jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lowik Chanussot | Abhishek Das | Siddharth Goyal | Thibaut Lavril | Muhammed Shuaibi | Morgane Rivière | Kevin Tran | Javier Heras‐Domingo | Caleb Ho | Weihua Hu | Aini Palizhati | Anuroop Sriram | Brandon M. Wood | Junwoong Yoon | Devi Parikh | C. Lawrence Zitnick | Zachary W. Ulissi
Source: ACS Catalysis (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

Open Catalyst 2020 (OC20) Dataset and Community Challenges Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuel synthesis, long-term energy storage, and renewable fertilizer production. Despite considerable effort by the catalysis community to apply machine learning models to the computational catalyst discovery process, it remains an open challenge to build models that can generalize across both elemental compositions of surfaces and adsorbate identity/configurations, perhaps because datasets have been smaller in catalysis than in related fields. To address this, we developed the OC20 dataset, consisting of 1,281,040 density functional theory (DFT) relaxations (∼264,890,000 single-point evaluations) across a wide swath of materials, surfaces, and adsorbates (nitrogen, carbon, and oxygen chemistries). We supplemented this dataset with randomly perturbed structures, short timescale molecular dynamics, and electronic structure analyses. The dataset comprises three central tasks indicative of day-to-day catalyst modeling and comes with predefined train/validation/test splits to facilitate direct comparisons with future model development efforts. We applied three state-of-the-art graph neural network models (CGCNN, SchNet, and DimeNet++) to each of these tasks as baseline demonstrations for the community to build on. In almost every task, no upper limit on model size was identified, suggesting that even larger models are likely to improve on initial results. The dataset and baseline models are both provided as open resources as well as a public leader board to encourage community contributions to solve these important tasks. Baseline (sea) ; Computer science ; Task (project management) ; Renewable energy ; Catalysis ; Process (computing) ; Data science ; Chemistry ; Engineering ; Systems engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Jie Hu | Sankhyabrata Bandyopadhyay | Yuhui Liu | Liyang Shao
Source: Frontiers in Physics (review, 2021-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Review on Metasurface: From Principle to Smart Metadevices Metamaterials are composed of periodic subwavelength metallic/dielectric structures that resonantly couple to the electric and magnetic fields of the incident electromagnetic waves, exhibiting unprecedented properties which are most typical within the context of the electromagnetic domain. However, the practical application of metamaterials is found challenging due to the high losses, strong dispersion associated with the resonant responses, and the difficulty in the fabrication of nanoscale 3D structures. The optical metasurface is termed as 2D metamaterials that inherent all of the properties of metamaterials and also provide a solution to the limitation of the conventional metamaterials. Over the past few years, metasurfaces; have been employed for the design and fabrication of optical elements and systems with abilities that surpass the performance of conventional diffractive optical elements. Metasurfaces can be fabricated using standard lithography and nanoimprinting methods, which is easier campared to the fabrication of the counterpart 3 days metamaterials. In this review article, the progress of the research on metasurfaces is illustrated. Concepts of anomalous reflection and refraction, applications of metasurfaces with the Pancharatanm-Berry Phase, and Huygens metasurface are discussed. The development of soft metasurface opens up a new dimension of application zone in conformal or wearable photonics. The progress of soft metasurface has also been discussed in this review. Meta-devices that are being developed with the principle of the shaping of wavefronts are elucidated in this review. Furthermore, it has been established that properties of novel optical metasurface can be modulated by the change in mechanical, electrical, or optical stimuli which leads to the development of dynamic metasurface. Research thrusts over the area of tunable metasurface has been reviewed in this article. Over the recent year, it has been found that optical fibers and metasurface are coagulated for the development of optical devices with the advantages of both domains. The metasurface with lab-on fiber-based devices is being discussed in this review paper. Finally, research trends, challenges, and future scope of the work are summarized in the conclusion part of the article.‬‬‬‬‬‬‬ Metamaterial ; Wavefront ; Negative refraction ; Lithography ; Fabrication ; Photonic metamaterial ; Photonics ; Optics ; Context (archaeology) ; Physics ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Yongyu Liu | Amin Zhang | Ruhao Wang | Qian Zhang | Daxiang Cui
Source: Nano-Micro Letters (review, 2021-07-09)
Field: Materials Science — Materials Chemistry

A Review on Metal- and Metal Oxide-Based Nanozymes: Properties, Mechanisms, and Applications Since the ferromagnetic (Fe<sub>3</sub>O<sub>4</sub>) nanoparticles were firstly reported to exert enzyme-like activity in 2007, extensive research progress in nanozymes has been made with deep investigation of diverse nanozymes and rapid development of related nanotechnologies. As promising alternatives for natural enzymes, nanozymes have broadened the way toward clinical medicine, food safety, environmental monitoring, and chemical production. The past decade has witnessed the rapid development of metal- and metal oxide-based nanozymes owing to their remarkable physicochemical properties in parallel with low cost, high stability, and easy storage. It is widely known that the deep study of catalytic activities and mechanism sheds significant influence on the applications of nanozymes. This review digs into the characteristics and intrinsic properties of metal- and metal oxide-based nanozymes, especially emphasizing their catalytic mechanism and recent applications in biological analysis, relieving inflammation, antibacterial, and cancer therapy. We also conclude the present challenges and provide insights into the future research of nanozymes constituted of metal and metal oxide nanomaterials. Nanotechnology ; Nanomaterials ; Oxide ; Metal ; Materials science ; Metallurgy Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Kamal Choudhary | Brian DeCost
Source: npj Computational Materials (article, 2021-11-15)
Field: Materials Science — Materials Chemistry

Atomistic Line Graph Neural Network for improved materials property predictions Abstract Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles. We present an Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond angles. We demonstrate that angle information can be explicitly and efficiently included, leading to improved performance on multiple atomistic prediction tasks. We ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT, Materials project, and QM9 databases. ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks with better or comparable model training speed. Computer science ; Artificial neural network ; Graph ; Bond graph ; Representation (politics) ; Line (geometry) ; Theoretical computer science ; Property (philosophy) ; Algorithm ; Artificial intelligence ; Mathematics ; Combinatorics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Gus L. W. Hart | Tim Mueller | Cormac Toher | Stefano Curtarolo
Source: Nature Reviews Materials (review, 2021-07-20)
Field: Materials Science — Materials Chemistry

Machine learning for alloys Superalloy ; Machine learning ; Artificial intelligence ; Computer science ; The Renaissance ; Materials science ; Alloy ; Mechanical engineering ; Metallurgy ; Engineering Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Materials Characterization Techniques
Authors: Arbab Ali | Tufail Shah | Rehmat Ullah | Pingfan Zhou | Manlin Guo | Muhammad Ovais | Zhiqiang Tan | Yukui Rui
Source: Frontiers in Chemistry (review, 2021-07-13)
Field: Materials Science — Materials Chemistry

Review on Recent Progress in Magnetic Nanoparticles: Synthesis, Characterization, and Diverse Applications Diverse applications of nanoparticles (NPs) have revolutionized various sectors in society. In the recent decade, particularly magnetic nanoparticles (MNPs) have gained enormous interest owing to their applications in specialized areas such as medicine, cancer theranostics, biosensing, catalysis, agriculture, and the environment. Controlled surface engineering for the design of multi-functional MNPs is vital for achieving desired application. The MNPs have demonstrated great efficacy as thermoelectric materials, imaging agents, drug delivery vehicles, and biosensors. In the present review, first we have briefly discussed main synthetic methods of MNPs, followed by their characterizations and composition. Then we have discussed the potential applications of MNPs in different with representative examples. At the end, we gave an overview on the current challenges and future prospects of MNPs. This comprehensive review not only provides the mechanistic insight into the synthesis, functionalization, and application of MNPs but also outlines the limits and potential prospects. Nanotechnology ; Magnetic nanoparticles ; Characterization (materials science) ; Biosensor ; Drug delivery ; Surface modification ; Nanoparticle ; Materials science ; Biochemical engineering ; Engineering ; Chemical engineering Nanocluster Synthesis and Applications ; Nanomaterials for catalytic reactions ; Quantum Dots Synthesis And Properties
Authors: Chaobo Liang | Zhoujie Gu | Yali Zhang | Zhonglei Ma | Hua Qiu | Junwei Gu
Source: Nano-Micro Letters (review, 2021-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural Design Strategies of Polymer Matrix Composites for Electromagnetic Interference Shielding: A Review With the widespread application of electronic communication technology, the resulting electromagnetic radiation pollution has been significantly increased. Metal matrix electromagnetic interference (EMI) shielding materials have disadvantages such as high density, easy corrosion, difficult processing and high price, etc. Polymer matrix EMI shielding composites possess light weight, corrosion resistance and easy processing. However, the current polymer matrix composites present relatively low electrical conductivity and poor EMI shielding performance. This review firstly discusses the key concept, loss mechanism and test method of EMI shielding. Then the current development status of EMI shielding materials is summarized, and the research progress of polymer matrix EMI shielding composites with different structures is illustrated, especially for their preparation methods and evaluation. Finally, the corresponding key scientific and technical problems are proposed, and their development trend is also prospected. EMI ; Electromagnetic shielding ; Electromagnetic interference ; Materials science ; Composite material ; Corrosion ; Matrix (chemical analysis) ; Polymer ; Interference (communication) ; Mechanical engineering ; Electronic engineering ; Electrical engineering ; Engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Mohammad Mehdi Sabzehmeidani | Sahar Mahnaee | Mehrorang Ghaedi | Hadi Heidari | Vellaisamy A. L. Roy
Source: Materials Advances (review, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carbon based materials: a review of adsorbents for inorganic and organic compounds This review presents the adsorptive removal process of hazardous materials onto carbon-based materials comprising activated carbon, graphene, carbon nanotubes, carbon nanofibers, biochar and carbon aerogels. Carbon fibers ; Adsorption ; Materials science ; Environmental chemistry ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Engineering Supercapacitor Materials and Fabrication ; Graphene research and applications ; Aerogels and thermal insulation
Authors: Amritanand Sebastian | Rahul Pendurthi | Tanushree H. Choudhury | Joan M. Redwing | Saptarshi Das
Source: Nature Communications (article, 2021-01-29)
Field: Materials Science — Materials Chemistry

Benchmarking monolayer MoS2 and WS2 field-effect transistors Here we benchmark device-to-device variation in field-effect transistors (FETs) based on monolayer MoS<sub>2</sub> and WS<sub>2</sub> films grown using metal-organic chemical vapor deposition process. Our study involves 230 MoS<sub>2</sub> FETs and 160 WS<sub>2</sub> FETs with channel lengths ranging from 5 μm down to 100 nm. We use statistical measures to evaluate key FET performance indicators for benchmarking these two-dimensional (2D) transition metal dichalcogenide (TMD) monolayers against existing literature as well as ultra-thin body Si FETs. Our results show consistent performance of 2D FETs across 1 × 1 cm<sup>2</sup> chips owing to high quality and uniform growth of these TMDs followed by clean transfer onto device substrates. We are able to demonstrate record high carrier mobility of 33 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> in WS<sub>2</sub> FETs, which is a 1.5X improvement compared to the best reported in the literature. Our experimental demonstrations confirm the technological viability of 2D FETs in future integrated circuits. Monolayer ; Field-effect transistor ; Materials science ; Transistor ; Benchmarking ; Chemical vapor deposition ; Molybdenum disulfide ; Optoelectronics ; Nanotechnology ; Electronic circuit ; Electron mobility ; Electrical engineering ; Voltage 2D Materials and Applications ; Advancements in Semiconductor Devices and Circuit Design ; Nanowire Synthesis and Applications
Authors: Yanhua Jia | Qinglin Jiang | Hengda Sun | Peipei Liu | Dehua Hu | Yanzhong Pei | Weishu Liu | Xavier Crispin | Simone Fabiano | Yuguang Ma | Yong Cao
Source: Advanced Materials (review, 2021-09-05)
Field: Materials Science — Materials Chemistry

Wearable Thermoelectric Materials and Devices for Self‐Powered Electronic Systems The emergence of artificial intelligence and the Internet of Things has led to a growing demand for wearable and maintenance-free power sources. The continual push toward lower operating voltages and power consumption in modern integrated circuits has made the development of devices powered by body heat finally feasible. In this context, thermoelectric (TE) materials have emerged as promising candidates for the effective conversion of body heat into electricity to power wearable devices without being limited by environmental conditions. Driven by rapid advances in processing technology and the performance of TE materials over the past two decades, wearable thermoelectric generators (WTEGs) have gradually become more flexible and stretchable so that they can be used on complex and dynamic surfaces. In this review, the functional materials, processing techniques, and strategies for the device design of different types of WTEGs are comprehensively covered. Wearable self-powered systems based on WTEGs are summarized, including multi-function TE modules, hybrid energy harvesting, and all-in-one energy devices. Challenges in organic TE materials, interfacial engineering, and assessments of device performance are discussed, and suggestions for future developments in the area are provided. This review will promote the rapid implementation of wearable TE materials and devices in self-powered electronic systems. Wearable technology ; Wearable computer ; Energy harvesting ; Context (archaeology) ; Electronics ; Electricity ; Thermoelectric generator ; Materials science ; Computer science ; Thermoelectric effect ; Nanotechnology ; Power (physics) ; Electrical engineering ; Embedded system ; Engineering Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Pascal Friederich | Florian Häse | Jonny Proppe | Alán Aspuru‐Guzik
Source: Nature Materials (review, 2021-05-27)
Field: Materials Science — Materials Chemistry

Machine-learned potentials for next-generation matter simulations Computer science ; Bridging (networking) ; Computation ; Field (mathematics) ; Computational model ; Process (computing) ; Artificial intelligence ; Machine learning ; Nanotechnology ; Computational science ; Data science ; Algorithm ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: Morten Niklas Gjerding | Alireza Taghizadeh | Asbjørn Rasmussen | Sajid Ali | Fabian Bertoldo | Thorsten Deilmann | Nikolaj Rørbæk Knøsgaard | Mads Kruse | Ask Hjorth Larsen | Simone Manti | Thomas Garm Pedersen | Urko Petralanda | Thorbjørn Skovhus | Mark Kamper Svendsen | Jens Jørgen Mortensen | Thomas Olsen | Kristian Sommer Thygesen
Source: 2D Materials (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

Recent progress of the Computational 2D Materials Database (C2DB) Abstract The Computational 2D Materials Database (C2DB) is a highly curated open database organising a wealth of computed properties for more than 4000 atomically thin two-dimensional (2D) materials. Here we report on new materials and properties that were added to the database since its first release in 2018. The set of new materials comprise several hundred monolayers exfoliated from experimentally known layered bulk materials, (homo)bilayers in various stacking configurations, native point defects in semiconducting monolayers, and chalcogen/halogen Janus monolayers. The new properties include exfoliation energies, Bader charges, spontaneous polarisations, Born charges, infrared polarisabilities, piezoelectric tensors, band topology invariants, exchange couplings, Raman spectra and second harmonic generation spectra. We also describe refinements of the employed material classification schemes, upgrades of the computational methodologies used for property evaluations, as well as significant enhancements of the data documentation and provenance. Finally, we explore the performance of Gaussian process-based regression for efficient prediction of mechanical and electronic materials properties. The combination of open access, detailed documentation, and extremely rich materials property data sets make the C2DB a unique resource that will advance the science of atomically thin materials. Stacking ; Property (philosophy) ; Raman spectroscopy ; Set (abstract data type) ; Janus ; Point (geometry) 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Mingchao Wang | Renhao Dong⧫ | Xinliang Feng
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Two-dimensional conjugated metal–organic frameworks (2D <i>c</i>-MOFs): chemistry and function for MOFtronics The 21st century has seen a reinvention of how modern electronics impact our daily lives; silicon-electronics and organic electronics are currently at the core of modern electronics. Recent advances have demonstrated that conductive metal-organic frameworks (MOFs), as another unique class of electronic materials, are emerging to provide additional possibility for multifunctional electronic devices that brings us "MOFtronics". Typically, two-dimensional conjugated MOFs (2D c-MOFs) are a novel class of layer-stacked MOFs with in-plane extended π-conjugation that exhibit unique properties such as intrinsic porosity, crystallinity, stability, and electrical conductivity as well as tailorable band gaps. Benefiting from their unique features and high conductivity, 2D c-MOFs have displayed great potential for multiple high-performance (opto)electronic, magnetic, and energy devices. In this review article, we introduce the chemical and synthetic methodologies of 2D c-MOFs, intrinsic influences on their electronic structures and charge transport properties, as well as multifunctional applications of this class of materials for MOFtronics and potential power sources for MOFtronics. We highlight the benefits and limitations of thus-far developed 2D c-MOFs from synthesis to function and offer our perspectives in regard to the challenges to be addressed. Spintronics ; Conjugated system ; Nanotechnology ; Metal-organic framework ; Electronics ; Chemistry ; Function (biology) ; Organic electronics ; Materials science ; Physics ; Organic chemistry ; Transistor ; Physical chemistry ; Polymer ; Ferromagnetism Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Graphene research and applications
Authors: Tsz Wai Ko | Jonas A. Finkler | Stefan Goedecker | Jörg Behler
Source: Nature Communications (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations. Charge (physics) ; Electronegativity ; Electrostatics ; Artificial neural network ; Statistical physics ; Range (aeronautics) ; Computer science ; Electronic structure ; Work (physics) ; Partial charge ; Physics ; Chemical physics ; Nanotechnology ; Computational physics ; Biological system ; Materials science ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electronic and Structural Properties of Oxides