jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Bucevičius | Rūta Gerasimaitė | Kamila A. Kiszka | Shalini Pradhan | Georgij Kostiuk | Tanja Koenen | Gražvydas Lukinavičius
Source: Nature Communications (article, 2023-03-09)
Field: Chemistry — Organic Chemistry

A general highly efficient synthesis of biocompatible rhodamine dyes and probes for live-cell multicolor nanoscopy The development of live-cell fluorescence nanoscopy is powered by the availability of suitable fluorescent probes. Rhodamines are among the best fluorophores for labeling intracellular structures. Isomeric tuning is a powerful method for optimizing the biocompatibility of rhodamine-containing probes without affecting their spectral properties. An efficient synthesis pathway for 4-carboxyrhodamines is still lacking. We present a facile protecting-group-free 4-carboxyrhodamines' synthesis based on the nucleophilic addition of lithium dicarboxybenzenide to the corresponding xanthone. This approach drastically reduces the number of synthesis steps, expands the achievable structural diversity, increases overall yields and permits gram-scale synthesis of the dyes. We synthesize a wide range of symmetrical and unsymmetrical 4-carboxyrhodamines covering the whole visible spectrum and target them to multiple structures in living cells - microtubules, DNA, actin, mitochondria, lysosomes, Halo-tagged and SNAP-tagged proteins. The enhanced permeability fluorescent probes operate at submicromolar concentrations, allowing high-contrast STED and confocal microscopy of living cells and tissues. STED microscopy ; Fluorescence ; Biophysics ; Rhodamine ; Biocompatibility ; Confocal microscopy ; Microscopy ; Fluorescence microscope ; Chemistry ; Fluorophore ; Nanotechnology ; Materials science ; Biology ; Cell biology ; Stimulated emission Click Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; Advanced Fluorescence Microscopy Techniques
Authors: Xin‐Da Huang | Xiu‐Fang Ma | Li‐Min Zheng
Source: Angewandte Chemie International Edition (article, 2023-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Photo‐responsive Single‐Molecule Magnet Showing 0D to 1D Single‐Crystal‐to‐Single‐Crystal Structural Transition and Hysteresis Modulation Photo-responsive lanthanide-based single-molecule magnets (SMM) hold great promise for future switching and memory devices. Herein, we report a dysprosium phosphonate [Dy<sup>III</sup> (SCN)<sub>2</sub> (NO<sub>3</sub> )(depma)<sub>2</sub> (4-hpy)<sub>2</sub> ] (1Dy), which features a supramolecular framework containing layers of hydrogen-bonding network and pillars of π-π interacted anthracene units. The photocycloaddition reaction of anthracene pairs led to a rapid and reversible single-crystal-to-single-crystal (SC-SC) structural transition to form the 1D coordination polymer [Dy<sup>III</sup> (SCN)<sub>2</sub> (NO<sub>3</sub> )(depma<sub>2</sub> )(4-hpy)<sub>2</sub> ]<sub>n</sub> (2Dy), accompanied by photoswitchable SMM properties with the reduction of effective energy barrier by half and the narrowing of the butterfly-like hysteresis loop. The diluted sample showed a photo-induced switch of the blocking temperature (T<sub>B</sub> ) from 3.8 K for 1Dy@Y to 2.6 K for 2Dy@Y. This work may inspire the construction of lanthanide-based molecular materials with targeted photo-responsive magnetic properties. Single crystal ; Lanthanide ; Dysprosium ; Crystallography ; Single-molecule magnet ; Supramolecular chemistry ; Anthracene ; Materials science ; Molecule ; Hysteresis ; Chemistry ; Crystal structure ; Magnetization ; Photochemistry ; Condensed matter physics ; Inorganic chemistry ; Magnetic field Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Haoke Qiu | Lunyang Liu | Xuepeng Qiu | Xuemin Dai | Xiangling Ji | Zhao‐Yan Sun
Source: Chemical Science (article, 2023-12-06)
Field: Materials Science — Materials Chemistry

PolyNC: a natural and chemical language model for the prediction of unified polymer properties Language models exhibit a profound aptitude for addressing multimodal and multidomain challenges, a competency that eludes the majority of off-the-shelf machine learning models. Consequently, language models hold great potential for comprehending the intricate interplay between material compositions and diverse properties, thereby accelerating material design, particularly in the realm of polymers. While past limitations in polymer data hindered the use of data-intensive language models, the growing availability of standardized polymer data and effective data augmentation techniques now opens doors to previously uncharted territories. Here, we present a revolutionary model to enable rapid and precise prediction of Polymer properties <i>via</i> the power of Natural language and Chemical language (PolyNC). To showcase the efficacy of PolyNC, we have meticulously curated a labeled prompt-structure-property corpus encompassing 22 970 polymer data points on a series of essential polymer properties. Through the use of natural language prompts, PolyNC gains a comprehensive understanding of polymer properties, while employing chemical language (SMILES) to describe polymer structures. In a unified text-to-text manner, PolyNC consistently demonstrates exceptional performance on both regression tasks (such as property prediction) and the classification task (polymer classification). Simultaneous and interactive multitask learning enables PolyNC to holistically grasp the structure-property relationships of polymers. Through a combination of experiments and characterizations, the generalization ability of PolyNC has been demonstrated, with attention analysis further indicating that PolyNC effectively learns structural information about polymers from multimodal inputs. This work provides compelling evidence of the potential for deploying end-to-end language models in polymer research, representing a significant advancement in the AI community's dedicated pursuit of advancing polymer science. Task (project management) ; Computer science ; Polymer ; Natural (archaeology) ; Unified Model ; Artificial intelligence ; Chemistry ; Systems engineering ; Engineering ; Organic chemistry ; Biology ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Tengbo Ma | Kunpeng Ruan | Yongqiang Guo | Yixin Han | Junwei Gu
Source: Science China Materials (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Controlled length and number of thermal conduction pathways for copper wire/poly(lactic acid) composites via 3D printing Materials science ; Thermal conduction ; Composite material ; Thermal conductivity ; Electrical conductor ; Thermal ; Thermodynamics Thermal properties of materials ; Dielectric materials and actuators ; Composite Material Mechanics
Authors: Penghua Ying | Ting Liang | Ke Xu | Jin Zhang | Jianbin Xu | Zheng Zhong | Zheyong Fan
Source: ACS Applied Materials & Interfaces (article, 2023-07-23)
Field: Chemistry — Inorganic Chemistry

Sub-Micrometer Phonon Mean Free Paths in Metal–Organic Frameworks Revealed by Machine Learning Molecular Dynamics Simulations Metal-organic frameworks (MOFs) are a family of materials that have high porosity and structural tunability and hold great potential in various applications, many of which require a proper understanding of the thermal transport properties. Molecular dynamics (MD) simulations play an important role in characterizing the thermal transport properties of various materials. However, due to the complexity of the structures, it is difficult to construct accurate empirical interatomic potentials for reliable MD simulations of MOFs. To this end, we develop a set of accurate yet highly efficient machine-learned potentials for three typical MOFs, including MOF-5, HKUST-1, and ZIF-8, using the neuroevolution potential approach as implemented in the GPUMD package, and perform extensive MD simulations to study thermal transport in the three MOFs. Although the lattice thermal conductivity values of the three MOFs are all predicted to be smaller than 1 W/(m K) at room temperature, the phonon mean free paths (MFPs) are found to reach the sub-micrometer scale in the low-frequency region. As a consequence, the apparent thermal conductivity only converges to the diffusive limit for micrometer single crystals, which means that the thermal conductivity is heavily reduced in nanocrystalline MOFs. The sub-micrometer phonon MFPs are also found to be correlated with a moderate temperature dependence of thermal conductivity between those in typical crystalline and amorphous materials. Both the large phonon MFPs and the moderate temperature dependence of thermal conductivity fundamentally change our understanding of thermal transport in MOFs. Materials science ; Phonon ; Micrometer ; Thermal conductivity ; Molecular dynamics ; Amorphous solid ; Thermal diffusivity ; Nanotechnology ; Chemical physics ; Thermal ; Nanocrystalline material ; Condensed matter physics ; Thermodynamics ; Computational chemistry ; Crystallography ; Physics ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Qiyuan Zhao | Sai Mahit Vaddadi | Michael Woulfe | Lawal Adewale Ogunfowora | Sanjay Garimella | Olexandr Isayev | Brett M. Savoie
Source: Scientific Data (article, 2023-03-20)
Field: Materials Science — Materials Chemistry

Comprehensive exploration of graphically defined reaction spaces Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties. Graph ; Chemistry ; Chemical space ; Molecule ; Computer science ; Transition state ; Product (mathematics) ; Chemical reaction ; Computational chemistry ; Mathematics ; Theoretical computer science ; Organic chemistry ; Drug discovery ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ashwini Gupta | Anindya Bhaduri | Lori Graham‐Brady
Source: Mechanics of Materials (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Accelerated multiscale mechanics modeling in a deep learning framework Materials science ; Multiscale modeling ; Computational mechanics ; Damage mechanics ; Peridynamics ; Continuum mechanics ; Mechanics ; Statistical physics ; Finite element method ; Structural engineering ; Engineering ; Physics Machine Learning in Materials Science ; Composite Material Mechanics ; Drilling and Well Engineering
Authors: Jeong Hyun Han | Yae‐Chan Lim | Ryeong Myeong Kim | Jiawei Lv | Nam Heon Cho | Hyeohn Kim | Seok Daniel Namgung | Sang Won Im | Ki Tae Nam
Source: ACS Nano (article, 2023-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural-Network-Enabled Design of a Chiral Plasmonic Nanodimer for Target-Specific Chirality Sensing Quantitative analysis of chiral molecules in various solvents is essential. However, there are still many challenges to enhancing the sensitivity in precisely determining both concentration and chirality. Here, we built an algorithmic methodology to predict and optimally design the chiroptical response of chiral plasmonic sensors for a specific target chiral analyte with the aid of deep learning. Based upon the analytic and intuitive understanding of the Born-Kuhn type plasmonic nanodimer, we designed and trained the neural networks that can successfully predict the chiroptical properties and further inversely design the plasmonic structure to achieve the intended circular dichroism. The developed algorithm could identify the optimum structure exhibiting the maximum sensitivity for the given specific analytes. Surprisingly, we discovered that sensitivity strongly depends on the various conditions of analytes and can be finely tuned with the structural parameters of plasmonic nanodimers. We envision that this study can provide a general platform to develop ultrasensitive chiral plasmonic sensors whose structure and sensitivity have been evolved algorithmically for adoption in specific applications. Plasmon ; Chirality (physics) ; Circular dichroism ; Analyte ; Sensitivity (control systems) ; Materials science ; Nanotechnology ; Artificial neural network ; Computer science ; Biological system ; Chemistry ; Physics ; Artificial intelligence ; Optoelectronics ; Electronic engineering ; Engineering ; Stereochemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Shuaiming He | Xinpeng Zhao | Emily Q. Wang | Grace S. Chen | Po‐Yen Chen | Liangbing Hu
Source: Annual Review of Materials Research (article, 2023-04-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineered Wood: Sustainable Technologies and Applications Natural wood has been used for construction, fuel, and furniture for thousands of years because of its versatility, renewability, and aesthetic appeal. However, new opportunities for wood are arising as researchers have developed ways to tune the material's optical, thermal, mechanical, and ionic transport properties by chemically and physically modifying wood's naturally porous structure and chemical composition. Such modifications can be used to produce sustainable, functional materials for various emerging applications such as automobiles, construction, energy storage, and environmental remediation. In this review, we highlight recent advancements in engineered wood for sustainable technologies, including thermal and light management, environmental remediation, nanofluidics, batteries, and structural materials with high strength-to-weight ratios. Additionally, the current challenges, opportunities, and future of wood research are discussed, providing a guideline for the further development of next-generation, sustainable wood-based materials. Environmental remediation ; Emerging technologies ; Sustainable development ; Mechanical strength ; Environmental science ; Construction engineering ; Materials science ; Nanotechnology ; Engineering ; Composite material Supercapacitor Materials and Fabrication ; Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials
Authors: Irini Furxhi | Anna Luisa Costa | Socorro Vázquez‐Campos | Carlos Fito | Danail Hristozov | Juan Antonio Tamayo‐Ramos | Susanne Resch | Margherita Cioffi | Steffi Friedrichs | Cris Rocca | Eugenia Valsami‐Jones | Iseult Lynch | Araceli Sánchez Jiménez | Lucian Farcal
Source: RSC Sustainability (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Status, implications and challenges of European safe and sustainable by design paradigms applicable to nanomaterials and advanced materials Current European (EU) policies, such as the Green Deal, envisage safe and sustainable by design (SSbD) practices for the management of chemicals, which cogently entail nanomaterials (NMs) and advanced materials (AdMa). Risk analysis (engineering) ; Nanomaterials ; Nanotechnology ; Business ; Materials science Nanoparticles: synthesis and applications ; Recycling and Waste Management Techniques ; Chemistry and Chemical Engineering
Authors: Manuel Cordova | Pinelopi Moutzouri | Sten O. Nilsson Lill | A. J. P. Cousen | Martin Kearns | Stefan T. Norberg | Anna Svensk Ankarberg | James F. McCabe | Arthur C. Pinon | Staffan Schantz | Lyndon Emsley
Source: Nature Communications (article, 2023-08-23)
Field: Chemistry — Spectroscopy

Atomic-level structure determination of amorphous molecular solids by NMR Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder leads to a high degree of overlap between measured signals, and prevents the unambiguous identification of a single modeled periodic structure as representative of the whole material. Here, we determine the atomic-level ensemble structure of the amorphous form of the drug AZD4625 by combining solid-state NMR experiments with molecular dynamics (MD) simulations and machine-learned chemical shifts. By considering the combined shifts of all <sup>1</sup>H and <sup>13</sup>C atomic sites in the molecule, we determine the structure of the amorphous form by identifying an ensemble of local molecular environments that are in agreement with experiment. We then extract and analyze preferred conformations and intermolecular interactions in the amorphous sample in terms of the stabilization of the amorphous form of the drug. Amorphous solid ; Intermolecular force ; Molecular dynamics ; Materials science ; Molecular solid ; Molecule ; Chemical physics ; Solid-state nuclear magnetic resonance ; Local structure ; Crystallography ; Nuclear magnetic resonance ; Computational chemistry ; Chemistry ; Physics ; Organic chemistry Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Yuxiang Chen | Fengyu Zhang | Zhibo Dang | Xiao He | Chunxiong Luo | Zhengchang Liu | Pu Peng | Yuchen Dai | Yijing Huang | Yu Li | Zheyu Fang
Source: Opto-Electronic Science (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral detection of biomolecules based on reinforcement learning Chirality plays an important role in biological processes, and enantiomers often possess similar physical properties and different physiologic functions. In recent years, chiral detection of enantiomers become a popular topic. Plasmonic metasurfaces enhance weak inherent chiral effects of biomolecules, so they are used in chiral detection. Artificial intelligence algorithm makes a lot of contribution to many aspects of nanophotonics. Here, we propose a nanostructure design method based on reinforcement learning and devise chiral nanostructures to distinguish enantiomers. The algorithm finds out the metallic nanostructures with a sharp peak in circular dichroism spectra and emphasizes the frequency shifts caused by nearfield interaction of nanostructures and biomolecules. Our work inspires universal and efficient machine-learning methods for nanophotonic design. Biomolecule ; Chirality (physics) ; Nanophotonics ; Nanostructure ; Plasmon ; Reinforcement learning ; Nanotechnology ; Enantiomer ; Circular dichroism ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Chemistry ; Optoelectronics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Xin Liu | Alei Dang | Tiehu Li | Yiting Sun | Weibin Deng | Tung-Chun Lee | Yong Yang | Amir Zada | Boning Wang | Yuhui Liu | Shaoheng Wu | Tingkai Zhao
Source: Sensors and Actuators B Chemical (article, 2023-04-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient multiplexed label-free detection by flexible MXene/graphene oxide fibers with enhanced charge transfer and hot spots effect Graphene ; Materials science ; Detection limit ; Multiplexing ; Raman scattering ; Oxide ; Nanotechnology ; Surface plasmon resonance ; Plasmon ; Optoelectronics ; Raman spectroscopy ; Nanoparticle ; Chemistry ; Optics ; Computer science ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Lin Wang | Skyler Quine | Alex N. Frickenstein | Michael A. Lee | Wen Yang | Vinit Sheth | Margaret D. Bourlon | Yuxin He | Shanxin Lyu | Lucila Garcia‐Contreras | Yan D. Zhao | Stefan Wilhelm
Source: Advanced Functional Materials (article, 2023-11-20)
Field: Materials Science — Biomaterials

Exploring and Analyzing the Systemic Delivery Barriers for Nanoparticles Most nanomedicines require efficient <i>in vivo</i> delivery to elicit diagnostic and therapeutic effects. However, en route to their intended tissues, systemically administered nanoparticles often encounter delivery barriers. To describe these barriers, we propose the term "nanoparticle blood removal pathways" (NBRP), which summarizes the interactions between nanoparticles and the body's various cell-dependent and cell-independent blood clearance mechanisms. We reviewed nanoparticle design and biological modulation strategies to mitigate nanoparticle-NBRP interactions. As these interactions affect nanoparticle delivery, we studied the preclinical literature from 2011-2021 and analyzed nanoparticle blood circulation and organ biodistribution data. Our findings revealed that nanoparticle surface chemistry affected the <i>in vivo</i> behavior more than other nanoparticle design parameters. Combinatory biological-PEG surface modification improved the blood area under the curve by ~418%, with a decrease in liver accumulation of up to 47%. A greater understanding of nanoparticle-NBRP interactions and associated delivery trends will provide new nanoparticle design and biological modulation strategies for safer, more effective, and more efficient nanomedicines. Nanoparticle ; Biodistribution ; Nanotechnology ; Materials science ; Drug delivery ; In vivo ; Blood circulation ; Systemic circulation ; Nanomedicine ; Medicine ; Biology Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications