jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zehong Yang | Hongyan Xu | Xiaojun Zhao
Source: Advanced Science (review, 2020-03-20)
Field: Materials Science — Biomaterials

Designer Self‐Assembling Peptide Hydrogels to Engineer 3D Cell Microenvironments for Cell Constructs Formation and Precise Oncology Remodeling in Ovarian Cancer Designer self-assembling peptides form the entangled nanofiber networks in hydrogels by ionic-complementary self-assembly. This type of hydrogel has realistic biological and physiochemical properties to serve as biomimetic extracellular matrix (ECM) for biomedical applications. The advantages and benefits are distinct from natural hydrogels and other synthetic or semisynthetic hydrogels. Designer peptides provide diverse alternatives of main building blocks to form various functional nanostructures. The entangled nanofiber networks permit essential compositional complexity and heterogeneity of engineering cell microenvironments in comparison with other hydrogels, which may reconstruct the tumor microenvironments (TMEs) in 3D cell cultures and tissue-specific modeling in vitro. Either ovarian cancer progression or recurrence and relapse are involved in the multifaceted TMEs in addition to mesothelial cells, fibroblasts, endothelial cells, pericytes, immune cells, adipocytes, and the ECM. Based on the progress in common hydrogel products, this work focuses on the diverse designer self-assembling peptide hydrogels for instructive cell constructs in tissue-specific modeling and the precise oncology remodeling for ovarian cancer, which are issued by several research aspects in a 3D context. The advantages and significance of designer peptide hydrogels are discussed, and some common approaches and coming challenges are also addressed in current complex tumor diseases. Self-healing hydrogels ; Extracellular matrix ; Self-assembling peptide ; Nanofiber ; Tumor microenvironment ; Tissue engineering ; Context (archaeology) ; Nanotechnology ; Cell biology ; Chemistry ; Materials science ; Cancer research ; Biology ; Medicine ; Biomedical engineering ; Tumor cells Supramolecular Self-Assembly in Materials ; Glycosylation and Glycoproteins Research ; Nanoplatforms for cancer theranostics
Authors: Nongnuch Artrith | Zhexi Lin | Jingguang G. Chen
Source: ACS Catalysis (article, 2020-08-03)
Field: Materials Science — Materials Chemistry

Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning Machine learning is ideally suited for the pattern detection in large uniform data sets, but consistent experimental data sets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on a computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C–C bond-scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM–Pt–Pt(111) and Pt–TM–Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. The approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of catalytic materials. Bimetallic strip ; Catalysis ; Selectivity ; Chemistry ; Transition metal ; Ethanol ; Machine learning ; Yield (engineering) ; Computational model ; Artificial intelligence ; Computer science ; Materials science ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Matthew R. Carbone | Mehmet Topsakal | Deyu Lu | Shinjae Yoo
Source: Physical Review Letters (article, 2020-04-16)
Field: Materials Science — Materials Chemistry

Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy Simulations of excited state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. As a proof of principle, we demonstrate that graph-based neural networks can be used to predict the x-ray absorption near-edge structure spectra of molecules to quantitative accuracy. Specifically, the predicted spectra reproduce nearly all prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Besides its own utility in spectral analysis and structure inference, our method can be combined with structure search algorithms to enable high-throughput spectrum sampling of the vast material configuration space, which opens up new pathways to material design and discovery. Computer science ; Spectral line ; Throughput ; Inference ; Excited state ; Absorption (acoustics) ; Artificial neural network ; Computational physics ; Ground truth ; Algorithm ; Materials science ; Optics ; Artificial intelligence ; Physics ; Atomic physics ; Quantum mechanics ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Julian Full | Santosh P. Panchal | Julian Götz | Ana‐Maria Krause | Agnieszka Nowak‐Król
Source: Angewandte Chemie International Edition (article, 2020-11-27)
Field: Chemistry — Organic Chemistry

Modular Synthesis of Organoboron Helically Chiral Compounds: Cutouts from Extended Helices Two types of helically chiral compounds bearing one and two boron atoms were synthesized by a modular approach. Formation of the helical scaffolds was executed by the introduction of boron to flexible biaryl and triaryl derived from small achiral building blocks. All-ortho-fused azabora[7]helicenes feature exceptional configurational stability, blue or green fluorescence with quantum yields (Φ<sub>fl</sub> ) of 18-24 % in solution, green or yellow solid-state emission (Φ<sub>fl</sub> up to 23 %), and strong chiroptical response with large dissymmetry factors of up to 1.12×10<sup>-2</sup> . Azabora[9]helicenes consisting of angularly and linearly fused rings are blue emitters exhibiting Φ<sub>fl</sub> of up to 47 % in CH<sub>2</sub> Cl<sub>2</sub> and 25 % in the solid state. As revealed by the DFT calculations, their P-M interconversion pathway is more complex than that of H1. Single-crystal X-ray analysis shows clear differences in the packing arrangement of methyl and phenyl derivatives. These molecules are proposed as primary structures of extended helices. Solid-state ; Boron ; Chemistry ; Molecule ; Crystallography ; Modular design ; Axial chirality ; Chirality (physics) ; Stereochemistry ; Materials science ; Enantioselective synthesis ; Physics ; Organic chemistry ; Physical chemistry ; Catalysis ; Chiral symmetry breaking ; Symmetry breaking Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes
Authors: Nikolai A. Zarkevich | V. I. Zverev
Source: Crystals (article, 2020-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Viable Materials with a Giant Magnetocaloric Effect This review of the current state of magnetocalorics is focused on materials exhibiting a giant magnetocaloric response near room temperature. To be economically viable for industrial applications and mass production, materials should have desired useful properties at a reasonable cost and should be safe for humans and the environment during manufacturing, handling, operational use, and after disposal. The discovery of novel materials is followed by a gradual improvement of properties by compositional adjustment and thermal or mechanical treatment. Consequently, with time, good materials become inferior to the best. There are several known classes of inexpensive materials with a giant magnetocaloric effect, and the search continues. Magnetic refrigeration ; Materials science ; Process engineering ; Thermal ; Engineering ; Thermodynamics ; Physics Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials
Authors: Vonika Ka‐Man Au
Source: Frontiers in Chemistry (review, 2020-08-28)
Field: Chemistry — Inorganic Chemistry

Recent Advances in the Use of Metal-Organic Frameworks for Dye Adsorption Organic dyes are heavily used in industries for the manufacture of colored goods. This has eventually resulted in the generation of contaminated wastewater which is hard to be purified. Recent studies have demonstrated that metal-organic frameworks (MOFs), a class of supramolecular materials of immense interest, are useful in the adsorption of organic dye molecules because of their modifiable porous structures. In this mini review, the recent advances in the use of MOFs for the adsorption of organic dyes will be summarized. Metal-organic framework ; Adsorption ; Organic molecules ; Organic dye ; Supramolecular chemistry ; Nanotechnology ; Materials science ; Molecule ; Chemistry ; Chemical engineering ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal
Authors: Yanming Wang | Tian Xie | Arthur France‐Lanord | Arthur Berkley | Jeremiah A. Johnson | Yang Shao‐Horn | Jeffrey C. Grossman
Source: Chemistry of Materials (article, 2020-04-22)
Field: Materials Science — Materials Chemistry

Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics Solid polymer electrolytes (SPEs) are considered promising building blocks of next-generation lithium-ion batteries due to their advantages in safety, cost, and flexibility. However, current SPEs suffer from a low ionic conductivity, motivating the development of novel highly conductive SPE materials. Here we propose a new SPE design approach that integrates coarse-grained molecular dynamics (CGMD) with machine learning. A continuous high-dimensional design space, composed of physically interpretable universal descriptors, was constructed by the coarse graining of chemical species. A Bayesian optimization (BO) algorithm was then employed to efficiently explore this space via autonomous CGMD simulations. Adopting this CGMD-BO approach, we obtained comprehensive descriptions of the relationships between the lithium conductivity and intrinsic material properties at the molecular level, such as the molecule size and nonbonding interaction strength, to provide guidance on directions to improve upon the components of the best-known electrolytes, including anion, secondary site, and backbone chain. Molecular dynamics ; Electrolyte ; Materials science ; Lithium (medication) ; Fast ion conductor ; Electrical conductor ; Granularity ; Polymer ; Conductive polymer ; Molecule ; Polymer electrolytes ; Ion ; Nanotechnology ; Ionic conductivity ; Chemical physics ; Computer science ; Computational chemistry ; Chemistry ; Physical chemistry ; Organic chemistry ; Composite material ; Electrode Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: Bingnan Han | Yuxuan Lin | Yafang Yang | Nannan Mao | Wenyue Li | Haozhe Wang | Kenji Yasuda | Xirui Wang | Valla Fatemi | Lin Zhou | Joel I-Jan Wang | Qiong Ma | Yuan Cao | Daniel Rodan‐Legrain | Ya‐Qing Bie | Efrén Navarro‐Moratalla | Dahlia Klein | David MacNeill | Sanfeng Wu | Hikari Kitadai | Xi Ling | Pablo Jarillo‐Herrero | Jing Kong | Jihao Yin | Tomás Palacios
Source: Advanced Materials (article, 2020-06-09)
Field: Materials Science — Materials Chemistry

Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries. Characterization (materials science) ; Deep learning ; Intuition ; Artificial intelligence ; Materials science ; Identification (biology) ; Computer science ; Nanotechnology ; Artificial neural network ; Machine learning Graphene research and applications ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Haru Hirai | Shun Ito | Shinjiro Takano | Kiichirou Koyasu | Tatsuya Tsukuda
Source: Chemical Science (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Ligand-protected gold/silver superatoms: current status and emerging trends Monolayer-protected gold/silver clusters have attracted much interest as nano-scale building units for novel functional materials owing to their nonbulk-like structures and size-specific properties. They can be viewed as ligand-protected superatoms because their magic stabilities and fundamental properties are well explained in the framework of the jellium model. In the last decade, the number of ligand-protected superatoms with atomically-defined structures has been increasing rapidly thanks to the well-established synthesis and structural determination by X-ray crystallography. This perspective summarizes the current status and emerging trends in synthesis and characterization of superatoms. The topics related to synthesis include (1) development of targeted synthesis based on transformation, (2) enhancement of robustness and synthetic yield for practical applications, and (3) development of controlled fusion and assembly of well-defined superatoms to create new properties. New characterization approaches are also introduced such as (1) mass spectrometry and laser spectroscopies in the gas phase, (2) determination of static and dynamic structures, and (3) computational analysis by machine learning. Finally, future challenges and prospects are discussed for further promotion and development of materials science of superatoms. Nanotechnology ; Ligand (biochemistry) ; Current (fluid) ; Chemistry ; Characterization (materials science) ; Materials science ; Physics ; Biochemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Sheng Ye | Kai Zhong | Jinxiao Zhang | Wei Hu | Jonathan D. Hirst | Guozhen Zhang | Shaul Mukamel | Jun Jiang
Source: Journal of the American Chemical Society (article, 2020-10-30)
Field: Chemistry — Spectroscopy

A Machine Learning Protocol for Predicting Protein Infrared Spectra Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties. Chemistry ; Biomolecule ; Infrared spectroscopy ; Transferability ; Folding (DSP implementation) ; Infrared ; Spectral line ; Biological system ; Quantum chemical ; Protocol (science) ; Protein secondary structure ; Protein folding ; Chemical physics ; Computational chemistry ; Pattern recognition (psychology) ; Molecule ; Artificial intelligence ; Machine learning ; Computer science ; Biochemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Vikash Kaphle | Pushpa Raj Paudel | Drona Dahal | Raj Kishen Radha Krishnan | Björn Lüssem
Source: Nature Communications (article, 2020-05-20)
Field: Materials Science — Polymers and Plastics

Finding the equilibrium of organic electrochemical transistors Organic Electrochemical Transistors are versatile sensors that became essential for the field of organic bioelectronics. However, despite their importance, an incomplete understanding of their working mechanism is currently precluding a targeted design of Organic Electrochemical Transistors and it is still challenging to formulate precise design rules guiding materials development in this field. Here, it is argued that current capacitive device models neglect lateral ion currents in the transistor channel and therefore fail to describe the equilibrium state of Organic Electrochemical Transistors. An improved model is presented, which shows that lateral ion currents lead to an accumulation of ions at the drain contact, which significantly alters the transistor behavior. Overall, these results show that a better understanding of the interface between the organic semiconductor and the drain electrode is needed to reach a full understanding of Organic Electrochemical Transistors. Bioelectronics ; Transistor ; Organic semiconductor ; Materials science ; Electrochemistry ; Field-effect transistor ; Nanotechnology ; Electrode ; Capacitive sensing ; Optoelectronics ; Organic electronics ; Electrical engineering ; Chemistry ; Biosensor ; Voltage ; Engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Chenwen Li | Lanlan Li | Sheng Chen | Jianxiang Zhang | Wan-Liang Lü
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-03-18)
Field: Materials Science — Materials Chemistry

Antioxidant Nanotherapies for the Treatment of Inflammatory Diseases Reactive oxygen species (ROS) are essential in regulating various physiological functions. However, overproduction of ROS is implicated in the pathogenesis of various inflammatory diseases. Antioxidant therapy has thus represented an effective strategy for the treatment of oxidative stress relevant inflammatory diseases. Conventional anti-oxidative agents showed limited <i>in vivo</i> effects owing to their non-specific distribution and low retention in disease sites. Over the past decades, significant achievements have been made in the development of antioxidant nanotherapies that exhibit multiple advantages such as excellent pharmacokinetics, stable anti-oxidative activity, and intrinsic ROS-scavenging properties. This review provides a comprehensive overview on recent advances in antioxidant nanotherapies, including ROS-scavenging inorganic nanoparticles, organic nanoparticles with intrinsic antioxidant activity, and drug-loaded anti-oxidant nanoparticles. We highlight the biomedical applications of antioxidant nanotherapies in the treatment of different inflammatory diseases, with an emphasis on inflammatory bowel disease, cardiovascular disease, and brain diseases. Current challenges and future perspectives to promote clinical translation of antioxidant nanotherapies are also briefly discussed. Antioxidant ; Oxidative stress ; Reactive oxygen species ; Pharmacology ; In vivo ; Chemistry ; Disease ; Inflammation ; Medicine ; Biology ; Biochemistry ; Immunology ; Biotechnology ; Pathology Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications ; Nanoplatforms for cancer theranostics
Authors: Yifei Chen | Yuan Tian | Yumei Zhou | Daqing Fang | Xiangdong Ding | Jun Sun | Dezhen Xue
Source: Journal of Alloys and Compounds (article, 2020-06-23)
Field: Materials Science — Biomaterials

Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy Alloy ; Homogenization (climate) ; Ductility (Earth science) ; Materials science ; Computer science ; Process engineering ; Mechanical engineering ; Metallurgy ; Engineering ; Creep Magnesium Alloys: Properties and Applications ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Haoran Xu | Jingbo Ma | Peng Tan | Bin Chen | Zhen Wu | Yanxiang Zhang | Huizhi Wang | Jin Xuan | Meng Ni
Source: Energy and AI (article, 2020-04-19)
Field: Materials Science — Materials Chemistry

Towards online optimisation of solid oxide fuel cell performance: Combining deep learning with multi-physics simulation The use of solid oxide fuel cells (SOFCs) is a promising approach towards achieving sustainable electricity production from fuel. The utilisation of the hydrocarbons and biomass in SOFCs is particularly attractive owing to their wide distribution, high energy density, and low price. The long-term operation of SOFCs using such fuels remains difficult owing to a lack of an effective diagnosis and optimisation system, which requires not only a precise analysis but also a fast response. In this study, we developed a hybrid model for an on-line analysis of SOFCs at the cell level. The model combines a multi-physics simulation (MPS) and deep learning, overcoming the complexity of MPS for a model-based control system, and reducing the cost of building a database (compared with the experiments) for the training of a deep neural network. The maximum temperature gradient and heat generation are two target parameters for an efficient operation of SOFCs. The results show that a precise prediction can be achieved from a trained AI algorithm, in which the relative error between the MPS and AI models is less than 1%. Moreover, an online optimisation is realised using a genetic algorithm, achieving the maximum power density within the limitations of the temperature gradient and operating conditions. This method can also be applied to the prediction and optimisation of other non-liner, dynamic systems. Artificial neural network ; Computer science ; Solid oxide fuel cell ; Power (physics) ; Power density ; Electricity generation ; Deep learning ; Fuel cells ; Process engineering ; Artificial intelligence ; Engineering Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Gabriela Corrêa Carvalho | Rafael Miguel Sábio | Taís de Cássia Ribeiro | A. Monteiro | Daniela Vassalo Pereira | Sidney J. L. Ribeiro | Marlus Chorilli
Source: Pharmaceutical Research (review, 2020-09-07)
Field: Materials Science — Biomaterials

Highlights in Mesoporous Silica Nanoparticles as a Multifunctional Controlled Drug Delivery Nanoplatform for Infectious Diseases Treatment Nanocarriers ; Nanotechnology ; Mesoporous silica ; Drug delivery ; Drug ; Nanomedicine ; Medicine ; Nanoparticle ; Materials science ; Pharmacology ; Chemistry ; Mesoporous material Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques