authors: Siddappa A. Patil | Amy P Hoagland | Shivaputra A. Patil | Alejandro Bugarin
Source: Future Medicinal Chemistry (review, 2020-11-24)
Field: Chemistry — Organic Chemistry

<i>N</i> -Heterocyclic Carbene-Metal Complexes as Bio-Organometallic Antimicrobial and Anticancer Drugs, an Update (2015–2020) <i>N</i>-heterocyclic carbenes (NHCs) are organic compounds that typically mimic the chemical properties of phosphines. NHCs have made a significant impact on the field of coordination and organometallic chemistry because they are easy to prepare and handle and because of their versatility and stability. Importantly, the physicochemical properties of NHCs can be easily fine-tuned by simple variation of substituents on the nitrogen atoms. Over the past few years, various NHC-metal complexes have been extensively used as metal-based drug candidates and catalysts (homogeneous or heterogeneous) for various applications. To help assist future work with these compounds, this review provides a thorough review on the latest information involving some biomedical applications of NHC-metal complexes. Specifically, this article focuses on recent advances in the design, synthesis, characterization and biomedical applications (e.g., antimicrobial and anticancer activity) of various NHC-metal complexes (metal: silver, gold, palladium, rhodium, ruthenium, iridium and platinum) covering work published from 2015 to 2020. It is hoped that the promising discoveries to date will help accelerate studies on the encouraging potential of NHC-metal complexes as a class of effective therapeutic agents. Antimicrobial ; Carbene ; Chemistry ; Combinatorial chemistry ; Antifungal ; Microbiology ; Organic chemistry ; Biology ; Catalysis N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Carbon dioxide utilization in catalysis
Authors: P. Mehrabi | Henrike M. Müller‐Werkmeister | Jan-Philipp Leimkohl | Hendrik Schikora | J. Ninković | S. Krivokuca | L. Andricek | Sascha W. Epp | D.A. Sherrell | Robin L. Owen | Arwen R. Pearson | Friedjof Tellkamp | Eike C. Schulz | R. J. Dwayne Miller
Source: Journal of Synchrotron Radiation (article, 2020-02-27)
Field: Materials Science — Materials Chemistry

The HARE chip for efficient time-resolved serial synchrotron crystallography Serial synchrotron crystallography (SSX) is an emerging technique for static and time-resolved protein structure determination. Using specifically patterned silicon chips for sample delivery, the `hit-and-return' (HARE) protocol allows for efficient time-resolved data collection. The specific pattern of the crystal wells in the HARE chip provides direct access to many discrete time points. HARE chips allow for optical excitation as well as on-chip mixing for reaction initiation, making a large number of protein systems amenable to time-resolved studies. Loading of protein microcrystals onto the HARE chip is streamlined by a novel vacuum loading platform that allows fine-tuning of suction strength while maintaining a humid environment to prevent crystal dehydration. To enable the widespread use of time-resolved serial synchrotron crystallography (TR-SSX), detailed technical descriptions of a set of accessories that facilitate TR-SSX workflows are provided. Synchrotron ; Chip ; Silicon chip ; Mixing (physics) ; Computer science ; Crystallography ; Materials science ; Chemistry ; Silicon ; Optoelectronics ; Physics ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Charles Romain | Stéphane Bellemin‐Laponnaz | Samuel Dagorne
Source: Coordination Chemistry Reviews (article, 2020-07-24)
Field: Chemistry — Organic Chemistry

Recent progress on NHC-stabilized early transition metal (group 3–7) complexes: Synthesis and applications Catalysis ; Steric effects ; Monomer ; Reactivity (psychology) ; Homogeneous ; Transition metal ; Metathesis ; Main group element ; Polymerization ; Chemistry ; Combinatorial chemistry ; Metal ; Nanotechnology ; Group (periodic table) ; Materials science ; Organic chemistry ; Polymer N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Synthetic Organic Chemistry Methods
Authors: Chenlu He | Zeyu Feng | Sizhen Shan | Mengqiao Wang | Xin Chen | Gang Zou
Source: Nature Communications (article, 2020-03-04)
Field: Materials Science — Materials Chemistry

Highly enantioselective photo-polymerization enhanced by chiral nanoparticles and in situ photopatterning of chirality Chiral noble metal nanoparticles has recently gained great interest due to their potential applications including ultrasensitive chiral recognition and asymmetric synthesis. We anticipate that they could be utilized to induce asymmetric photo-polymerization reactions with high enantioselectivity and reactivity. Here, we report such a system. By employing silver nanoparticles modified with cysteine as the chiral inducer, polydiacetylene (PDA) with high chiral asymmetry was obtained from achiral diacetylene monomers triggered with unpolarized UV light. Furthermore, the helical sense of chirality can be controlled by varying the wavelength of UV irradiation. This enables a feasible and economical method to fabricate programmable 2D patterns of chiral PDA with tailored chirality distributions, such as smooth gradients in chirality and micropatterns with tailorable circularly polarized luminescence. Our finding not only opens a pathway for producing programmable chiroptical micropatterns, but also is highly valuable for deeper understanding of symmetry breaking in enantioselective photochemical reactions. Enantioselective synthesis ; Chirality (physics) ; In situ ; Nanoparticle ; Polymerization ; Nanotechnology ; Materials science ; Photochemistry ; Chemistry ; Chemical engineering ; Catalysis ; Physics ; Organic chemistry ; Polymer ; Symmetry breaking Photochromic and Fluorescence Chemistry ; Synthesis and Properties of Aromatic Compounds ; Polydiacetylene-based materials and applications
Authors: Flaviu Tăbăran | Cristian Matea | Teodora Mocan | Marian Mihaiu | Cornel Iancu | Lucian Mocan
Source: International Journal of Nanomedicine (review, 2020-03-01)
Field: Materials Science — Materials Chemistry

&lt;p&gt;Silver Nanoparticles for the Therapy of Tuberculosis&lt;/p&gt; Rapid emergence of aggressive, multidrug-resistant Mycobacteria strain represents the main cause of the current antimycobacterial-drug crisis and status of tuberculosis (TB) as a major global health problem. The relatively low-output of newly approved antibiotics contributes to the current orientation of research towards alternative antibacterial molecules such as advanced materials. Nanotechnology and nanoparticle research offers several exciting new-concepts and strategies which may prove to be valuable tools in improving the TB therapy. A new paradigm in antituberculous therapy using silver nanoparticles has the potential to overcome the medical limitations imposed in TB treatment by the drug resistance which is commonly reported for most of the current organic antibiotics. There is no doubt that AgNPs are promising future therapeutics for the medication of mycobacterial-induced diseases but the viability of this complementary strategy depends on overcoming several critical therapeutic issues as, poor delivery, variable intramacrophagic antimycobacterial efficiency, and residual toxicity. In this paper, we provide an overview of the pathology of mycobacterial-induced diseases, andhighlight the advantages and limitations of silver nanoparticles (AgNPs) in TB treatment. Antimycobacterial ; Tuberculosis ; Drug delivery ; Nanotechnology ; Antibiotics ; Medicine ; Silver nanoparticle ; Drug ; Extensively drug-resistant tuberculosis ; Mycobacterium tuberculosis ; Pharmacology ; Materials science ; Nanoparticle ; Biology ; Microbiology ; Pathology Nanoparticles: synthesis and applications ; Phenothiazines and Benzothiazines Synthesis and Activities ; Tuberculosis Research and Epidemiology
Authors: Timothy Murray | S. Thomas | Yuxiang Wu | Wayne Neil | Christopher Hutchinson
Source: Additive manufacturing (article, 2020-02-12)
Field: Materials Science — Materials Chemistry

Selective laser melting of nickel aluminium bronze Materials science ; Intermetallic ; Microstructure ; Bronze ; Metallurgy ; Aluminium ; Selective laser melting ; Corrosion ; Martensite ; Alloy Metallurgy and Material Science ; Additive Manufacturing Materials and Processes ; Additive Manufacturing and 3D Printing Technologies