jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiawei Yang | Kaiyu Cui | Xusheng Cai | Jian Xiong | H. Zhu | Shijie Rao | Sheng Xu | Yidong Huang | Fang Liu | Xue Feng | Wei Zhang
Source: Laser & Photonics Review (article, 2022-05-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultraspectral Imaging Based on Metasurfaces with Freeform Shaped Meta‐Atoms Abstract Metasurfaces have an exceptional capacity to manipulate the phase, amplitude, polarization, or spectrum of light. However, unit cells, or meta‐atoms, of metasurfaces are conventionally designed using regular shapes, limiting performance improvement. The utilization of metasurfaces with freeform shaped meta‐atoms for on‐chip ultraspectral imaging is proposed, where the freeform shaped patterns are generated with controllable feature sizes and boundary curvatures for feasible fabrication. These patterns broaden design diversity and enrich metasurface‐unit spectral response with complicated Bloch modes, thus improving spectral imaging performance with enhanced spectrum recovery precision for broadband spectra and smaller center‐wavelength deviation for narrowband spectra. A snapshot on‐chip ultraspectral imaging, with 356 × 436 spectral pixels is experimentally demonstrated. Spectral resolution is state‐of‐the‐art, at 0.5 nm, and mean fidelity of spectral reconstruction for a standard color board reaches 98.78%. These results support future applications in the field of precise intelligent perception. Moreover, the generating method for freeform shaped patterns also benefits for the forward and inverse designs for high‐performance metasurfaces. Narrowband ; Spectral imaging ; Optics ; Chip ; Broadband ; Computer science ; Materials science ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Random lasers and scattering media
Authors: Manas Sajjan | Junxu Li | Raja Selvarajan | Shree Hari Sureshbabu | Sumit Suresh Kale | Rishabh Gupta | Vinit Singh | Sabre Kais
Source: Chemical Society Reviews (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Quantum machine learning for chemistry and physics Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms. Computer science ; Artificial intelligence ; Set (abstract data type) ; Quantum ; Quantum machine learning ; Machine learning ; Generality ; Data science ; Quantum computer ; Physics ; Psychology ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Andreas Bender | Nadine Schneider | Marwin Segler | W. Patrick Walters | Ola Engkvist | Tiago Rodrigues
Source: Nature Reviews Chemistry (review, 2022-05-24)
Field: Materials Science — Materials Chemistry

Evaluation guidelines for machine learning tools in the chemical sciences Workflow ; Computer science ; Credibility ; Transparency (behavior) ; Data science ; Relevance (law) ; Checklist ; Completeness (order theory) ; Artificial intelligence ; Management science ; Psychology ; Epistemology ; Engineering ; Political science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: I. A. Vasyukova | Olga V. Zakharova | Денис Кузнецов | Alexander Gusev
Source: Nanomaterials (review, 2022-05-24)
Field: Materials Science — Materials Chemistry

Synthesis, Toxicity Assessment, Environmental and Biomedical Applications of MXenes: A Review MXenes are a family of two-dimensional (2D) composite materials based on transition metal carbides, nitrides and carbonitrides that have been attracting attention since 2011. Combination of electrical and mechanical properties with hydrophilicity makes them promising materials for biomedical applications. This review briefly discusses methods for the synthesis of MXenes, their potential applications in medicine, ranging from sensors and antibacterial agents to targeted drug delivery, cancer photo/chemotherapy, tissue engineering, bioimaging, and environmental applications such as sensors and adsorbents. We focus on in vitro and in vivo toxicity and possible mechanisms. We discuss the toxicity analogies of MXenes and other 2D materials such as graphene, mentioning the greater biocompatibility of MXenes. We identify existing barriers that hinder the formation of objective knowledge about the toxicity of MXenes. The most important of these barriers are the differences in the methods of synthesis of MXenes, their composition and structure, including the level of oxidation, the number of layers and flake size; functionalization, test concentrations, duration of exposure, and individual characteristics of biological test objects Finally, we discuss key areas for further research that need to involve new methods of nanotoxicology, including predictive computational methods. Such studies will bring closer the prospect of widespread industrial production and safe use of MXene-based products. MXenes ; Nanotechnology ; Biocompatibility ; Biochemical engineering ; Drug delivery ; Materials science ; Engineering MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Bokai Liu | N. Vu‐Bac | Xiaoying Zhuang | Xiaolong Fu | Timon Rabczuk
Source: Composite Structures (article, 2022-03-12)
Field: Materials Science — Materials Chemistry

Stochastic full-range multiscale modeling of thermal conductivity of Polymeric carbon nanotubes composites: A machine learning approach Materials science ; Thermal conductivity ; Carbon nanotube ; Multiscale modeling ; Artificial neural network ; Representative elementary volume ; Finite element method ; Context (archaeology) ; Volume fraction ; Composite material ; Computer science ; Artificial intelligence ; Physics ; Thermodynamics Thermal properties of materials ; Carbon Nanotubes in Composites ; Composite Material Mechanics
Authors: Christos Xiouras | Fabio Cameli | Gustavo Lunardon Quilló | Michail E. Kavousanakis | Dionisios G. Vlachos | Georgios D. Stefanidis
Source: Chemical Reviews (review, 2022-06-27)
Field: Materials Science — Materials Chemistry

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia. Cheminformatics ; Transformative learning ; Artificial intelligence ; Workflow ; Computer science ; Variety (cybernetics) ; Machine learning ; Process (computing) ; Automation ; Bridging (networking) ; Chemistry ; Data science ; Algorithm ; Nanotechnology ; Engineering ; Database Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jennifer D. Lee | Jeffrey B. Miller | Anna V. Shneidman | Lixin Sun | Jason F. Weaver | Joanna Aizenberg | Juergen Biener | J. Anibal Boscoboinik | Alexandre C. Foucher | Anatoly I. Frenkel | Jessi E. S. van der Hoeven | Boris Kozinsky | Nicholas Marcella | M. M. Montemore | Hio Tong Ngan | C. R. O'Connor | Cameron J. Owen | Darı́o Stacchiola | Eric A. Stach | R. J. Madix | Philippe Sautet | C. M. Friend
Source: Chemical Reviews (review, 2022-03-07)
Field: Materials Science — Materials Chemistry

Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition, and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts─in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity─are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag, and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. The integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes. Catalysis ; Chemistry ; Nanoporous ; Selectivity ; Heterogeneous catalysis ; Alloy ; Reactivity (psychology) ; Nanotechnology ; Characterization (materials science) ; Chemical engineering ; Oxide ; Nanoparticle ; Metal ; Materials science ; Organic chemistry Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Marcel F. Langer | Alex Goeßmann | Matthias Rupp
Source: npj Computational Materials (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning Abstract Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We comprehensively review and discuss current representations and relations between them. For selected state-of-the-art representations, we compare energy predictions for organic molecules, binary alloys, and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution, regression method, and hyper-parameter optimization. Interpolation (computer graphics) ; Computer science ; Binary number ; Quantum ; Statistical physics ; Theoretical computer science ; Computational science ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Manuel Vázquez Sulleiro | Antonio Dominguez‐Alfaro | Núria Alegret | Alessandro Silvestri | I. Jénnifer Gómez
Source: Sensing and Bio-Sensing Research (article, 2022-10-25)
Field: Materials Science — Materials Chemistry

2D Materials towards sensing technology: From fundamentals to applications 2D materials raised extensive attention for physical-, chemical- and wearable-sensors, among others. The principal reason is the outstanding capability of 2D materials to detect specific analytes and physical stimuli through diverse responses. After a short introduction to 2D materials, a critical explanation of sensors containing 2D materials is provided to justify their applications. Following by a description of the most relevant transduction mechanisms of 2D materials employed for sensing. This review explores their advancement and implementation in sensing during the last decade in several areas, such as physical, chemical, bio and wearable sensors. Different types of 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), 2D carbides and nitrides of transition metals (MXenes) and black phosphorus (BP), are considered. MXenes ; Graphene ; Nanotechnology ; Black phosphorus ; Hexagonal boron nitride ; Materials science ; Boron nitride ; Wearable computer ; Computer science ; Optoelectronics MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Yang Li | Biao Huang | Xun Zhao | Ziyang Luo | Shunfei Liang | Huizhen Qin | Lingyun Chen
Source: Journal of Power Sources (article, 2022-02-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Zeolitic imidazolate framework-L-assisted synthesis of inorganic and organic anion-intercalated hetero-trimetallic layered double hydroxide sheets as advanced electrode materials for aqueous asymmetric super-capacitor battery Hydroxide ; Materials science ; Zeolitic imidazolate framework ; Imidazolate ; Inorganic chemistry ; Electrochemistry ; Electrode ; Battery (electricity) ; Supercapacitor ; Chemical engineering ; Layered double hydroxides ; Chemistry ; Metal-organic framework ; Organic chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Layered Double Hydroxides Synthesis and Applications
Authors: Fabian Grünewald | Riccardo Alessandri | P. Kroon | Luca Monticelli | Paulo C. T. Souza | ‪Siewert J. Marrink
Source: Nature Communications (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that provides 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating realistic coordinates for polymer melt simulations, single-stranded as well as circular single-stranded DNA. We further demonstrate the power of our approach by setting up a microphase-separated block copolymer system, and by generating a liquid-liquid phase separated system inside a lipid vesicle. Python (programming language) ; Suite ; Computer science ; Nanomaterials ; Nanotechnology ; Programming language ; Materials science Carbon Nanotubes in Composites ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Qiaofeng Yao | Lingmei Liu | Sami Malola | Ge Meng | Hongyi Xu | Zhennan Wu | Tiankai Chen | Yitao Cao | María Francisca Matus | Antti Pihlajamäki | Yu Han | Hannu Häkkinen | Jianping Xie
Source: Nature Chemistry (article, 2022-11-10)
Field: Materials Science — Materials Chemistry

Supercrystal engineering of atomically precise gold nanoparticles promoted by surface dynamics Chemistry ; Nanoparticle ; Linker ; Crystallography ; Nanotechnology ; Chemical physics ; Chemical engineering ; Materials science Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Pharmacological Effects and Toxicity Studies
Authors: Matthew S. Johnson | Xiaorui Dong | Alon Grinberg Dana | Yunsie Chung | David Farina | Ryan J. Gillis | Mengjie Liu | Nathan W. Yee | Katrín Blöndal | Emily Mazeau | Colin A. Grambow | A. Mark Payne | Kevin Spiekermann | Hao‐Wei Pang | C. Franklin Goldsmith | Richard H. West | William H. Green
Source: Journal of Chemical Information and Modeling (article, 2022-10-12)
Field: Chemistry — Organic Chemistry

RMG Database for Chemical Property Prediction The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of &gt;130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources. Thermochemistry ; Estimator ; Computer science ; Database ; Additive function ; Chemistry ; Solvation ; Thermodynamics ; Data mining ; Mathematics ; Solvent ; Physical chemistry ; Physics ; Statistics ; Organic chemistry Free Radicals and Antioxidants ; Chemical Thermodynamics and Molecular Structure ; Machine Learning in Materials Science
Authors: Wenzhe Yi | Ping Xiao | Xiaochen Liu | Zitong Zhao | Xiangshi Sun | Jue Wang | Lei Zhou | Guanru Wang | Haiqiang Cao | Dangge Wang | Yaping Li
Source: Signal Transduction and Targeted Therapy (review, 2022-12-02)
Field: Chemistry — Organic Chemistry

Recent advances in developing active targeting and multi-functional drug delivery systems via bioorthogonal chemistry Bioorthogonal chemistry reactions occur in physiological conditions without interfering with normal physiological processes. Through metabolic engineering, bioorthogonal groups can be tagged onto cell membranes, which selectively attach to cargos with paired groups via bioorthogonal reactions. Due to its simplicity, high efficiency, and specificity, bioorthogonal chemistry has demonstrated great application potential in drug delivery. On the one hand, bioorthogonal reactions improve therapeutic agent delivery to target sites, overcoming off-target distribution. On the other hand, nanoparticles and biomolecules can be linked to cell membranes by bioorthogonal reactions, providing approaches to developing multi-functional drug delivery systems (DDSs). In this review, we first describe the principle of labeling cells or pathogenic microorganisms with bioorthogonal groups. We then highlight recent breakthroughs in developing active targeting DDSs to tumors, immune systems, or bacteria by bioorthogonal chemistry, as well as applications of bioorthogonal chemistry in developing functional bio-inspired DDSs (biomimetic DDSs, cell-based DDSs, bacteria-based and phage-based DDSs) and hydrogels. Finally, we discuss the difficulties and prospective direction of bioorthogonal chemistry in drug delivery. We expect this review will help us understand the latest advances in the development of active targeting and multi-functional DDSs using bioorthogonal chemistry and inspire innovative applications of bioorthogonal chemistry in developing smart DDSs for disease treatment. Bioorthogonal chemistry ; Drug delivery ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Computational biology ; Click chemistry ; Biology ; Materials science Click Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; RNA Interference and Gene Delivery