jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ronghui Lou | Weizhen Liu | Rongjie Li | Shanshan Li | Xuming He | Wenqing Shui
Source: Nature Communications (article, 2021-11-18)
Field: Chemistry — Spectroscopy

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation Abstract Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation. Phosphoproteomics ; Workflow ; Computer science ; Profiling (computer programming) ; In silico ; Proteome ; Computational biology ; Data mining ; Bioinformatics ; Database ; Biology ; Operating system ; Kinase ; Protein phosphorylation Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Siqi Chen | Yongjie Dong | Song Ma | Jiayuan Ren | Xipeng Yang | Yingjie Wang | Shaoyu Lü
Source: ACS Applied Materials & Interfaces (article, 2021-03-10)
Field: Materials Science — Materials Chemistry

Superstretching MXene Composite Hydrogel as a Bidirectional Stress Response Thixotropic Sensor The arrival of the era of artificial intelligence is constantly advancing the development of flexible electronic materials. However, low mechanical properties, nonflexible signal transmission, and insensitive signal output have restricted their development as sensors. In this study, a superstretching MXene composite conductive hydrogel was developed with a tensile strain of more than 1800%. The hydrogel was used as a flexible wearable sensor to detect human motion signals in real time. High sensitivity was achieved using the sensor to discern multidirectional human motions, such as bending of human joints, throat vocalization, swallowing, and pulse beat. In addition, rapid resilience was observed for the MXene composite hydrogel after unloading reverse compressive stress, which can quickly cause a specific current response in the micropressure area without leaving any traces. This thixotropic sensor achieves a rapid response to bidirectional stress and has huge application prospects in the field of human body motion detection and national defense information encryption. Materials science ; Composite number ; Capacitive sensing ; Electronic skin ; Wearable computer ; Bending ; SIGNAL (programming language) ; Composite material ; Acoustics ; Optoelectronics ; Nanotechnology ; Computer science MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications
Authors: Muhammed Kallumottakkal | Mousa Hussein | Muhammad Z. Iqbal
Source: Frontiers in Materials (article, 2021-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Progress of 2D Nanomaterials for Application on Microwave Absorption: A Comprehensive Study Rapid advancements and wide spread of microwave- and RF-communication systems over the years have led to an abundant increase in electromagnetic energy radiation in our living environment. Such an increase in microwave sources is due to the development and advancement in communication techniques (mobile phones, laptops, and antennas for aeronautics or automobile) and electronic warfare in the military field (radar and satellite). Recently research efforts are focused on finding solutions to guarantee protection from electromagnetic (EM) radiations. The EM absorbing materials are used to overcome these issues to ensure public protection as well as safe military operations. Various types of EM absorbing materials comprising composite materials have been progressively developed and researched. This kind of material is developed by impeding absorbing charges (magnetic or dielectric) into a host matrix material. Recently, carbon allotropes such as graphene, MXenes, carbon nanotubes (CNTs), and carbon fibers have attracted increasing attention owing to their EMI shielding characteristics and lightweight. This work presents a comprehensive study on the recent research progress on the application of nanomaterials for electromagnetic shielding and absorption. The review will cover the microwave absorption mechanism and absorption performance using graphene, MXenes, carbon nanotubes (CNTs), carbides, and ferromagnetic metals. Overall, the review will present a timely update on the research progress of microwave absorption performance of various nanomaterials. MXenes ; Microwave ; Carbon nanotube ; Materials science ; Nanotechnology ; Graphene ; Electromagnetic shielding ; Absorption (acoustics) ; Nanomaterials ; Telecommunications ; Computer science ; Composite material Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Yekkuni L. Balachandran | Xuanyu Li | Xingyu Jiang
Source: Nano Letters (article, 2021-02-01)
Field: Chemistry — Inorganic Chemistry

Integrated Microfluidic Synthesis of Aptamer Functionalized Biozeolitic Imidazolate Framework (BioZIF-8) Targeting Lymph Node and Tumor Targeted delivery of therapeutic molecules using nanomaterials is desired to elicit specific responses toward diseases. Such an integrated synthesis of functional material using a microfluidic approach is a great challenge. Functional metal organic frameworks (MOFs) with unique structural diversity possess a complicated synthesis procedure thereby requiring a modest, straightforward approach to synthesize size-controllable MOFs. Here, we develop an integrated microfluidic chip to synthesize the aptamer-modified biozeolitic imidazolate framework (BioZIF-8) to target the lymph node and tumor. The first stage of the microfluidic chip forms the ZIF-8 encapsulating biomolecules (bovine serum albumin, small interfering ribonucleic acid, and doxorubicin). The second stage modifies the surface of BioZIF-8 with the aptamer. Our approach reduces the overall synthesis time (∼3 mg/10 min against 15 h for the conventional two-step method) and encapsulates a higher number of biomolecules. The microfluidic approach realizes the rapid and fine-tuned synthesis of functional MOFs integrated into one-step. Aptamer ; Microfluidics ; Biomolecule ; Nanotechnology ; Zeolitic imidazolate framework ; Imidazolate ; Microfluidic chip ; Materials science ; Chemistry ; Combinatorial chemistry ; Metal-organic framework Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Dendrimers and Hyperbranched Polymers
Authors: Jifeng Liu | Hongyan Long | Dagmar Zeuschner | Andreas F. B. Räder | William J. Polacheck | Horst Kessler | Lydia Sorokin | Britta Trappmann
Source: Nature Communications (article, 2021-06-07)
Field: Materials Science — Biomaterials

Synthetic extracellular matrices with tailored adhesiveness and degradability support lumen formation during angiogenic sprouting A major deficit in tissue engineering strategies is the lack of materials that promote angiogenesis, wherein endothelial cells from the host vasculature invade the implanted matrix to form new blood vessels. To determine the material properties that regulate angiogenesis, we have developed a microfluidic in vitro model in which chemokine-guided endothelial cell sprouting into a tunable hydrogel is followed by the formation of perfusable lumens. We show that long, perfusable tubes only develop if hydrogel adhesiveness and degradability are fine-tuned to support the initial collective invasion of endothelial cells and, at the same time, allow for matrix remodeling to permit the opening of lumens. These studies provide a better understanding of how cell-matrix interactions regulate angiogenesis and, therefore, constitute an important step towards optimal design criteria for tissue-engineered materials that require vascularization. Sprouting ; Extracellular ; Extracellular matrix ; Lumen (anatomy) ; Cell biology ; Chemistry ; Biology ; Botany Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Bone Tissue Engineering Materials
Authors: Lloyd C. Murfin | Simon E. Lewis
Source: Molecules (review, 2021-01-12)
Field: Chemistry — Spectroscopy

Azulene—A Bright Core for Sensing and Imaging Azulene is a hydrocarbon isomer of naphthalene known for its unusual colour and fluorescence properties. Through the harnessing of these properties, the literature has been enriched with a series of chemical sensors and dosimeters with distinct colorimetric and fluorescence responses. This review focuses specifically on the latter of these phenomena. The review is subdivided into two sections. Section one discusses turn-on fluorescent sensors employing azulene, for which the literature is dominated by examples of the unusual phenomenon of azulene protonation-dependent fluorescence. Section two focuses on fluorescent azulenes that have been used in the context of biological sensing and imaging. To aid the reader, the azulene skeleton is highlighted in blue in each compound. Azulene ; Fluorescence ; Context (archaeology) ; Chemistry ; Naphthalene ; Protonation ; Photochemistry ; Chromophore ; Physics ; Organic chemistry ; Optics ; Biology Molecular Sensors and Ion Detection ; Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials
Authors: Gordon G. C. Peterson | Jakoah Brgoch
Source: Journal of Physics Energy (article, 2021-02-09)
Field: Materials Science — Materials Chemistry

Materials discovery through machine learning formation energy Abstract The budding field of materials informatics has coincided with a shift towards artificial intelligence to discover new solid-state compounds. The steady expansion of repositories for crystallographic and computational data has set the stage for developing data-driven models capable of predicting a bevy of physical properties. Machine learning methods, in particular, have already shown the ability to identify materials with near ideal properties for energy-related applications by screening crystal structure databases. However, examples of the data-guided discovery of entirely new, never-before-reported compounds remain limited. The critical step for determining if an unknown compound is synthetically accessible is obtaining the formation energy and constructing the associated convex hull. Fortunately, this information has become widely available through density functional theory (DFT) data repositories to the point that they can be used to develop machine learning models. In this Review, we discuss the specific design choices for developing a machine learning model capable of predicting formation energy, including the thermodynamic quantities governing material stability. We investigate several models presented in the literature that cover various possible architectures and feature sets and find that they have succeeded in uncovering new DFT-stable compounds and directing materials synthesis. To expand access to machine learning models for synthetic solid-state chemists, we additionally present MatLearn . This web-based application is intended to guide the exploration of a composition diagram towards regions likely to contain thermodynamically accessible inorganic compounds. Finally, we discuss the future of machine-learned formation energy and highlight the opportunities for improved predictive power toward the synthetic realization of new energy-related materials. Computer science ; Machine learning ; Artificial intelligence ; Cheminformatics ; Field (mathematics) ; Materials informatics ; Chemistry ; Health informatics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Waqas W. Ahmed | Mohamed Farhat | Xiangliang Zhang | Ying Wu
Source: Physical Review Research (article, 2021-02-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak Concealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature. Yet, the invention of artificial materials and new physical principles for optical and sound wave manipulation translated this abstract concept into reality by making an object optically and acoustically "invisible." Here, we present the notion of a machine learning driven acoustic cloak and demonstrate an example of such a cloak with a multilayered core-shell configuration. We develop deterministic and probabilistic deep learning models based on autoencoderlike neural network structure to retrieve the structural and material properties of the cloaking shell surrounding the object that suppresses scattering of sound in a broad spectral range, as if it was not there. The probabilistic model enhances the generalization ability of design procedure and uncovers the sensitivity of the cloak's parameters on the spectral response for practical implementation. This proposal opens up avenues to expedite the design of intelligent cloaking devices for tailored spectral response and offers a feasible solution for inverse scattering problems. Cloak ; Cloaking ; Probabilistic logic ; Broadband ; Generalization ; Object (grammar) ; Inverse scattering problem Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Random lasers and scattering media
Authors: Rémi Pétuya | Samantha Durdy | Dmytro Antypov | Michael W. Gaultois | Neil G. Berry | George R. Darling | Alexandros P. Katsoulidis | Matthew S. Dyer | Matthew J. Rosseinsky
Source: Angewandte Chemie International Edition (article, 2021-12-08)
Field: Chemistry — Inorganic Chemistry

Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry The choice of metal and linker together define the structure and therefore the guest accessibility of a metal-organic framework (MOF), but the large number of possible metal-linker combinations makes the selection of components for synthesis challenging. We predict the guest accessibility of a MOF with 80.5 % certainty based solely on the identity of these two components as chosen by the experimentalist, by decomposing reported experimental three-dimensional MOF structures in the Cambridge Structural Database into metal and linker and then learning the connection between the components' chemistry and the MOF porosity. Pore dimensions of the guest-accessible space are classified into four ranges with three sequential models. Both the dataset and the predictive models are available to download and offer simple guidance in prioritization of the choice of the components for exploratory MOF synthesis for separation and catalysis based on guest accessibility considerations. Linker ; Metal-organic framework ; Prioritization ; Computer science ; Metal ; Chemistry ; Artificial intelligence ; Nanotechnology ; Materials science ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yu Liao | Junhua Bai | Fuwen Chen | Guanglong Xu | Yuwen Cui
Source: Journal of Material Science and Technology (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

Microstructural strengthening and toughening mechanisms in Fe-containing Ti-6Al-4V: A comparison between homogenization and aging treated states Materials science ; Homogenization (climate) ; Microstructure ; Ultimate tensile strength ; Alloy ; Castability ; Toughness ; Composite material ; Fracture toughness ; Metallurgy Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Advanced materials and composites
Authors: Xiangyu Zhang | Kexin Zhang | Hyeonsuk Yoo | Yongjin Lee
Source: ACS Sustainable Chemistry & Engineering (article, 2021-02-05)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Driven Discovery of Metal–Organic Frameworks for Efficient CO<sub>2</sub> Capture in Humid Condition This paper presents a computational study to design tailor-made metal–organic frameworks (MOFs) for efficient CO2 capture in humid conditions. Target-specific MOFs were generated in our computational platform incorporating the Monte Carlo tree search and recurrent neural networks according to the objective function values that combine three requirements of high adsorption performance, experimental accessibility of designed materials, and good hydrophobicity (i.e., the low Henry coefficient of water in pore space) to be applied in humid conditions. With a given input of 27 different combinations of metal node and topology net information extracted from experimental MOFs, our approach successfully designed promising and novel metal–organic frameworks for CO2 capture, satisfying the three requirements in good balance. Furthermore, the detailed analysis of the structure–property relationship identified that moderate Di (the diameter of the largest included sphere) of 14.18 Å and accessible surface area (ASA) of 1750 m2/g values are desirable for high-performing MOFs for CO2 capture, which is attributed to the trade-off relationship between good adsorption selectivity (small pore size is desired) and high adsorption capacity (sufficient pore size is necessary). Adsorption ; Metal-organic framework ; Node (physics) ; Characterisation of pore space in soil ; Function (biology) ; Artificial neural network ; Computer science ; Monte Carlo method ; Materials science ; Topology (electrical circuits) ; Mathematics ; Machine learning ; Porosity ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications
Authors: Marko Melander
Source: Current Opinion in Electrochemistry (article, 2021-04-28)
Field: Chemistry — Electrochemistry

Grand canonical ensemble approach to electrochemical thermodynamics, kinetics, and model Hamiltonians The unique feature of electrochemistry is the ability to control reaction thermodynamics and kinetics by the application of electrode potential. Recently, theoretical methods and computational approaches within the grand canonical ensemble (GCE) have enabled to explicitly include and control the electrode potential in first principles calculations. In this review, recent advances and future promises of GCE density functional theory and rate theory are discussed. Particular focus is devoted to considering how the GCE methods either by themselves or combined with model Hamiltonians can be used to address intricate phenomena such as solvent/electrolyte effects and nuclear quantum effects to provide a detailed understanding of electrochemical reactions and interfaces. Grand canonical ensemble ; Electrochemistry ; Thermodynamics ; Kinetics ; Electrolyte ; Density functional theory ; Quantum ; Chemistry ; Statistical physics ; Electrode ; Computational chemistry ; Physics ; Physical chemistry ; Quantum mechanics ; Mathematics ; Monte Carlo method Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Jinggang Lan | Venkat Kapil | Piero Gasparotto | Michele Ceriotti | Marcella Iannuzzi | Vladimir V. Rybkin
Source: Nature Communications (article, 2021-02-03)
Field: Materials Science — Materials Chemistry

Simulating the ghost: quantum dynamics of the solvated electron The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron. Solvated electron ; Electron ; Quantum ; Statistical physics ; Electronic structure ; Function (biology) ; Wave function ; Physics ; Computer science ; Molecular dynamics ; Chemical physics ; Atomic physics ; Quantum mechanics ; Radiolysis Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Oliver T. Hofmann | Egbert Zojer | Lukas Hörmann | Andreas Jeindl | Reinhard J. Maurer
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

First-principles calculations of hybrid inorganic–organic interfaces: from state-of-the-art to best practice The computational characterization of inorganic-organic hybrid interfaces is arguably one of the technically most challenging applications of density functional theory. Due to the fundamentally different electronic properties of the inorganic and the organic components of a hybrid interface, the proper choice of the electronic structure method, of the algorithms to solve these methods, and of the parameters that enter these algorithms is highly non-trivial. In fact, computational choices that work well for one of the components often perform poorly for the other. As a consequence, default settings for one materials class are typically inadequate for the hybrid system, which makes calculations employing such settings inefficient and sometimes even prone to erroneous results. To address this issue, we discuss how to choose appropriate atomistic representations for the system under investigation, we highlight the role of the exchange-correlation functional and the van der Waals correction employed in the calculation and we provide tips and tricks how to efficiently converge the self-consistent field cycle and to obtain accurate geometries. We particularly focus on potentially unexpected pitfalls and the errors they incur. As a summary, we provide a list of best practice rules for interface simulations that should especially serve as a useful starting point for less experienced users and newcomers to the field. Work (physics) ; Nanotechnology ; Materials science ; State (computer science) ; Computer science ; Engineering ; Mechanical engineering ; Algorithm Electronic and Structural Properties of Oxides ; Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science