jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jincheng Liu | Langli Luo | Hai Xiao | Junfa Zhu | Yang He | Jun Li
Source: Journal of the American Chemical Society (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

Metal Affinity of Support Dictates Sintering of Gold Catalysts Sintering during heterogeneous catalytic reactions is one of the most notorious deactivation channels in catalysts of supported metal nanoparticles. It is therefore critical to understand the effect of support on the sintering behavior. Here, by using <i>in situ</i> aberration-corrected transmission electron microscopy and computational modeling, the atomic-scale dynamic interactions are revealed between Au nanoparticles and various supports. It is found that Au nanoparticles on ceria have a smaller contact angle and are apparently less mobile, especially at surface steps when compared with those on the amorphous silica. Analogous to hydrophilicity, we attribute the origin of mobility of small nanoparticles to metal affinity, which determines the interaction between metal and support material. <i>Ab initio</i> molecular dynamics (AIMD) and machine learning-based deep potential molecular dynamics (DPMD) simulations directly capture a coalescence process on the silica surface and the strong pinning of gold on ceria. The joint experimental and theoretical results on the atomic scale demonstrate the metal affinity of active and inert supports as the key descriptor pertinent to sintering and deactivation of heterogeneous catalysts. Sintering ; Coalescence (physics) ; Chemistry ; Catalysis ; Nanoparticle ; Metal ; Amorphous solid ; Inert ; Transmission electron microscopy ; Molecular dynamics ; Atomic units ; Chemical engineering ; Amorphous silica ; Heterogeneous catalysis ; Nanotechnology ; Chemical physics ; Computational chemistry ; Materials science ; Crystallography ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Xiao‐Ming Jiang | Shuiquan Deng | Myung‐Hwan Whangbo | Guo‐Cong Guo
Source: National Science Review (review, 2022-02-10)
Field: Materials Science — Materials Chemistry

Material research from the viewpoint of functional motifs As early as 2001, the need for the 'functional motif theory' was pointed out, to assist the rational design of functional materials. The properties of materials are determined by their functional motifs and how they are arranged in the materials. Uncovering functional motifs and their arrangements is crucial in understanding the properties of materials and rationally designing new materials of desired properties. The functional motifs of materials are the critical microstructural units (e.g. constituent components and building blocks) that play a decisive role in generating certain material functions, and can not be replaced with other structural units without the loss, or significant suppression, of relevant functions. The role of functional motifs and their arrangement in materials, with representative examples, is presented. The microscopic structures of these examples can be classified into six types on a length scale smaller than ∼10 nm with maximum subatomic resolution, i.e. crystal, magnetic, aperiodic, defect, local and electronic structures. Functional motif analysis can be employed in the function-oriented design of materials, as elucidated by taking infrared non-linear optical materials as an example. Machine learning is more efficient in predicting material properties and screening materials with high efficiency than high-throughput experimentation and high-throughput calculations. In order to extract functional motifs and find their quantitative relationships, the development of sufficiently reliable databases for material structures and properties is imperative. Aperiodic graph ; Structural motif ; Rational design ; Material Design ; Function (biology) ; Materials science ; Computer science ; Functional design ; High resolution ; Nanotechnology ; Chemistry ; Mathematics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystal Structures and Properties
Authors: A.M. Radzi | Sheikh Ahmad Zaki | Mohamad Zaki Hassan | R.A. Ilyas | Khairur Rijal Jamaludin | Mohd Yusof Md Daud | Sa'ardin Abd. Aziz
Source: Polymers (review, 2022-03-29)
Field: Materials Science — Polymers and Plastics

Bamboo-Fiber-Reinforced Thermoset and Thermoplastic Polymer Composites: A Review of Properties, Fabrication, and Potential Applications Natural-fiber-reinforced composites, especially bamboo, are an alternative material to compete with conventional materials. Their environmentally friendly, renewable, low-cost, low-density, non-toxic, and fully biodegradable properties are concerning for researchers because of their advantages over synthetic polymers. This comprehensive review presents the results of work on bamboo fiber composites with special reference to bamboo types, thermoplastic and thermoset polymers matrices, hybrid composites, and their applications. In addition, several studies prove that these properties are very good and efficient in various applications. However, in the development of composite technology, bamboo fiber has certain constraints, especially in moisture conditions. Moisture is one of the factors that reduces the potential of bamboo fiber and makes it a critical issue in the manufacturing industry. Therefore, various efforts have been made to ensure that these properties are not affected by moisture by treating the surface fibers using chemical treatments. Thermosetting polymer ; Materials science ; Bamboo ; Composite material ; Environmentally friendly ; Fiber ; Thermoplastic ; Natural fiber ; Moisture ; Composite number ; Synthetic fiber ; Polymer Natural Fiber Reinforced Composites ; Bamboo properties and applications
Authors: Sebastian Loewner | Sebastian Heene | Timo Baroth | Henrik Heymann | Fabian Cholewa | Holger Blume | Cornelia Blume
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-08-17)
Field: Materials Science — Biomaterials

Recent advances in melt electro writing for tissue engineering for 3D printing of microporous scaffolds for tissue engineering Melt electro writing (MEW) is a high-resolution 3D printing technique that combines elements of electro-hydrodynamic fiber attraction and melts extrusion. The ability to precisely deposit micro- to nanometer strands of biocompatible polymers in a layer-by-layer fashion makes MEW a promising scaffold fabrication method for all kinds of tissue engineering applications. This review describes possibilities to optimize multi-parametric MEW processes for precise fiber deposition over multiple layers and prevent printing defects. Printing protocols for nonlinear scaffolds structures, concrete MEW scaffold pore geometries and printable biocompatible materials for MEW are introduced. The review discusses approaches to combining MEW with other fabrication techniques with the purpose to generate advanced scaffolds structures. The outlined MEW printer modifications enable customizable collector shapes or sacrificial materials for non-planar fiber deposition and nozzle adjustments allow redesigned fiber properties for specific applications. Altogether, MEW opens a new chapter of scaffold design by 3D printing. 3D printing ; Scaffold ; Fabrication ; Tissue engineering ; Materials science ; Fused deposition modeling ; Biocompatible material ; Microporous material ; Nanotechnology ; Layer (electronics) ; Fiber ; Extrusion ; Computer science ; Biomedical engineering ; Composite material ; Engineering Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; 3D Printing in Biomedical Research
Authors: Hussein Sabit | Mohamed Abdel-Hakeem | Tahsin Shoala | Shaimaa Abdel-Ghany | Mokhtar Mamdouh Abdel-Latif | Jawaher Almulhim | Mohamed Mansy
Source: Pharmaceutics (review, 2022-07-28)
Field: Materials Science — Biomaterials

Nanocarriers: A Reliable Tool for the Delivery of Anticancer Drugs Nanomedicines have gained popularity due to their potential therapeutic applications, especially cancer treatment. Targeted nanoparticles can deliver drugs directly to cancer cells and enable prolonged drug release, reducing off-target toxicity and increasing therapeutic efficacy. However, translating nanomedicines from preclinical to clinical settings has been difficult. Rapid advancements in nanotechnology promise to enhance cancer therapies. Nanomedicine offers advanced targeting and multifunctionality. Nanoparticles (NPs) have several uses nowadays. They have been studied as drug transporters, tumor gene delivery agents, and imaging contrast agents. Nanomaterials based on organic, inorganic, lipid, or glycan substances and synthetic polymers have been used to enhance cancer therapies. This review focuses on polymeric nanoparticle delivery strategies for anticancer nanomedicines. Nanocarriers ; Nanomedicine ; Drug delivery ; Drug ; Nanotechnology ; Cancer ; Cancer therapy ; Targeted drug delivery ; Pharmacology ; Medicine ; Nanoparticle ; Materials science Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Ting Wan | Luca Capaldo | Davide Ravelli | Walter Vitullo | Felix J. de Zwart | Bas de Bruin | Timothy Noël
Source: Journal of the American Chemical Society (article, 2022-12-30)
Field: Chemistry — Organic Chemistry

Photoinduced Halogen-Atom Transfer by <i>N</i> -Heterocyclic Carbene-Ligated Boryl Radicals for C(sp <sup>3</sup> )–C(sp <sup>3</sup> ) Bond Formation Herein, we present a comprehensive study on the use of <i>N</i>-heterocyclic carbene (NHC)-ligated boryl radicals to enable C(sp<sup>3</sup>)-C(sp<sup>3</sup>) bond formation under visible-light irradiation via Halogen-Atom Transfer (XAT). The methodology relies on the use of an acridinium dye to generate the boron-centered radicals from the corresponding NHC-ligated boranes via single-electron transfer (SET) and deprotonation. These boryl radicals subsequently engage with alkyl halides in an XAT step, delivering the desired nucleophilic alkyl radicals. The present XAT strategy is very mild and accommodates a broad scope of alkyl halides, including medicinally relevant compounds and biologically active molecules. The key role of NHC-ligated boryl radicals in the operative reaction mechanism has been elucidated through a combination of experimental, spectroscopic, and computational studies. This methodology stands as a significant advancement in the chemistry of NHC-ligated boryl radicals, which had long been restricted to radical reductions, enabling C-C bond formation under visible-light photoredox conditions. Chemistry ; Radical ; Carbene ; Deprotonation ; Photochemistry ; Alkyl ; Photoredox catalysis ; Halogen ; Flash photolysis ; Nucleophile ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Photocatalysis ; Reaction rate constant ; Ion Radical Photochemical Reactions ; Organoboron and organosilicon chemistry ; Catalytic C–H Functionalization Methods
Authors: Kevin P. Greenman | William H. Green | Rafael Gómez‐Bombarelli
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Multi-fidelity prediction of molecular optical peaks with deep learning Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of <i>ab initio</i> and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28 000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties. Fidelity ; Deep learning ; High fidelity ; Computer science ; Artificial intelligence ; Physics ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Denghui Lu | Wanrun Jiang | Yixiao Chen | Linfeng Zhang | Weile Jia | Handong Wang | Mohan Chen
Source: Journal of Chemical Theory and Computation (article, 2022-08-04)
Field: Materials Science — Materials Chemistry

DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning-based PES model. This scheme, we call DP Compress, is an efficient postprocessing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H<sub>2</sub>O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online. Computer science ; Computation ; Deep learning ; Boosting (machine learning) ; Compression (physics) ; Scheme (mathematics) ; Computational science ; Artificial neural network ; Artificial intelligence ; Computer engineering ; Efficient energy use ; Algorithm ; Materials science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Yuichi Motoyama | Ryo Tamura | Kazuyoshi Yoshimi | Kei Terayama | Tsuyoshi Ueno | Koji Tsuda
Source: Computer Physics Communications (article, 2022-05-16)
Field: Materials Science — Materials Chemistry

Bayesian optimization package: PHYSBO PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application in the basic sciences such as physics and materials science. Bayesian optimization is used to select an appropriate input for experiments/simulations from candidate inputs listed in advance in order to obtain better output values with the help of machine learning prediction. PHYSBO can be used to find better solutions for both single and multi-objective optimization problems. At each cycle in the Bayesian optimization, a single proposal or multiple proposals can be obtained for the next experiments/simulations. These proposals can be obtained interactively for use in experiments. PHYSBO is available at https://github.com/issp-center-dev/PHYSBO. Program Title: PHYSBO CPC Library link to program files: https://doi.org/10.17632/22d72yb6k6.1 Developer's repository link: https://github.com/issp-center-dev/PHYSBO Licensing provisions: GNU General Public License version 3 Programming language: Python3 External routines/libraries: NumPy, SciPy, MPI for Python. Nature of problem: Bayesian optimization (BO) can be used to select inputs that will yield better outputs from a list of candidate inputs with the help of machine learning prediction through a Gaussian process. Although BO is a powerful tool, two of its components, training the Gaussian process regression and optimizing the acquisition function, are generally computationally expensive. Moreover, hyperparameter tuning is necessary for the former process. Solution method: PHYSBO is a Python library for performing fast and scalable Bayesian optimization. To avoid the computationally expensive training process, PHYSBO uses a random feature map, Thompson sampling, and a one-rank Cholesky update. In addition, PHYSBO performs hyperparameter tuning automatically by maximizing the Type II likelihood, and MPI parallelization is used to reduce the calculation time for optimizing the acquisition function. Bayesian optimization ; Python (programming language) ; Computer science ; Bayesian probability ; Scalability ; Optimization problem ; Artificial intelligence ; Machine learning ; Algorithm ; Programming language Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Heat Transfer and Optimization
Authors: John Westbrook | Jasmine Young | Chenghua Shao | Zukang Feng | Vladimir Guranović | Catherine L. Lawson | Brinda Vallat | Paul D. Adams | John M. Berrisford | G. Bricogne | Kay Diederichs | Robbie P. Joosten | Peter M. Keller | Nigel W. Moriarty | Oleg V. Sobolev | Sameer Velankar | Clemens Vonrhein | David G. Waterman | Genji Kurisu | Helen M. Berman | S.K. Burley | Ezra Peisach
Source: Journal of Molecular Biology (review, 2022-04-20)
Field: Materials Science — Materials Chemistry

PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide. Protein Data Bank ; Computer science ; Software ; Protein Data Bank (RCSB PDB) ; Data science ; Data bank ; Biological data ; External Data Representation ; Open data ; Information retrieval ; World Wide Web ; Bioinformatics ; Biology ; Artificial intelligence ; Protein structure ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies
Authors: Yanxi Zhang | Gang Ye | Tom P. A. van der Pol | Jingjin Dong | Eveline R. W. van Doremaele | Imke Krauhausen | Yuru Liu | Paschalis Gkoupidenis | Giuseppe Portale | Jun Song | Ryan C. Chiechi | Yoeri van de Burgt
Source: Advanced Functional Materials (article, 2022-04-08)
Field: Materials Science — Polymers and Plastics

High‐Performance Organic Electrochemical Transistors and Neuromorphic Devices Comprising Naphthalenediimide‐Dialkoxybithiazole Copolymers Bearing Glycol Ether Pendant Groups Abstract Organic electrochemical transistors (OECTs) have emerged as building blocks for low power circuits, biosensors, and neuromorphic computing. While p‐type polymer materials for OECTs are well developed, the choice of high‐performance n‐type polymers is limited, despite being essential for cation and metabolite biosensors, and crucial for constructing complementary circuits. N‐type conjugated polymers that have efficient ion‐to‐electron transduction are highly desired for electrochemical applications. In this contribution, three non‐fused, planar naphthalenediimide (NDI)‐dialkoxybithiazole (2Tz) copolymers, which systematically increase the amount of polar tri(ethylene glycol) (TEG) side chains: PNDI2OD‐2Tz (0 TEG), PNDIODTEG‐2Tz (1 TEG), PNDI2TEG‐2Tz (2 TEG), are reported. It is demonstrated that the OECT performance increases with the number of TEG side chains resulting from the progressively higher hydrophilicity and larger electron affinities. Benefiting from the high electron mobility, excellent ion conduction capability, efficient ion‐to‐electron transduction, and low‐lying lowest unoccupied molecular orbital energy level, the 2 TEG polymer achieves close to 10 5 on‐off ratio, fast switching, 1000 stable operation cycles in aqueous electrolyte, and has a long shelf life. Moreover, the higher number TEG chain substituted polymer exhibits good conductance state retention over two orders of magnitudes in electrochemical resistive random‐access memory devices, highlighting its potential for neuromorphic computing. Materials science ; Neuromorphic engineering ; Side chain ; Ethylene glycol ; Electrochemistry ; Polymer ; Copolymer ; Nanotechnology ; Electrolyte ; Conjugated system ; Chemical engineering ; Electrode ; Chemistry ; Computer science ; Composite material Conducting polymers and applications ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications