jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shanshan He | Ruisi Wang | Hailu Luo
Source: Nanophotonics (review, 2022-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computing metasurfaces for all‐optical image processing: a brief review Computing metasurfaces are two-dimensional artificial nanostructures capable of performing mathematical operations on the input electromagnetic field, including its amplitude, phase, polarization, and frequency distributions. Rapid progress in the development of computing metasurfaces provide exceptional abilities for all-optical image processing, including the edge-enhanced imaging, which opens a broad range of novel and superior applications for real-time pattern recognition. In this paper, we review recent progress in the emerging field of computing metasurfaces for all-optical image processing, focusing on innovative and promising applications in optical analog operations, image processing, microscopy imaging, and quantum imaging. Nanomaterials ; Computer science ; Image processing ; Nanotechnology ; Optics ; Materials science ; Optoelectronics ; Image (mathematics) ; Physics ; Computer vision Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Chuanyin Xiong | Tianxu Wang | Zhenyang Zhao | Yonghao Ni
Source: SmartMat (article, 2022-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress in the development of smart supercapacitors Abstract Recently, with the continuous development of human society and the continuous innovation of technologies, the intelligence era has arrived. Various intelligent electronic devices continue to be developed, in which flexible wearable electronic devices are highly favored by people. To meet the requirements of the normal operation of intelligent devices, the key point lies in the development of new smart energy storage devices. Accordingly, smart supercapacitors have been widely focused on and studied by researchers recently with the introduction of intelligent functions, such as electrochromism, self‐healing, and shape memory, into supercapacitors to broaden their application fields and promote their smart development. This can meet not only people's energy needs but also people's diverse personality needs and make our life more convenient, fast, and more intelligent than ever. Therefore, it is very important to summarize related work on smart supercapacitors. Although researchers have performed much research on smart supercapacitors, there is still little literature summary on the related work of different smart supercapacitors. Accordingly, this paper mainly introduces the research progress on electrochromic, self‐healing, shape memory, and self‐charging smart supercapacitors in recent years and discusses the development prospects and challenges of smart supercapacitors. Supercapacitor ; Computer science ; Electrochromism ; Wearable computer ; Key (lock) ; Smart material ; Smartwatch ; Nanotechnology ; Embedded system ; Computer security ; Materials science ; Capacitance Supercapacitor Materials and Fabrication ; Advanced Memory and Neural Computing ; Conducting polymers and applications
Authors: Songbin Liu | Zhengce An | Bo Zhou
Source: Chemical Engineering Journal (article, 2022-10-08)
Field: Materials Science — Materials Chemistry

Optical multiplexing of upconversion in nanoparticles towards emerging applications Photon upconversion ; Nanotechnology ; Materials science ; Dopant ; Nanostructure ; Nanophotonics ; Multiplexing ; Nanoparticle ; Doping ; Computer science ; Optoelectronics ; Telecommunications Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Perovskite Materials and Applications
Authors: Stephan N. Steinmann | Qing Wang | Zhi Wei Seh
Source: Materials Horizons (review, 2022-12-09)
Field: Materials Science — Materials Chemistry

How machine learning can accelerate electrocatalysis discovery and optimization Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts <i>via</i> a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories. Electrocatalyst ; Context (archaeology) ; Computer science ; Characterization (materials science) ; Machine learning ; Process optimization ; Throughput ; Process (computing) ; Artificial intelligence ; Nanotechnology ; Biochemical engineering ; Materials science ; Chemistry ; Engineering ; Electrochemistry ; Chemical engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Roshan Patel | Carlos H. Borca | Michael A. Webb
Source: Molecular Systems Design & Engineering (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Featurization strategies for polymer sequence or composition design by machine learning In this work, we present, evaluate, and analyze strategies for representing polymer chemistry to machine learning models for the advancement of data-driven sequence or composition design of macromolecules. Sequence (biology) ; Composition (language) ; Computer science ; Artificial intelligence ; Chemistry ; Art ; Biochemistry ; Literature Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Thijs Stuyver | Connor W. Coley
Source: The Journal of Chemical Physics (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability There is a perceived dichotomy between structure-based and descriptor-based molecular representations used for predictive chemistry tasks. Here, we study the performance, generalizability, and explainability of the quantum mechanics-augmented graph neural network (ml-QM-GNN) architecture as applied to the prediction of regioselectivity (classification) and of activation energies (regression). In our hybrid QM-augmented model architecture, structure-based representations are first used to predict a set of atom- and bond-level reactivity descriptors derived from density functional theory calculations. These estimated reactivity descriptors are combined with the original structure-based representation to make the final reactivity prediction. We demonstrate that our model architecture leads to significant improvements over structure-based GNNs in not only overall accuracy but also in generalization to unseen compounds. Even when provided training sets of only a couple hundred labeled data points, the ml-QM-GNN outperforms other state-of-the-art structure-based architectures that have been applied to these tasks as well as descriptor-based (linear) regressions. As a primary contribution of this work, we demonstrate a bridge between data-driven predictions and conceptual frameworks commonly used to gain qualitative insights into reactivity phenomena, taking advantage of the fact that our models are grounded in (but not restricted to) QM descriptors. This effort results in a productive synergy between theory and data science, wherein QM-augmented models provide a data-driven confirmation of previous qualitative analyses, and these analyses in turn facilitate insights into the decision-making process occurring within ml-QM-GNNs. Generalizability theory ; Generalization ; Computer science ; Representation (politics) ; Machine learning ; Artificial neural network ; Artificial intelligence ; Reactivity (psychology) ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ankit Roy | M. F. N. Taufique | Hrishabh Khakurel | Ram Devanathan | D. D. Johnson | Ganesh Balasubramanian
Source: npj Materials Degradation (article, 2022-01-31)
Field: Materials Science — Materials Chemistry

Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys Abstract More than $270 billion is spent on combatting corrosion annually in the USA alone. As such, we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our focus is on a non-traditional class of alloys called multi-principal element alloys (MPEAs). Given the vast search space due to the variety of compositions and descriptors to be considered, and based upon existing corrosion data for MPEAs, we demonstrate descriptor optimization to predict corrosion resistance of any given MPEA. Our ML model with descriptor optimization predicts the corrosion resistance of a given MPEA in the presence of an aqueous environment by down selecting two environmental descriptors (pH of the medium and halide concentration), one chemical composition descriptor (atomic % of element with minimum reduction potential), and two atomic descriptors (difference in lattice constant ( $$\Delta {{{\mathrm{a}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Δ</mml:mi> <mml:mi>a</mml:mi> </mml:mrow> </mml:math> ) and average reduction potential). Our findings show that, while it is possible to down select corrosion-resistant MPEAs by using ML from a large search space, a larger dataset and higher quality data are needed to accurately predict the corrosion rate of MPEAs. This study shows both the promise and the perils of ML when applied to a complex chemical phenomenon like corrosion of alloys. Corrosion ; Principal component analysis ; Materials science ; Computer science ; Predictive modelling ; Artificial intelligence ; Variety (cybernetics) ; Machine learning ; Algorithm ; Metallurgy Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: M. Vijayakumar | G. J. Surendhar | L. Natrayan | Pravin P. Patil | P.M. Bupathi Ram | Prabhu Paramasivam
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Evolution and Recent Scenario of Nanotechnology in Agriculture and Food Industries Nanotechnology is an emerging technology in the field of food and agriculture; every individual molecule and atom can be modified or controlled by this technology. Everything that exists on the earth is made up of atoms and molecules. Problems in all the fields can easily be overcome by modifying or altering their nanosize. Similarly, nanotechnology could address many issues in the agriculture and food industries. Nanomaterials play a keen role in the place of pesticides, fertilizers, and biosensors. Nutrition enhancement, safe delivery of bioactive components and micronutrients, and food preservations were facilitated by the applications of nanotechnologies. Efforts have to be taken to create awareness among the public in this nanotechnology field. Future research directions were identified in this review to improve the nanoembedded agriculture system. This article reviews the recent development in the agriculture and food industries through nanotechnology, application of nanotechnology in the food and agricultural sectors, and research backlogs in such sectors. Agriculture ; Nanotechnology ; Applications of nanotechnology ; Impact of nanotechnology ; Materials science ; Business ; Societal impact of nanotechnology ; Ecology ; Biology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Aluminum toxicity and tolerance in plants and animals
Authors: Danial Khatamsaz | Brent Vela | Prashant Singh | D. D. Johnson | Douglas Allaire | Raymundo Arróyave
Source: Acta Materialia (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys Bayesian optimization ; Materials science ; Ductility (Earth science) ; Material Design ; Constraint (computer-aided design) ; Mathematical optimization ; Density functional theory ; Metric (unit) ; Computer science ; Mechanical engineering ; Composite material ; Mathematics ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Zhonghui Shen | Hanxing Liu | Yang Shen | Jia‐Mian Hu | Long‐Qing Chen | Ce‐Wen Nan
Source: Interdisciplinary materials (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

Machine learning in energy storage materials Abstract With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution of the materials research paradigm. Here, taking dielectric capacitors and lithium‐ion batteries as two representative examples, we review substantial advances of machine learning in the research and development of energy storage materials. First, a thorough discussion of the machine learning framework in materials science is presented. Then, we summarize the applications of machine learning from three aspects, including discovering and designing novel materials, enriching theoretical simulations, and assisting experimentation and characterization. Finally, a brief outlook is highlighted to spark more insights on the innovative implementation of machine learning in materials science. SPARK (programming language) ; Computer science ; Energy storage ; Machine learning ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Diptendu Roy | Shyama Charan Mandal | Biswarup Pathak
Source: The Journal of Physical Chemistry Letters (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Exploration of High Entropy Alloy-Based Catalysts for Selective CO<sub>2</sub> Reduction to Methanol Catalytic conversion of CO<sub>2</sub> to carbon neutral fuels can be ecofriendly and allow for economic replacement of fossil fuels. Here, we have investigated high-throughput screening of high entropy alloy (Cu, Co, Ni, Zn, and Sn) based catalysts through machine learning (ML) for CO<sub>2</sub> hydrogenation to methanol. Stability and catalytic activity studies of these catalysts have been performed for all possible combinations, where different elemental, compositional, and surface microstructural features were used as input parameters. Adsorption energy values of CO<sub>2</sub> reduction intermediates on the CuCoNiZnMg- and CuCoNiZnSn-based catalysts have been used to train the ML models. Successful prediction of adsorption energies of the adsorbates using CuCoNiZnMg-based training data is achieved except for two intermediates. Hence, we show that activity and selectivity of these catalysts can be successfully predicted for CO<sub>2</sub> hydrogenation to methanol and have screened a series of high entropy-based catalysts (from 36750 considered catalysts) which could be promising for methanol synthesis. Catalysis ; Methanol ; Adsorption ; Alloy ; Selectivity ; Entropy (arrow of time) ; Materials science ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Thermodynamics ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; High Entropy Alloys Studies ; Advanced materials and composites
Authors: Ryan Lee | Erwin A. B. Mulder | Jonathan B. Hopkins
Source: Science Robotics (article, 2022-10-19)
Field: Materials Science — Polymers and Plastics

Mechanical neural networks: Architected materials that learn behaviors Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned behaviors in the midst of changing conditions (e.g., rising levels of internal damage, varying fixturing scenarios, and fluctuating external loads) while also acquiring new behaviors best suited for the situation at hand. Here, we describe a class of architected materials, called mechanical neural networks (MNNs), that achieve such learning capabilities by tuning the stiffness of their constituent beams similar to how artificial neural networks (ANNs) tune their weights. An example lattice was fabricated to demonstrate its ability to learn multiple mechanical behaviors simultaneously, and a study was conducted to determine the effect of lattice size, packing configuration, algorithm type, behavior number, and linear-versus-nonlinear stiffness tunability on MNN learning as proposed. Thus, this work lays the foundation for artificial-intelligent (AI) materials that can learn behaviors and properties. Artificial neural network ; Stiffness ; Nonlinear system ; Computer science ; Lattice (music) ; Artificial intelligence ; Aside ; Materials science ; Structural engineering ; Engineering ; Acoustics ; Physics Polymer composites and self-healing ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Constantine Tsounis | Priyank V. Kumar | Hassan Masood | Rutvij Pankaj Kulkarni | Gopalakrishnan Sai Gautam | Christoph R. Müller | Rose Amal | Denis A. Kuznetsov
Source: Angewandte Chemie International Edition (review, 2022-10-24)
Field: Materials Science — Materials Chemistry

Advancing MXene Electrocatalysts for Energy Conversion Reactions: Surface, Stoichiometry, and Stability Abstract MXenes, due to their tailorable chemistry and favourable physical properties, have great promise in electrocatalytic energy conversion reactions. To exploit fully their enormous potential, further advances specific to electrocatalysis revolving around their performance, stability, compositional discovery and synthesis are required. The most recent advances in these aspects are discussed in detail: surface functional and stoichiometric modifications which can improve performance, Pourbaix stability related to their electrocatalytic operating conditions, density functional theory and advances in machine learning for their discovery, and prospects in large scale synthesis and solution processing techniques to produce membrane electrode assemblies and integrated electrodes. This Review provides a perspective that is complemented by new density functional theory calculations which show how these recent advances in MXene material design are paving the way for effective electrocatalysts required for the transition to integrated renewable energy systems. MXenes ; Electrocatalyst ; Density functional theory ; Nanotechnology ; Pourbaix diagram ; Materials science ; Energy transformation ; Computer science ; Chemistry ; Electrode ; Electrochemistry ; Computational chemistry ; Physical chemistry ; Physics ; Thermodynamics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Jaehong Park | Youngseon Shim | Franklin Lee | Aravind Rammohan | Sushmit Goyal | Mun‐Bo Shim | Changwook Jeong | Dae Sin Kim
Source: ACS Polymers Au (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (<i>T</i> <sub>g</sub>), melting temperature (<i>T</i> <sub>m</sub>), density (ρ), and elastic modulus (<i>E</i>) with substantial dependence on the dataset, which is the best for <i>T</i> <sub>g</sub> (<i>R</i> <sup>2</sup> ∼ 0.9) and worst for <i>E</i> (<i>R</i> <sup>2</sup> ∼ 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with <i>T</i> <sub>g</sub>, as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property. Polymer ; Computer science ; Biological system ; Graph ; Elastic net regularization ; Subspace topology ; Dimensionality reduction ; Artificial intelligence ; Materials science ; Convolutional neural network ; Representation (politics) ; Algorithm ; Theoretical computer science ; Feature selection Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties