jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Timo Gahlmann | Philippe Tassin
Source: Physical review. B./Physical review. B (article, 2022-08-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep neural networks for the prediction of the optical properties and the free-form inverse design of metamaterials Many phenomena in physics, including light, water waves, and sound, are described by wave equations. Given their coefficients, wave equations can be solved to high accuracy, but the presence of the wavelength scale often leads to large computer simulations for anything beyond the simplest geometries. The inverse problem, determining the coefficients from a field on a boundary, is even more demanding, since traditional optimization requires a large number of forward problems to be solved sequentially. Here we show that the free-form inverse problem of wave equations can be solved with machine learning. First we show that deep neural networks can be used to predict the optical properties of nanostructured materials such as metasurfaces. Then we demonstrate the free-form inverse design of such nanostructures and show that constraints imposed by experimental feasibility can be taken into account. Our neural networks promise automated design in several technologies based on the wave equation. Inverse problem ; Artificial neural network ; Inverse ; Metamaterial ; Wave equation ; Computer science ; Boundary value problem ; Wavelength ; Field (mathematics) ; Physics ; Applied mathematics ; Mathematics ; Mathematical analysis ; Optics ; Artificial intelligence ; Geometry Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Acoustic Wave Phenomena Research
Authors: Isha Sachdeva | R. Sivasubramani | Utkarsh Chadha | Hruditha Punugoti | Senthil Kumaran Selvaraj
Source: Neural Computing and Applications (article, 2022-08-17)
Field: Chemistry — Organic Chemistry

Computational AI models in VAT photopolymerization: a review, current trends, open issues, and future opportunities Stereolithography ; Computer science ; Artificial intelligence ; Machine learning ; Automation ; Artificial neural network ; Photopolymer ; Process (computing) ; Industrial engineering ; Variety (cybernetics) ; Feature (linguistics) ; 3D printing ; Mechanical engineering ; Engineering ; Materials science Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Steve Kench | Isaac Squires | Amir Dahari | Samuel J. Cooper
Source: Scientific Data (article, 2022-10-22)
Field: Materials Science — Materials Chemistry

MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN 3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. This has proven a useful tool for understanding how complex material systems behave under applied stresses, temperatures and chemical conditions. However, 3D imaging of materials is challenging for a number of reasons, including limited field of view, low resolution and difficult sample preparation. Recently, a machine learning method, SliceGAN, was developed to statistically generate 3D microstructural datasets of arbitrary size using a single 2D input slice as training data. In this paper, we present the results from applying SliceGAN to 87 different microstructures, ranging from biological materials to high-strength steels. To demonstrate the accuracy of the synthetic volumes created by SliceGAN, we compare three microstructural properties between the 2D training data and 3D generations, which show good agreement. This new microstructure library both provides valuable 3D microstructures that can be used in models, and also demonstrates the broad applicability of the SliceGAN algorithm. Microstructure ; Ranging ; Computer science ; Materials science ; Finite element method ; Artificial intelligence ; Biological system ; Algorithm ; Metallurgy ; Structural engineering ; Engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Industrial Vision Systems and Defect Detection
Authors: Daniel Willhelm | Nathan M. Wilson | Raymundo Arróyave | Xiaoning Qian | Tahir Çağın | Ruth Pachter | Xiaofeng Qian
Source: ACS Applied Materials & Interfaces (article, 2022-05-27)
Field: Materials Science — Materials Chemistry

Predicting Van der Waals Heterostructures by a Combined Machine Learning and Density Functional Theory Approach Van der Waals (vdW) heterostructures are constructed by different two-dimensional (2D) monolayers vertically stacked and weakly coupled by van der Waals interactions. VdW heterostructures often possess rich physical and chemical properties that are unique to their constituent monolayers. As many 2D materials have been recently identified, the combinatorial configuration space of vdW-stacked heterostructures grows exceedingly large, making it difficult to explore through traditional experimental or computational approaches in a trial-and-error manner. Here, we present a computational framework that combines first-principles electronic structure calculations, 2D material database, and supervised machine learning methods to construct efficient data-driven models capable of predicting electronic and structural properties of vdW heterostructures from their constituent monolayer properties. We apply this approach to predict the band gap, band edges, interlayer distance, and interlayer binding energy of vdW heterostructures. Our data-driven model will open avenues for efficient screening and discovery of low-dimensional vdW heterostructures and moiré superlattices with desired electronic and optical properties for targeted device applications. van der Waals force ; Heterojunction ; Materials science ; Monolayer ; Density functional theory ; Band gap ; Superlattice ; Electronic structure ; Nanotechnology ; Condensed matter physics ; Physics ; Molecule ; Optoelectronics ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Guillermo Fernandez-Bunster | Pamela Pavez
Source: Molecules (review, 2022-11-30)
Field: Materials Science — Biomaterials

Novel Production Methods of Polyhydroxyalkanoates and Their Innovative Uses in Biomedicine and Industry Polyhydroxyalkanoate (PHA), a biodegradable polymer obtained from microorganisms and plants, have been widely used in biomedical applications and devices, such as sutures, cardiac valves, bone scaffold, and drug delivery of compounds with pharmaceutical interests, as well as in food packaging. This review focuses on the use of polyhydroxyalkanoates beyond the most common uses, aiming to inform about the potential uses of the biopolymer as a biosensor, cosmetics, drug delivery, flame retardancy, and electrospinning, among other interesting uses. The novel applications are based on the production and composition of the polymer, which can be modified by genetic engineering, a semi-synthetic approach, by changing feeding carbon sources and/or supplement addition, among others. The future of PHA is promising, and despite its production costs being higher than petroleum-based plastics, tools given by synthetic biology, bioinformatics, and machine learning, among others, have allowed for great production yields, monomer and polymer functionalization, stability, and versatility, a key feature to increase the uses of this interesting family of polymers. Polyhydroxyalkanoates ; Biopolymer ; Biochemical engineering ; Drug delivery ; Nanotechnology ; Electrospinning ; Biomedicine ; Biotechnology ; Polymer ; Computer science ; Materials science ; Chemistry ; Engineering ; Organic chemistry ; Biology ; Bioinformatics ; Bacteria biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Anna Archetti | Renjie Lin | Nathanaël Restori | Fatemeh Kiani | Ted V. Tsoulos | Giulia Tagliabue
Source: Nanophotonics (article, 2022-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermally reconfigurable metalens Reconfigurable metalenses are compact optical components composed by arrays of meta-atoms that offer unique opportunities for advanced optical systems, from microscopy to augmented reality platforms. Although poorly explored in the context of reconfigurable metalenses, thermo-optical effects in resonant silicon nanoresonators have recently emerged as a viable strategy to realize tunable meta-atoms. In this work, we report the proof-of-concept design of an ultrathin (300 nm thick) and thermo-optically reconfigurable silicon metalens operating at a fixed, visible wavelength (632 nm). Importantly, we demonstrate continuous, linear modulation of the focal-length up to 21% (from 165 μm at 20 °C to 135 μm at 260 °C). Operating under right-circularly polarized light, our metalens exhibits an average conversion efficiency of 26%, close to mechanically modulated devices, and has a diffraction-limited performance. Overall, we envision that, combined with machine-learning algorithms for further optimization of the meta-atoms, thermally reconfigurable metalenses with improved performance will be possible. Also, the generality of this approach could offer inspiration for the realization of active metasurfaces with other emerging materials within field of thermo-nanophotonics. Nanomaterials ; Nanotechnology ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Chemical and Physical Properties of Materials
Authors: Mads-Peter Verner Christiansen | Nikolaj Rønne | Bjørk Hammer
Source: arXiv (Cornell University) (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Atomistic Global Optimization X: A Python package for optimization of atomistic structures Modelling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual identity and position of all involved atoms. Obtaining such information for macro-molecules, nano-particles, clusters, and for the surface, interface, and bulk phases of amorphous and solid materials represents a difficult high dimensional global optimization problem. The rise of machine learning techniques in materials science has, however, led to many compelling developments that may speed up such structure searches. The complexity of the new methods have established the necessity for an efficient way of experimenting with and assembling them into global optimization algorithms. In this paper we introduce the Atomistic Global Optimization X (AGOX) framework and code, as a customizable approach to building efficient global optimization algorithms. A modular way of expressing global optimization algorithms is described and modern programming practices are used to enable that modularity in the freely available AGOX python package. Two examples of global optimization problems are analyzed: One that is computationally inexpensive which is used to showcase that AGOX enables the expression of multiple global optimization algorithms. As the other example, AGOX is used for solving a complex atomistic optimization problem for a metal-nitride nano-cluster embedded in a graphene sheet as described at the density functional theory (DFT) level. Python (programming language) ; Global optimization ; Computer science ; Modular design ; Optimization problem ; Computational science ; Theoretical computer science ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Xinghao Chen | Gongyi Cheng | Shuhan Liu | Sizhuo Meng | Yiping Jiao | Wenjie Zhang | Jing Liang | Wang Zhang | Bin Wang | Xiaoxuan Xu | Jing Xu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2022-05-16)
Field: Chemistry — Analytical Chemistry

Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish Convolutional neural network ; Pooling ; Chemometrics ; Computer science ; sort ; Pattern recognition (psychology) ; Artificial intelligence ; Partial least squares regression ; Artificial neural network ; Fish <Actinopterygii> ; Biological system ; Machine learning ; Fishery ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Alex Jinpeng Wang | Haotong Liang | Austin McDannald | Ichiro Takeuchi | A. Gilad Kusne
Source: Oxford Open Materials Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Benchmarking active learning strategies for materials optimization and discovery Abstract Autonomous physical science is revolutionizing materials science. In these systems, machine learning (ML) controls experiment design, execution and analysis in a closed loop. Active learning, the ML field of optimal experiment design, selects each subsequent experiment to maximize knowledge toward the user goal. Autonomous system performance can be further improved with the implementation of scientific ML, also known as inductive bias-engineered artificial intelligence, which folds prior knowledge of physical laws (e.g. Gibbs phase rule) into the algorithm. As the number, diversity and uses for active learning strategies grow, there is an associated growing necessity for real-world reference datasets to benchmark strategies. We present a reference dataset and demonstrate its use to benchmark active learning strategies in the form of various acquisition functions. Active learning strategies are used to rapidly identify materials with optimal physical properties within a compositional phase diagram mapping a ternary materials system. The data are from an actual Fe-Co-Ni thin-film library and include previously acquired experimental data for materials compositions, X-ray diffraction patterns and two functional properties of magnetic coercivity and the Kerr rotation. Popular active learning methods along with a recent scientific active learning method are benchmarked for their materials optimization performance. Among the acquisition functions benchmarked, Expected Improvement demonstrated the best overall performance. We discuss the relationship between algorithm performance, materials search space complexity and the incorporation of prior knowledge, and we encourage benchmarking more and novel active learning schemes. Benchmarking ; Active learning (machine learning) ; Benchmark (surveying) ; Computer science ; Machine learning ; Artificial intelligence ; Reinforcement learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Kethaki Wickramaarachchi | Manickam Minakshi | S. Assa Aravindh | Rukshima Dabare | Xiangpeng Gao | Zhong‐Tao Jiang | Kok Wai Wong
Source: Nanomaterials (article, 2022-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models Porous carbon derived from grape marc (GM) was synthesized via carbonization and chemical activation processes. Extrinsic nitrogen (N)-dopant in GM, activated by KOH, could render its potential use in supercapacitors effective. The effects of chemical activators such as potassium hydroxide (KOH) and zinc chloride (ZnCl<sub>2</sub>) were studied to compare their activating power toward the development of pore-forming mechanisms in a carbon electrode, making them beneficial for energy storage. GM carbon impregnated with KOH for activation (KAC), along with urea as the N-dopant (KAC<sub>urea</sub>), exhibited better morphology, hierarchical pore structure, and larger surface area (1356 m<sup>2</sup> g<sup>-1</sup>) than the GM carbon activated by ZnCl<sub>2</sub> (ZnAC). Moreover, density functional theory (DFT) investigations showed that the presence of N-dopant on a graphite surface enhances the chemisorption of O adsorbates due to the enhanced charge-transfer mechanism. KAC<sub>urea</sub> was tested in three aqueous electrolytes with different ions (LiOH, NaOH, and NaClO<sub>4</sub>), which delivered higher specific capacitance, with the NaOH electrolyte exhibiting 139 F g<sup>-1</sup> at a 2 mA current rate. The NaOH with the alkaline cation Na<sup>+</sup> offered the best capacitance among the electrolytes studied. A multilayer perceptron (MLP) model was employed to describe the effects of synthesis conditions and physicochemical and electrochemical parameters to predict the capacitance and power outputs. The proposed MLP showed higher accuracy, with an R<sup>2</sup> of 0.98 for capacitance prediction. Repurposing ; Supercapacitor ; Fabrication ; Doping ; Computer science ; Artificial intelligence ; Materials science ; Nanotechnology ; Engineering ; Chemistry ; Optoelectronics ; Medicine ; Capacitance ; Waste management Supercapacitor Materials and Fabrication
Authors: Simone Gallarati | Puck van Gerwen | Rubén Laplaza | Sergi Vela | Alberto Fabrizio | Clémence Corminbœuf
Source: Chemical Science (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance. Combinatorial chemistry ; Chemistry ; Computational biology ; Nanotechnology ; Computer science ; Materials science ; Biology Asymmetric Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Reactions
Authors: Yixi Deng | Lei Liu | Jingyan Li | Li Gao
Source: Biosensors (review, 2022-09-20)
Field: Materials Science — Materials Chemistry

Sensors Based on the Carbon Nanotube Field-Effect Transistors for Chemical and Biological Analyses Nano biochemical sensors play an important role in detecting the biomarkers related to human diseases, and carbon nanotubes (CNTs) have become an important factor in promoting the vigorous development of this field due to their special structure and excellent electronic properties. This paper focuses on applying carbon nanotube field-effect transistor (CNT-FET) biochemical sensors to detect biomarkers. Firstly, the preparation method, physical and electronic properties and functional modification of CNTs are introduced. Then, the configuration and sensing mechanism of CNT-FETs are introduced. Finally, the latest progress in detecting nucleic acids, proteins, cells, gases and ions based on CNT-FET sensors is summarized. Carbon nanotube ; Field-effect transistor ; Nanotechnology ; Materials science ; Carbon nanotube field-effect transistor ; Transistor ; Nucleic acid ; Chemistry ; Electrical engineering ; Engineering ; Biochemistry Carbon Nanotubes in Composites ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Jonathan Farhi | Ioannis N. Lykakis | George E. Κostakis
Source: Catalysts (article, 2022-06-15)
Field: Chemistry — Organic Chemistry

Metal-Catalysed A3 Coupling Methodologies: Classification and Visualisation The multicomponent reaction of aldehydes, amines, and alkynes, known as A3 coupling, yields propargylamines, a valuable organic scaffold, and has received significant interest and attention in the last years. In order to fully realise the potential of the metal-based catalytic protocols that facilitate this transformation, we summarise substrates, in situ and well-characterised synthetic methods that provide this scaffold and attempt a monumental classification considering several variables (Metal, Coordinating atom(s), Ligand type and name, in-situ or well-characterised, co-catalyst, catalyst and ligand Loading (mol%), solvent, volume, atmosphere, temperature, microwave, time, yield, selectivity (e.e. d.r.), substrate name, functionality, loading (amines, aldehydes, alkynes), and use of molecular sieves). This pioneering work creates a valuable database that contains 2376 entries and allows us to produce graphs and better visualise their impact on the reaction. Catalysis ; Substrate (aquarium) ; Ligand (biochemistry) ; Yield (engineering) ; Solvent ; Selectivity ; Metal ; Combinatorial chemistry ; Chemistry ; Molecular sieve ; Coupling reaction ; Materials science ; Organic chemistry ; Metallurgy ; Geology Click Chemistry and Applications ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Fabio Zobi
Source: Micro (article, 2022-06-20)
Field: Materials Science — Biomaterials

Diatom Biosilica in Targeted Drug Delivery and Biosensing Applications: Recent Studies Diatoms are single-cell algae encased in a cell wall (named frustule) composed of transparent, biogenic (or opaline) silica with intricate and strikingly regular patterns. Over the past 30 years, these microorganisms have proven to be a valuable replacement for synthetic silica that satisfies numerous pharmaceutical requirements for the realization of drug delivery vectors, biosensing supports and photonic crystals. It is not only the structural features of the diatoms, but also the possibility of chemically modifying the frustule that permits the relatively straightforward transformation of the biosilica into potential devices for biomedical applications. In this short review, we explore the applications of diatoms-derived biosilica in the drug delivery and biosensing fields. Specifically, we consider the use of diatoms for the targeted delivery of anticancer and antibiotic drugs and how the same microalgae are employed in the fabrications of biosensors whose analyte signal response is evaluated via fluorescence and surface-enhanced Raman scattering techniques. We limit our discussion to studies published in the last seven years, with the intention of minimizing possible redundancy with respect to previously published contributions. Frustule ; Biosensor ; Nanotechnology ; Drug delivery ; Diatom ; Analyte ; Bioanalysis ; Materials science ; Chemistry ; Biology ; Ecology ; Chromatography Diatoms and Algae Research ; Algal biology and biofuel production ; Analytical Chemistry and Sensors