jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Grigorii Skorupskii | Benjamin A. Trump | Thomas W. Kasel | Craig M. Brown | Christopher H. Hendon | Mircea Dincă
Source: Nature Chemistry (article, 2019-11-25)
Field: Chemistry — Inorganic Chemistry

Efficient and tunable one-dimensional charge transport in layered lanthanide metal–organic frameworks Lanthanide ; Stacking ; Chemistry ; Covalent bond ; Metal-organic framework ; Ligand (biochemistry) ; Ion ; Metal ; Nanotechnology ; Chemical physics ; Materials science ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Condensed Matter Physics
Authors: Thomas J. Clough | Lijun Jiang | Ka‐Leung Wong | Nicholas J. Long
Source: Nature Communications (review, 2019-03-29)
Field: Materials Science — Materials Chemistry

Ligand design strategies to increase stability of gadolinium-based magnetic resonance imaging contrast agents Gadolinium(III) complexes have been widely utilised as magnetic resonance imaging (MRI) contrast agents for decades. In recent years however, concerns have developed about their toxicity, believed to derive from demetallation of the complexes in vivo, and the relatively large quantities of compound required for a successful scan. Recent efforts have sought to enhance the relaxivity of trivalent gadolinium complexes without sacrificing their stability. This review aims to examine the strategic design of ligands synthesised for this purpose, provide an overview of recent successes in gadolinium-based contrast agent development and assess the requirements for clinical translation. Gadolinium ; Magnetic resonance imaging ; Contrast (vision) ; Ligand (biochemistry) ; In vivo ; Nuclear magnetic resonance ; Medicine ; Chemistry ; Radiology ; Computer science ; Internal medicine ; Receptor ; Organic chemistry ; Biotechnology ; Biology ; Physics ; Artificial intelligence Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Magnetism in coordination complexes
Authors: Nishant Pappireddi | Lance Martin | Martin Wühr
Source: ChemBioChem (review, 2019-01-04)
Field: Chemistry — Spectroscopy

A Review on Quantitative Multiplexed Proteomics Over the last few decades, mass spectrometry-based proteomics has become an increasingly powerful tool that is now able to routinely detect and quantify thousands of proteins. A major advance for global protein quantification was the introduction of isobaric tags, which, in a single experiment, enabled the global quantification of proteins across multiple samples. Herein, these methods are referred to as multiplexed proteomics. The principles, advantages, and drawbacks of various multiplexed proteomics techniques are discussed and compared with alternative approaches. We also discuss how the emerging combination of multiplexing with targeted proteomics might enable the reliable and high-quality quantification of very low abundance proteins across multiple conditions. Lastly, we suggest that fusing multiplexed proteomics with data-independent acquisition approaches might enable the comparison of hundreds of different samples without missing values, while maintaining the superb measurement precision and accuracy obtainable with isobaric tag quantification. Proteomics ; Quantitative proteomics ; Isobaric labeling ; Multiplexing ; Label-free quantification ; Computational biology ; Computer science ; Tandem mass tag ; Biology ; Telecommunications Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Zaichun Liu | Xinhai Yuan | Shuaishuai Zhang | Jing Wang | Qinghong Huang | Nengfei Yu | Yusong Zhu | Lijun Fu | Faxing Wang | Yuhui Chen | Yuping Wu
Source: NPG Asia Materials (article, 2019-03-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Three-dimensional ordered porous electrode materials for electrochemical energy storage Abstract The past decade has witnessed substantial advances in the synthesis of various electrode materials with three-dimensional (3D) ordered macroporous or mesoporous structures (the so-called “inverse opals”) for applications in electrochemical energy storage devices. This review summarizes recent advancements in 3D ordered porous (3DOP) electrode materials and their unusual electrochemical properties endowed by their intrinsic and geometric structures. The 3DOP electrode materials discussed here mainly include carbon materials, transition metal oxides (such as TiO 2 , SnO 2 , Co 3 O 4 , NiO, Fe 2 O 3 , V 2 O 5 , Cu 2 O, MnO 2 , and GeO 2 ), transition metal dichalcogenides (such as MoS 2 and WS 2 ), elementary substances (such as Si, Ge, and Au), intercalation compounds (such as Li 4 Ti 5 O 12 , LiCoO 2 , LiMn 2 O 4 , LiFePO 4 ), and conductive polymers (polypyrrole and polyaniline). Representative applications of these materials in Li ion batteries, aqueous rechargeable lithium batteries, Li-S batteries, Li-O 2 batteries, and supercapacitors are presented. Particular focus is placed on how ordered porous structures influence the electrochemical performance of electrode materials. Additionally, we discuss research opportunities as well as the current challenges to facilitate further contributions to this emerging research frontier. Materials science ; Supercapacitor ; Mesoporous material ; Nanotechnology ; Electrode ; Electrochemistry ; Energy storage ; Lithium (medication) ; Nanocomposite ; Transition metal ; Polyaniline ; Electrochemical energy storage ; Porosity ; Graphene ; Chemical engineering ; Polymer ; Composite material ; Catalysis Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Transition Metal Oxide Nanomaterials
Authors: A. Filipa Almeida | Rui Moreira | Tiago Rodrigues
Source: Nature Reviews Chemistry (review, 2019-08-21)
Field: Materials Science — Materials Chemistry

Synthetic organic chemistry driven by artificial intelligence Artificial intelligence ; Computer science ; Chemistry ; Synthetic biology ; Nanotechnology ; Data science ; Bioinformatics ; Materials science ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yi Wang | Kang Zhang | Xian Qin | Tianhan Li | Juhui Qiu | Tieying Yin | Junli Huang | Sean McGinty | Giuseppe Pontrelli | Jun Ren | Qiwei Wang | Wei Wu | Guixue Wang
Source: Advanced Science (article, 2019-04-24)
Field: Materials Science — Biomaterials

Biomimetic Nanotherapies: Red Blood Cell Based Core–Shell Structured Nanocomplexes for Atherosclerosis Management Abstract Cardiovascular disease is the leading cause of mortality worldwide. Atherosclerosis, one of the most common forms of the disease, is characterized by a gradual formation of atherosclerotic plaque, hardening, and narrowing of the arteries. Nanomaterials can serve as powerful delivery platforms for atherosclerosis treatment. However, their therapeutic efficacy is substantially limited in vivo due to nonspecific clearance by the mononuclear phagocytic system. In order to address this limitation, rapamycin (RAP)‐loaded poly(lactic‐ co ‐glycolic acid) (PLGA) nanoparticles are cloaked with the cell membrane of red blood cells (RBCs), creating superior nanocomplexes with a highly complex functionalized bio‐interface. The resulting biomimetic nanocomplexes exhibit a well‐defined “core–shell” structure with favorable hydrodynamic size and negative surface charge. More importantly, the biomimetic nature of the RBC interface results in less macrophage‐mediated phagocytosis in the blood and enhanced accumulation of nanoparticles in the established atherosclerotic plaques, thereby achieving targeted drug release. The biomimetic nanocomplexes significantly attenuate the progression of atherosclerosis. Additionally, the biomimetic nanotherapy approach also displays favorable safety properties. Overall, this study demonstrates the therapeutic advantages of biomimetic nanotherapy for atherosclerosis treatment, which holds considerable promise as a new generation of drug delivery system for safe and efficient management of atherosclerosis. Drug delivery ; PLGA ; In vivo ; Materials science ; Nanotechnology ; Phagocytosis ; Macrophage ; Nanoparticle ; Chemistry ; Medicine ; Immunology ; In vitro ; Biology ; Biochemistry Nanoparticle-Based Drug Delivery ; Extracellular vesicles in disease ; Blood properties and coagulation
Authors: Dipendra Jha | Kamal Choudhary | Francesca Tavazza | Wei‐keng Liao | Alok Choudhary | Carelyn E. Campbell | Ankit Agrawal
Source: Nature Communications (article, 2019-11-22)
Field: Materials Science — Materials Chemistry

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of [Formula: see text] observations, the proposed approach yields a mean absolute error (MAE) of [Formula: see text] eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself. Computation ; Computer science ; Density functional theory ; Set (abstract data type) ; Property (philosophy) ; Process (computing) ; Approximation error ; Data set ; Experimental data ; Algorithm ; Artificial intelligence ; Machine learning ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Wu‐Xing Zhou | Yuan Cheng | Ke‐Qiu Chen | Guofeng Xie | Tian Wang | Gang Zhang
Source: Advanced Functional Materials (article, 2019-09-09)
Field: Materials Science — Materials Chemistry

Thermal Conductivity of Amorphous Materials Abstract Thermal conductivity is one of the most fundamental properties of solid materials. The thermal conductivity of ideal crystal materials has been widely studied over the past hundreds years. On the contrary, for amorphous materials that have valuable applications in flexible electronics, wearable electrics, artificial intelligence chips, thermal protection, advanced detectors, thermoelectrics, and other fields, their thermal properties are relatively rarely reported. Moreover, recent research indicates that the thermal conductivity of amorphous materials is quite different from that of ideal crystal materials. In this article, the authors systematically review the fundamental physical aspects of thermal conductivity in amorphous materials. They discuss the method to distinguish the different heat carriers (propagons, diffusons, and locons) and the relative contribution from them to thermal conductivity. In addition, various influencing factors, such as size, temperature, and interfaces, are addressed, and a series of interesting anomalies are presented. Finally, the authors discuss a number of open problems on thermal conductivity of amorphous materials and a brief summary is provided. Thermal conductivity ; Materials science ; Amorphous solid ; Thermoelectric materials ; Thermal conductivity measurement ; Thermal conduction ; Thermal effusivity ; Thermal ; Nanotechnology ; Composite material ; Thermal contact conductance ; Thermal resistance ; Thermodynamics ; Physics ; Crystallography Thermal properties of materials ; Glass properties and applications ; Material Dynamics and Properties
Authors: Felipe Oviedo | Zekun Ren | Shijing Sun | Charles Settens | Zhe Liu | Noor Titan Putri Hartono | Savitha Ramasamy | Brian DeCost | Siyu Tian | Giuseppe Romano | A. Gilad Kusne | Tonio Buonassisi
Source: npj Computational Materials (article, 2019-05-17)
Field: Materials Science — Materials Chemistry

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks Abstract X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning three dimensionalities and seven space groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space group classification of 93 and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2 θ , which enables an XRD pattern to be obtained and classified in 5.5 min or less. Interpretability ; Curse of dimensionality ; Pooling ; Artificial intelligence ; Computer science ; Artificial neural network ; Convolutional neural network ; Machine learning ; Thin film ; Pattern recognition (psychology) ; F1 score ; Materials science ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Condensed Matter Physics
Authors: G. P. Purja Pun | R. Batra | R. Ramprasad | Y. Mishin
Source: Nature Communications (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Physically informed artificial neural networks for atomistic modeling of materials Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable parameters and are usually not accurate. The emerging machine-learning (ML) potentials achieve highly accurate interpolation within a large DFT database but, being purely mathematical constructions, suffer from poor transferability to unknown structures. We propose a new approach that can drastically improve the transferability of ML potentials by informing them of the physical nature of interatomic bonding. This is achieved by combining a rather general physics-based model (analytical bond-order potential) with a neural-network regression. This approach, called the physically informed neural network (PINN) potential, is demonstrated by developing a general-purpose PINN potential for Al. We suggest that the development of physics-based ML potentials is the most effective way forward in the field of atomistic simulations. Transferability ; Intuition ; Interatomic potential ; Artificial neural network ; Interpolation (computer graphics) ; Field (mathematics) ; Force field (fiction) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Geun Ho Gu | Juhwan Noh | I. P. Kim | Yousung Jung
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for renewable energy materials Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. Machine learning has demonstrated many successes to accelerate the discovery renewable energy materials. Renewable energy ; Limiting ; Sustainable energy ; Energy (signal processing) ; Energy engineering ; Environmental economics ; Computer science ; Engineering ; Mechanical engineering ; Economics ; Electrical engineering ; Physics Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials
Authors: Ying Zhang | Xingfeng He | Zhiqian Chen | Qiang Bai | Adelaide M. Nolan | Charles A. Roberts | Debasish Banerjee | Tomoya Matsunaga | Yifei Mo | Chen Ling
Source: Nature Communications (article, 2019-11-20)
Field: Materials Science — Materials Chemistry

Unsupervised discovery of solid-state lithium ion conductors Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10<sup>-4</sup>-10<sup>-1</sup> S cm<sup>-1</sup> predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data. Unsupervised learning ; Computer science ; Electrical conductor ; Property (philosophy) ; Lithium (medication) ; Machine learning ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Connor W. Coley | Natalie S. Eyke | Klavs F. Jensen
Source: Angewandte Chemie International Edition (article, 2019-09-25)
Field: Materials Science — Materials Chemistry

Autonomous Discovery in the Chemical Sciences Part I: Progress This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling. Part two reflects on these case studies and identifies a set of open challenges for the field. Automation ; Set (abstract data type) ; Drug discovery ; Laboratory automation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Tianjiao Jia | Yang Wang | Yuanyuan Dou | Yaowang Li | Mônica Jung de Andrade | Run Wang | Shaoli Fang | Jinɡjinɡ Li | Yu Zhou | Rui Qiao | Zhuangjian Liu | Yuan Cheng | Yewang Su | Majid Minary‐Jolandan | Ray H. Baughman | Dong Qian | Zunfeng Liu
Source: Advanced Functional Materials (article, 2019-03-07)
Field: Materials Science — Biomaterials

Moisture Sensitive Smart Yarns and Textiles from Self‐Balanced Silk Fiber Muscles Abstract Smart textiles that sense, interact, and adapt to environmental stimuli have provided exciting new opportunities for a variety of applications. However, current advances have largely remained at the research stage due to the high cost, complexity of manufacturing, and uncomfortableness of environment‐sensitive materials. In contrast, natural textile materials are more attractive for smart textiles due to their merits in terms of low cost and comfortability. Here, water fog and humidity‐driven torsional and tensile actuation of thermally set twisted, coiled, plied silk fibers, and weave textiles from these silk fibers are reported. When exposed to water fog, the torsional silk fiber provides a fully reversible torsional stroke of 547° mm −1 . Coiled‐and‐thermoset silk yarns provide a 70% contraction when the relative humidity is changed from 20% to 80%. Such an excellent actuation behavior originates from water absorption‐induced loss of hydrogen bonds within the silk proteins and the associated structural transformation, which are corroborated by atomistic and macroscopic characterization of silk and molecular dynamics simulations. With its large abundance, cost‐effectiveness, and comfortability for wearing, the silk muscles will open up additional possibilities in industrial applications, such as smart textiles and soft robotics. SILK ; Materials science ; Composite material ; Textile ; Smart material ; Fiber ; Nanotechnology Silk-based biomaterials and applications ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: R.I. Zubatyuk | Justin S. Smith | Jerzy Leszczyński | Olexandr Isayev
Source: Science Advances (article, 2019-08-02)
Field: Materials Science — Materials Chemistry

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network Atomic and molecular properties could be evaluated from the fundamental Schrodinger's equation and therefore represent different modalities of the same quantum phenomena. Here, we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in the computational cost. With AIMNet, we show a new dimension of transferability: the ability to learn new targets using multimodal information from previous training. The model can learn implicit solvation energy (SMD method) using only a fraction of the original training data and an archive median absolute deviation error of 1.1 kcal/mol compared to experimental solvation free energies in the MNSol database. Artificial neural network ; Computer science ; Machine learning ; Artificial intelligence ; Biological system ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics