jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: P. Scott Pedersen | David C. Blakemore | Gary M. Chinigo | Thomas Knauber | David W. C. MacMillan
Source: Journal of the American Chemical Society (article, 2023-09-20)
Field: Chemistry — Organic Chemistry

One-Pot Synthesis of Sulfonamides from Unactivated Acids and Amines via Aromatic Decarboxylative Halosulfonylation The coupling of carboxylic acids and amines to form amide linkages is the most commonly performed reaction in the pharmaceutical industry. Herein, we report a new strategy that merges these traditional amide coupling partners to generate sulfonamides, important amide bioisosteres. This method leverages copper ligand-to-metal charge transfer (LMCT) to convert aromatic acids to sulfonyl chlorides, followed by one-pot amination to form the corresponding sulfonamide. This process requires no prefunctionalization of the native acid or amine and extends to a diverse set of aryl, heteroaryl, and <i>s</i>-rich aliphatic substrates. Further, we extend this strategy to the synthesis of (hetero)aryl sulfonyl fluorides, which have found utility as "click" handles in chemical probes and programmable bifunctional reagents. Finally, we demonstrate the utility of these protocols in pharmaceutical analogue synthesis. Chemistry ; Sulfonamide ; Amide ; Aryl ; Amination ; Sulfonyl ; Reagent ; Combinatorial chemistry ; Amine gas treating ; Aromatic amine ; Organic chemistry ; Bifunctional ; Catalysis Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions ; Chemical Synthesis and Reactions
Authors: Jose A. Carrasco | Pau Congost‐Escoin | Mhamed Assebban | Gonzalo Abellán
Source: Chemical Society Reviews (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Antimonene: a tuneable post-graphene material for advanced applications in optoelectronics, catalysis, energy and biomedicine The post-graphene era is undoubtedly marked by two-dimensional (2D) materials such as quasi-van der Waals antimonene. This emerging material has a fascinating structure, exhibits a pronounced chemical reactivity (in contrast to graphene), possesses outstanding electronic properties and has been postulated for a plethora of applications. However, chemistry and physics of antimonene remain in their infancy, but fortunately recent discoveries have shed light on its unmatched allotropy and rich chemical reactivity offering a myriad of unprecedented possibilities in terms of fundamental studies and applications. Indeed, antimonene can be considered as one of the most appealing post-graphene 2D materials reported to date, since its structure, properties and applications can be chemically engineered from the ground up (both using top-down and bottom-up approaches), offering an unprecedented level of control in the realm of 2D materials. In this review, we provide an in-depth analysis of the recent advances in the synthesis, characterization and applications of antimonene. First, we start with a general introduction to antimonene, and then we focus on its general chemistry, physical properties, characterization and synthetic strategies. We then perform a comprehensive study on the allotropy, the phase transition mechanisms, the oxidation behaviour and chemical functionalization. From a technological point of view, we further discuss the applications recently reported for antimonene in the fields of optoelectronics, catalysis, energy storage, cancer therapy and sensing. Finally, important aspects such as new scalable methodologies or the promising perspectives in biomedicine are discussed, pinpointing antimonene as a cutting-edge material of broad interest for researchers working in chemistry, physics, materials science and biomedicine. Graphene ; Nanotechnology ; Biomedicine ; Materials science ; Characterization (materials science) ; Bioinformatics 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Hyunchang Park | Tae-Won Kang | Hyunjun Kim | Jeong‐Chul Kim | Zhenan Bao | Jiheong Kang
Source: Nature Communications (article, 2023-08-18)
Field: Materials Science — Polymers and Plastics

Toughening self-healing elastomer crosslinked by metal–ligand coordination through mixed counter anion dynamics Mechanically tough and self-healable polymeric materials have found widespread applications in a sustainable future. However, coherent strategies for mechanically tough self-healing polymers are still lacking due to a trade-off relationship between mechanical robustness and viscoelasticity. Here, we disclose a toughening strategy for self-healing elastomers crosslinked by metal-ligand coordination. Emphasis was placed on the effects of counter anions on the dynamic mechanical behaviors of polymer networks. As the coordinating ability of the counter anion increases, the binding of the anion leads to slower dynamics, thus limiting the stretchability and increasing the stiffness. Additionally, multimodal anions that can have diverse coordination modes provide unexpected dynamicity. By simply mixing multimodal and non-coordinating anions, we found a significant synergistic effect on mechanical toughness ( > 3 fold) and self-healing efficiency, which provides new insights into the design of coordination-based tough self-healing polymers. Toughening ; Elastomer ; Ligand (biochemistry) ; Self-healing ; Ion ; Metal ; Materials science ; Metal ions in aqueous solution ; Polymer chemistry ; Composite material ; Chemistry ; Toughness ; Receptor ; Medicine ; Organic chemistry ; Metallurgy ; Biochemistry Polymer composites and self-healing ; Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposites and Properties
Authors: Di Liu | Jiyan Pei | Xu Zhang | Xiao‐Wen Gu | Hui‐Min Wen | Banglin Chen | Guodong Qian | Bin Li
Source: Angewandte Chemie International Edition (article, 2023-01-24)
Field: Chemistry — Inorganic Chemistry

Scalable Green Synthesis of Robust Ultra‐Microporous Hofmann Clathrate Material with Record C<sub>3</sub>H<sub>6</sub> Storage Density for Efficient C<sub>3</sub>H<sub>6</sub>/C<sub>3</sub>H<sub>8</sub> Separation Developing porous materials for C<sub>3</sub> H<sub>6</sub> /C<sub>3</sub> H<sub>8</sub> separation faces the challenge of merging excellent separation performance with high stability and easy scalability of synthesis. Herein, we report a robust Hofmann clathrate material (ZJU-75a), featuring high-density strong binding sites to achieve all the above requirements. ZJU-75a adsorbs large amount of C<sub>3</sub> H<sub>6</sub> with a record high storage density of 0.818 g mL<sup>-1</sup> , and concurrently shows high C<sub>3</sub> H<sub>6</sub> /C<sub>3</sub> H<sub>8</sub> selectivity (54.2) at 296 K and 1 bar. Single-crystal structure analysis unveil that the high-density binding sites in ZJU-75a not only provide much stronger interactions with C<sub>3</sub> H<sub>6</sub> but also enable the dense packing of C<sub>3</sub> H<sub>6</sub> . Breakthrough experiments on gas mixtures afford both high separation factor of 14.7 and large C<sub>3</sub> H<sub>6</sub> uptake (2.79 mmol g<sup>-1</sup> ). This material is highly stable and can be easily produced at kilogram-scale using a green synthesis method, making it as a benchmark material to address major challenges for industrial C<sub>3</sub> H<sub>6</sub> /C<sub>3</sub> H<sub>8</sub> separation. Clathrate hydrate ; Microporous material ; Hydrogen storage ; Scalability ; Materials science ; Physics ; Chemistry ; Hydrogen ; Computer science ; Hydrate ; Database Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Covalent Organic Framework Applications
Authors: Shihua Yang | Mingge Wang | Tianye Wang | Mengchi Sun | Hanwei Huang | Xianbao Shi | Shijie Duan | Ying Wu | Jiaming Zhu | Funan Liu
Source: Materials Today Bio (review, 2023-04-25)
Field: Materials Science — Biomaterials

Self-assembled short peptides: Recent advances and strategies for potential pharmaceutical applications Self-assembled short peptides have intrigued scientists due to the convenience of synthesis, good biocompatibility, low toxicity, inherent biodegradability and fast response to change in the physiological environment. Therefore, it is necessary to present a comprehensive summary of the recent advances in the last decade regarding the construction, route of administration and application of self-assembled short peptides based on the knowledge on their unique and specific ability of self-assembly. Herein, we firstly explored the molecular mechanisms of self-assembly of short peptides, such as non-modified amino acids, as well as Fmoc-modified, N-functionalized, and C-functionalized peptides. Next, cell penetration, fusion, and peptide targeting in peptide-based drug delivery were characterized. Then, the common administration routes and the potential pharmaceutical applications (drug delivery, antibacterial activity, stabilizers, imaging agents, and applications in bioengineering) of peptide drugs were respectively summarized. Last but not least, some general conclusions and future perspectives in the relevant fields were briefly listed. Although with certain challenges, great opportunities are offered by self-assembled short peptides to the fascinating area of drug development. Biocompatibility ; Nanotechnology ; Drug delivery ; Peptide ; Antibacterial peptide ; Drug ; Chemistry ; Combinatorial chemistry ; Pharmacology ; Medicine ; Materials science ; Biochemistry ; Biology ; Antibacterial activity ; Organic chemistry Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Antimicrobial Peptides and Activities
Authors: Owen Queen | Gavin A. McCarver | Saitheeraj Thatigotla | Brendan P. Abolins | Cameron L. Brown | Vasileios Maroulas | Konstantinos D. Vogiatzis
Source: npj Computational Materials (article, 2023-05-30)
Field: Materials Science — Materials Chemistry

Polymer graph neural networks for multitask property learning Abstract The prediction of a variety of polymer properties from their monomer composition has been a challenge for material informatics, and their development can lead to a more effective exploration of the material space. In this work, PolymerGNN , a multitask machine learning architecture that relies on polymeric features and graph neural networks has been developed towards this goal. PolymerGNN provides accurate estimates for polymer properties based on a database of complex and heterogeneous polyesters (linear/branched, homopolymers/copolymers) with experimentally refined properties. In PolymerGNN , each polyester is represented as a set of monomer units, which are introduced as molecular graphs. A virtual screening of a large, computationally generated database with materials of variable composition was performed, a task that demonstrates the applicability of the PolymerGNN on future studies that target the exploration of the polymer space. Finally, a discussion on the explainability of the models is provided. Computer science ; Monomer ; Graph ; Polymer ; Artificial neural network ; Property (philosophy) ; Materials informatics ; Copolymer ; Variety (cybernetics) ; Artificial intelligence ; Theoretical computer science ; Materials science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Zihan Zou | Yujin Zhang | Lijun Liang | Mingzhi Wei | Jiancai Leng | Jun Jiang | Yi Luo | Wei Hu
Source: Nature Computational Science (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

A deep learning model for predicting selected organic molecular spectra Ultraviolet ; Spectral line ; Raman spectroscopy ; Molecular model ; Quantum chemistry ; Infrared ; Chemistry ; Biological system ; Computational chemistry ; Materials science ; Computer science ; Physics ; Molecule ; Optics ; Stereochemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Jing Lin | Zhimeng Liu | Yujie Guo | Shulin Wang | Tao Zhang | Xiangdong Xue | Rushuo Li | Shihao Feng | Linmeng Wang | Jiangtao Liu | Hongyi Gao | Ge Wang | Yanjing Su
Source: Nano Today (article, 2023-03-10)
Field: Chemistry — Inorganic Chemistry

Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis Computer science ; Rational design ; Nanoporous ; Chemical space ; Space (punctuation) ; Class (philosophy) ; Artificial intelligence ; Nanotechnology ; Systems engineering ; Machine learning ; Materials science ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Haijie He | E Shuang | Li Ai | Xiaogang Wang | Jun Yao | Chuang He | Boyuan Cheng
Source: Journal of Cleaner Production (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Exploiting machine learning for controlled synthesis of carbon dots-based corrosion inhibitors Corrosion ; Random forest ; Mean squared error ; Computer science ; Correlation coefficient ; Process (computing) ; Materials science ; Artificial intelligence ; Machine learning ; Mathematics ; Metallurgy ; Statistics Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Kristof T. Schütt | Stefaan S. P. Hessmann | Niklas W. A. Gebauer | Jonas Lederer | Michael Gastegger
Source: The Journal of Chemical Physics (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

SchNetPack 2.0: A neural network toolbox for atomistic machine learning SchNetPack is a versatile neural network toolbox that addresses both the requirements of method development and the application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks, and a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with a custom code and ready for complex training tasks, such as the generation of 3D molecular structures. Toolbox ; Computer science ; Artificial neural network ; Pipeline (software) ; Interface (matter) ; Artificial intelligence ; Deep learning ; Computer architecture ; Machine learning ; Programming language ; Parallel computing Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Mass Spectrometry Techniques and Applications
Authors: Bibhuti Bhusan Rath | Simon Krause | Bettina V. Lotsch
Source: Advanced Functional Materials (article, 2023-10-15)
Field: Materials Science — Materials Chemistry

Active Site Engineering in Reticular Covalent Organic Frameworks for Photocatalytic CO<sub>2</sub> Reduction Abstract Photochemical CO 2 reduction using ubiquitous sunlight akin to natural photosynthesis is an effective approach for conversion of renewable energy into useful chemical feedstock. Driven by the need for earth‐abundant, inexpensive, and sustainable photocatalysts with practical applicability, covalent organic frameworks (COFs) have emerged as a new generation of molecularly defined semiconductors with tunable optoelectronic properties. These reticular frameworks with highly ordered, porous and crystalline structures can be tailor‐made by covalently combining organic building blocks to target specific functions. To date, numerous COFs have been reported, which show promising activity for photocatalytic CO 2 reduction allowing to derive structure–property–function relationships. In this review, the different reported strategies are comprehensively analyzed and categorized for active site engineering in COF photocatalysts and the synthetic rationale and resulting catalytic activity for each approach are discussed. The recent advancements in terms of tailored photocatalyst design are then critically assessed, aspects of advanced materials characterization are analyzed, and future perspectives and challenges for the field are highlighted. Photocatalysis ; Materials science ; Nanotechnology ; Covalent bond ; Catalysis ; Biochemical engineering ; Organic chemistry ; Chemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yi-Lun Liao | Brandon C. Wood | Abhishek Das | Tess Smidt
Source: arXiv (Cornell University) (preprint, 2023-06-21)
Field: Materials Science — Materials Chemistry

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are limited to small degrees of equivariant representations due to their computational complexity. In this paper, we investigate whether these architectures can scale well to higher degrees. Starting from Equiformer, we first replace $SO(3)$ convolutions with eSCN convolutions to efficiently incorporate higher-degree tensors. Then, to better leverage the power of higher degrees, we propose three architectural improvements -- attention re-normalization, separable $S^2$ activation and separable layer normalization. Putting this all together, we propose EquiformerV2, which outperforms previous state-of-the-art methods on large-scale OC20 dataset by up to $9\%$ on forces, $4\%$ on energies, offers better speed-accuracy trade-offs, and $2\times$ reduction in DFT calculations needed for computing adsorption energies. Additionally, EquiformerV2 trained on only OC22 dataset outperforms GemNet-OC trained on both OC20 and OC22 datasets, achieving much better data efficiency. Finally, we compare EquiformerV2 with Equiformer on QM9 and OC20 S2EF-2M datasets to better understand the performance gain brought by higher degrees. Normalization (sociology) ; Equivariant map ; Separable space ; Scaling ; Computer science ; Leverage (statistics) ; Transformer ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Voltage ; Mathematics ; Pure mathematics ; Geometry ; Engineering ; Electrical engineering ; Mathematical analysis Machine Learning in Materials Science ; Advanced Neural Network Applications ; Topic Modeling
Authors: Mahsa Sedighi | Neha Shrestha | Zahra Mahmoudi | Zahra Khademi | Alireza Ghasempour | Hamideh Dehghan | Seyedeh Fahimeh Talebi | Maryam Toolabi | Véronique Préat | Bo Zhi Chen | Xindong Guo | Mohammad‐Ali Shahbazi
Source: Polymers (review, 2023-02-25)
Field: Materials Science — Biomaterials

Multifunctional Self-Assembled Peptide Hydrogels for Biomedical Applications Self-assembly is a growth mechanism in nature to apply local interactions forming a minimum energy structure. Currently, self-assembled materials are considered for biomedical applications due to their pleasant features, including scalability, versatility, simplicity, and inexpensiveness. Self-assembled peptides can be applied to design and fabricate different structures, such as micelles, hydrogels, and vesicles, by diverse physical interactions between specific building blocks. Among them, bioactivity, biocompatibility, and biodegradability of peptide hydrogels have introduced them as versatile platforms in biomedical applications, such as drug delivery, tissue engineering, biosensing, and treating different diseases. Moreover, peptides are capable of mimicking the microenvironment of natural tissues and responding to internal and external stimuli for triggered drug release. In the current review, the unique characteristics of peptide hydrogels and recent advances in their design, fabrication, as well as chemical, physical, and biological properties are presented. Additionally, recent developments of these biomaterials are discussed with a particular focus on their biomedical applications in targeted drug delivery and gene delivery, stem cell therapy, cancer therapy and immune regulation, bioimaging, and regenerative medicine. Self-healing hydrogels ; Nanotechnology ; Drug delivery ; Regenerative medicine ; Biocompatibility ; Tissue engineering ; Materials science ; Cancer therapy ; Biomedical engineering ; Stem cell ; Cancer ; Engineering ; Biology ; Cell biology Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; RNA Interference and Gene Delivery
Authors: Srinivasa Kartik Nemani | Mohammad Torkamanzadeh | Brian C. Wyatt | Volker Presser | Babak Anasori
Source: Communications Materials (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

Functional two-dimensional high-entropy materials Abstract Multiple principal element or high-entropy materials have recently been studied in the two-dimensional (2D) materials phase space. These promising classes of materials combine the unique behavior of solid-solution and entropy-stabilized systems with high aspect ratios and atomically thin characteristics of 2D materials. The current experimental space of these materials includes 2D transition metal oxides, carbides/carbonitrides/nitrides (MXenes), dichalcogenides, and hydrotalcites. However, high-entropy 2D materials have the potential to expand into other types, such as 2D metal-organic frameworks, 2D transition metal carbo-chalcogenides, and 2D transition metal borides (MBenes). Here, we discuss the entropy stabilization from bulk to 2D systems, the effects of disordered multi-valent elements on lattice distortion and local electronic structures and elucidate how these local changes influence the catalytic and electrochemical behavior of these 2D high-entropy materials. We also provide a perspective on 2D high-entropy materials research and its challenges and discuss the importance of this emerging field of nanomaterials in designing tunable compositions with unique electronic structures for energy, catalytic, electronic, and structural applications. Materials science ; Nanomaterials ; MXenes ; Transition metal ; High entropy alloys ; Nanotechnology ; Entropy (arrow of time) ; Configuration entropy ; Electronic structure ; Chemical physics ; Thermodynamics ; Catalysis ; Condensed matter physics ; Chemistry ; Metallurgy ; Physics ; Microstructure MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Hourui Zhang | Lu Zhang | Shuangshi Dong | Xiaoguang Duan | Dongyang Zhu | Bing‐Jie Ni | Cong Lyu
Source: Journal of Hazardous Materials (article, 2023-01-07)
Field: Materials Science — Materials Chemistry

Regulating energy band structures of triazine covalent organic frameworks with electron-donating/withdrawing substituents for visible-light-responsive photocatalytic tetracycline degradation and Cr(VI) reduction Photocatalysis ; Covalent bond ; Band gap ; Visible spectrum ; Electron transfer ; Photochemistry ; Redox ; Intramolecular force ; Materials science ; Triazine ; Degradation (telecommunications) ; Polar effect ; Nanotechnology ; Charge carrier ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Optoelectronics ; Polymer chemistry ; Catalysis ; Telecommunications ; Computer science Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Caching and Content Delivery