jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Turab Lookman | Prasanna V. Balachandran | Dezhen Xue | Ruihao Yuan
Source: npj Computational Materials (article, 2019-02-18)
Field: Materials Science — Materials Chemistry

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design Abstract One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics. Computer science ; Data science ; Field (mathematics) ; Process (computing) ; Informatics ; Space (punctuation) ; Management science ; Machine learning ; Engineering Machine Learning in Materials Science ; Machine Learning and Algorithms ; Computational Drug Discovery Methods
Authors: Lauri Himanen | Amber Geurts | Adam Stuart Foster | Patrick Rinke
Source: Advanced Science (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Data‐Driven Materials Science: Status, Challenges, and Perspectives Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning-typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field. Variety (cybernetics) ; Big data ; Perspective (graphical) ; Key (lock) ; State (computer science) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Kaichen Xu | Rui Zhou | Kuniharu Takei | Minghui Hong
Source: Advanced Science (review, 2019-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Toward Flexible Surface‐Enhanced Raman Scattering (SERS) Sensors for Point‐of‐Care Diagnostics Surface-enhanced Raman scattering (SERS) spectroscopy provides a noninvasive and highly sensitive route for fingerprint and label-free detection of a wide range of molecules. Recently, flexible SERS has attracted increasingly tremendous research interest due to its unique advantages compared to rigid substrate-based SERS. Here, the latest advances in flexible substrate-based SERS diagnostic devices are investigated in-depth. First, the intriguing prospect of point-of-care diagnostics is briefly described, followed by an introduction to the cutting-edge SERS technique. Then, the focus is moved from conventional rigid substrate-based SERS to the emerging flexible SERS technique. The main part of this report highlights the recent three categories of flexible SERS substrates, including actively tunable SERS, swab-sampling strategy, and the in situ SERS detection approach. Furthermore, other promising means of flexible SERS are also introduced. The flexible SERS substrates with low-cost, batch-fabrication, and easy-to-operate characteristics can be integrated into portable Raman spectroscopes for point-of-care diagnostics, which are conceivable to penetrate global markets and households as next-generation wearable sensors in the near future. Nanotechnology ; Raman scattering ; Substrate (aquarium) ; Materials science ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Point of care ; Computer science ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Ryosuke Jinnouchi | Jonathan Lahnsteiner | Ferenc Karsai | Georg Kresse | Menno Bokdam
Source: Physical Review Letters (article, 2019-06-07)
Field: Materials Science — Materials Chemistry

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding years of computing time. Here we present an on-the-fly machine learning scheme that generates force fields automatically during molecular dynamics simulations. This opens up the required time and length scales, while retaining the distinctive chemical precision of first principles methods and minimizing the need for human intervention. The method is widely applicable to multielement complex systems. We demonstrate its predictive power on the entropy driven phase transitions of hybrid perovskites, which have never been accurately described in simulations. Using machine learned potentials, isothermal-isobaric simulations give direct insight into the underlying microscopic mechanisms. Finally, we relate the phase transition temperatures of different perovskites to the radii of the involved species, and we determine the order of the transitions in Landau theory. Phase transition ; Statistical physics ; Computer science ; Inference ; Molecular dynamics ; Bayesian probability ; Bayesian inference ; Force field (fiction) ; Entropy (arrow of time) ; Artificial intelligence ; Machine learning ; Physics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Sepehr Talebian | Mehdi Mehrali | Nayere Taebnia | Cristian Pablo Pennisi | Firoz Babu Kadumudi | Javad Foroughi | Masoud Hasany | Mehdi Nikkhah | Mohsen Akbari | Gorka Orive | Alireza Dolatshahi‐Pirouz
Source: Advanced Science (review, 2019-06-14)
Field: Materials Science — Biomaterials

Self‐Healing Hydrogels: The Next Paradigm Shift in Tissue Engineering? Given their durability and long-term stability, self-healable hydrogels have, in the past few years, emerged as promising replacements for the many brittle hydrogels currently being used in preclinical or clinical trials. To this end, the incompatibility between hydrogel toughness and rapid self-healing remains unaddressed, and therefore most of the self-healable hydrogels still face serious challenges within the dynamic and mechanically demanding environment of human organs/tissues. Furthermore, depending on the target tissue, the self-healing hydrogels must comply with a wide range of properties including electrical, biological, and mechanical. Notably, the incorporation of nanomaterials into double-network hydrogels is showing great promise as a feasible way to generate self-healable hydrogels with the above-mentioned attributes. Here, the recent progress in the development of multifunctional and self-healable hydrogels for various tissue engineering applications is discussed in detail. Their potential applications within the rapidly expanding areas of bioelectronic hydrogels, cyborganics, and soft robotics are further highlighted. Self-healing hydrogels ; Tissue engineering ; Paradigm shift ; Self-healing ; Biomedical engineering ; Nanotechnology ; Computer science ; Materials science ; Engineering ; Chemical engineering ; Medicine ; Pathology ; Physics Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Hydrogels: synthesis, properties, applications
Authors: Adam C. Mater | Michelle L. Coote
Source: Journal of Chemical Information and Modeling (review, 2019-06-13)
Field: Materials Science — Materials Chemistry

Deep Learning in Chemistry Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry to drug and materials design and even synthesis planning. This review aims to explain the concepts of deep learning to chemists from any background and follows this with an overview of the diverse applications demonstrated in the literature. We hope that this will empower the broader chemical community to engage with this burgeoning field and foster the growing movement of deep learning accelerated chemistry. Deep learning ; Variety (cybernetics) ; Artificial intelligence ; Computer science ; Data science ; Field (mathematics) ; Cognitive science ; Chemistry ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Takashi Toyao | Zen Maeno | Satoru Takakusagi | Takashi Kamachi | Ichigaku Takigawa | Ken‐ichi Shimizu
Source: ACS Catalysis (article, 2019-12-16)
Field: Materials Science — Materials Chemistry

Machine Learning for Catalysis Informatics: Recent Applications and Prospects The discovery and development of catalysts and catalytic processes are essential components to maintaining an ecological balance in the future. Recent revolutions made in data science could have a great impact on traditional catalysis research in both industry and academia and could accelerate the development of catalysts. Machine learning (ML), a subfield of data science, can play a central role in this paradigm shift away from the use of traditional approaches. In this review, we present a user's guide for ML that we believe will be helpful for scientists performing research in the field of catalysis and summarize recent progress that has been made in utilizing ML to create homogeneous and heterogeneous catalysts. The focus of the review is on the design, synthesis, and characterization of catalytic materials/compounds as well as their applications to catalyzed processes. The ML technique not only enhances ways to discover catalysts but also serves as a powerful tool to establish a deeper understanding of relationships between the properties of materials/compounds and their catalytic activities, selectivities, and stabilities. This knowledge facilitates the establishment of principles employed to design catalysts and to enhance their efficiencies. Despite such advantages of ML, it is noteworthly that the current ML-assisted development of real catalysts remains in its infancy, mainly because of the complexity of catalysis associated with the fact that catalysis is a time-dependent dynamic event. In this review, we discuss how seamless integration of experiment, theory, and data science can be used to accelerate catalyst development and to guide future studies aimed at applications that will impact society's need to produce energy, materials, and chemicals. Moreover, the limitations and difficulties of ML in catalysis research originating from the complex nature of catalysis are discussed in order to make the catalysis community aware of challenges that need to be addressed for effective and practical use of ML in the field. Catalysis ; Biochemical engineering ; Nanotechnology ; Computer science ; Field (mathematics) ; Homogeneous ; Characterization (materials science) ; Materials informatics ; Cheminformatics ; Chemistry ; Health informatics ; Materials science ; Engineering ; Physics ; Political science ; Organic chemistry ; Computational chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Zhen-Lin Chen | Jia‐Ming Meng | Yong Cao | Jili Yin | Run-Qian Fang | Sheng‐Bo Fan | Chao Liu | Wenfeng Zeng | Yue‐He Ding | Dan Tan | Long Wu | Wenjing Zhou | Hao Chi | Rui-Xiang Sun | Meng‐Qiu Dong | Si‐Min He
Source: Nature Communications (article, 2019-07-30)
Field: Chemistry — Spectroscopy

A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides We describe pLink 2, a search engine with higher speed and reliability for proteome-scale identification of cross-linked peptides. With a two-stage open search strategy facilitated by fragment indexing, pLink 2 is ~40 times faster than pLink 1 and 3~10 times faster than Kojak. Furthermore, using simulated datasets, synthetic datasets, <sup>15</sup>N metabolically labeled datasets, and entrapment databases, four analysis methods were designed to evaluate the credibility of ten state-of-the-art search engines. This systematic evaluation shows that pLink 2 outperforms these methods in precision and sensitivity, especially at proteome scales. Lastly, re-analysis of four published proteome-scale cross-linking datasets with pLink 2 required only a fraction of the time used by pLink 1, with up to 27% more cross-linked residue pairs identified. pLink 2 is therefore an efficient and reliable tool for cross-linking mass spectrometry analysis, and the systematic evaluation methods described here will be useful for future software development. Proteome ; Computer science ; Computational biology ; Identification (biology) ; Data mining ; Mascot ; Search engine indexing ; Pattern recognition (psychology) ; Bioinformatics ; Artificial intelligence ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Kais Dhbaibi | Ludovic Favereau | Jeanne Crassous
Source: Chemical Reviews (review, 2019-07-11)
Field: Chemistry — Organic Chemistry

Enantioenriched Helicenes and Helicenoids Containing Main-Group Elements (B, Si, N, P) In this review, we discuss the rich chemistry of helicenes and helicenoids containing main-group elements. Enantioenriched helicenic derivatives containing main-group elements B, Si, N, and P, either incorporated within the helical backbone or grafted to it, will be thoroughly presented. We will describe their synthesis, resolution, and asymmetric synthesis, their structural features, electronic and chiroptical properties, and emission, together with other photochemical properties and applications. Chemistry ; Group (periodic table) ; Enantioselective synthesis ; Stereochemistry ; Combinatorial chemistry ; Chiral auxiliary ; Main group element ; Organic chemistry ; Catalysis Synthesis and Properties of Aromatic Compounds ; Photochromic and Fluorescence Chemistry ; Porphyrin and Phthalocyanine Chemistry
Authors: Yifei Zhang | Jeffrey B. Chou | Junying Li | Huashan Li | Qingyang Du | Anupama Yadav | Si Zhou | Mikhail Y. Shalaginov | Zhuoran Fang | Huikai Zhong | Christopher Roberts | Paul Robinson | Bridget Bohlin | Carlos Ríos | Hongtao Lin | Myungkoo Kang | Tian Gu | Jamie Warner | Vladimir Liberman | Kathleen Richardson | Juejun Hu
Source: Nature Communications (article, 2019-09-30)
Field: Materials Science — Materials Chemistry

Broadband transparent optical phase change materials for high-performance nonvolatile photonics Optical phase change materials (O-PCMs), a unique group of materials featuring exceptional optical property contrast upon a solid-state phase transition, have found widespread adoption in photonic applications such as switches, routers and reconfigurable meta-optics. Current O-PCMs, such as Ge-Sb-Te (GST), exhibit large contrast of both refractive index (Δn) and optical loss (Δk), simultaneously. The coupling of both optical properties fundamentally limits the performance of many applications. Here we introduce a new class of O-PCMs based on Ge-Sb-Se-Te (GSST) which breaks this traditional coupling. The optimized alloy, Ge<sub>2</sub>Sb<sub>2</sub>Se<sub>4</sub>Te<sub>1</sub>, combines broadband transparency (1-18.5 μm), large optical contrast (Δn = 2.0), and significantly improved glass forming ability, enabling an entirely new range of infrared and thermal photonic devices. We further demonstrate nonvolatile integrated optical switches with record low loss and large contrast ratio and an electrically-addressed spatial light modulator pixel, thereby validating its promise as a material for scalable nonvolatile photonics. Broadband ; Photonics ; Optical transparency ; Refractive index ; Optical switch ; Contrast ratio ; Optical communication ; High contrast ; Optical engineering ; Phase (matter) Phase-change materials and chalcogenides ; Transition Metal Oxide Nanomaterials ; 2D Materials and Applications
Authors: Lianlin Li | Hengxin Ruan | Che Liu | Ying Li | Ya Shuang | Andrea Alù | Cheng‐Wei Qiu | Tie Jun Cui
Source: Nature Communications (article, 2019-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning reprogrammable metasurface imager Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we experimentally report a real-time digital-metasurface imager that can be trained in-situ to generate the radiation patterns required by machine-learning optimized measurement modes. This imager is electronically reprogrammed in real time to access the optimized solution for an entire data set, realizing storage and transfer of full-resolution raw data in dynamically varying scenes. High-accuracy image coding and recognition are demonstrated in situ for various image sets, including hand-written digits and through-wall body gestures, using a single physical hardware imager, reprogrammed in real time. Our electronically controlled metasurface imager opens new venues for intelligent surveillance, fast data acquisition and processing, imaging at various frequencies, and beyond. Computer science ; Data acquisition ; Computer data storage ; Raw data ; Computer hardware ; Data set ; Artificial intelligence ; Transfer (computing) ; Microwave imaging ; Coding (social sciences) ; Computer vision ; Microwave ; Telecommunications Metamaterials and Metasurfaces Applications ; Microwave Imaging and Scattering Analysis ; Millimeter-Wave Propagation and Modeling
Authors: Haoran Ren | Gauthier Brière | Xinyuan Fang | Peinan Ni | Rajath Sawant | Sébastien Héron | Sébastien Chenot | S. Vézian | B. Damilano | Virginie Brändli | Stefan A. Maier | Patrice Genevet
Source: Nature Communications (article, 2019-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface orbital angular momentum holography Allowing subwavelength-scale-digitization of optical wavefronts to achieve complete control of light at interfaces, metasurfaces are particularly suited for the realization of planar phase-holograms that promise new applications in high-capacity information technologies. Similarly, the use of orbital angular momentum of light as a new degree of freedom for information processing can further improve the bandwidth of optical communications. However, due to the lack of orbital angular momentum selectivity in the design of conventional holograms, their utilization as an information carrier for holography has never been implemented. Here we demonstrate metasurface orbital angular momentum holography by utilizing strong orbital angular momentum selectivity offered by meta-holograms consisting of GaN nanopillars with discrete spatial frequency distributions. The reported orbital angular momentum-multiplexing allows lensless reconstruction of a range of distinctive orbital angular momentum-dependent holographic images. The results pave the way to the realization of ultrahigh-capacity holographic devices harnessing the previously inaccessible orbital angular momentum multiplexing. Orbital angular momentum multiplexing ; Angular momentum ; Holography ; Physics ; Orbital angular momentum of light ; Multiplexing ; Optics ; Digital holography ; Angular momentum of light ; Total angular momentum quantum number ; Angular momentum coupling ; Computer science ; Classical mechanics ; Telecommunications Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Advanced Antenna and Metasurface Technologies
Authors: Davin Tan | Felipe Garcı́a
Source: Chemical Society Reviews (review, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Main group mechanochemistry: from curiosity to established protocols In the last few decades, mechanochemistry has become rapidly established as a powerful tool enabling environmentally-benign and sustainable chemical syntheses. Not only have these techniques been demonstrated as viable alternatives to traditional solution-based syntheses, but they have also received attention for their ability to enable new reactivity and "unlocking" novel compounds inaccessible by conventional methods. Reflecting the rising popularity of mechanochemistry, many excellent reviews highlighting its benefits have recently been published. Whilst the scope of most of these focuses on organic chemistry, transition-metal catalysis, porous framework materials, coordination compounds and supramolecular synthesis, few have addressed the use of mechanochemical ball milling for the synthesis of compounds containing s- and p-block elements. This tutorial review turns the spotlight towards mechanochemical research in the field of inorganic main group chemistry, highlighting significant advantages that solid-state inorganic reactions often possess, and the potential for these to drive the development of greener methodologies within the modern main group arena. Mechanochemistry ; Scope (computer science) ; Nanotechnology ; Supramolecular chemistry ; Chemistry ; Material chemistry ; Coordination complex ; Mechanosynthesis ; Ball mill ; Biochemical engineering ; Green chemistry ; Materials science ; Engineering ; Organic chemistry ; Computer science ; Chemical engineering ; Metal ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography