jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yilei Wu | Changfeng Wang | Ming‐Gang Ju | Qiang‐Qiang Jia | Qionghua Zhou | Shuaihua Lu | Xinying Gao | Yi Zhang | Jinlan Wang
Source: Nature Communications (article, 2024-01-02)
Field: Materials Science — Materials Chemistry

Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available. Chemical space ; Computer science ; A priori and a posteriori ; Property (philosophy) ; Throughput ; Drug discovery ; Support vector machine ; Machine learning ; Biochemical engineering ; Artificial intelligence ; Nanotechnology ; Materials science ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Rizvi Syed Aal E Ali | Jiaolong Meng | Muhammad Ehtisham Ibraheem Khan | Xuefeng Jiang
Source: Artificial Intelligence Chemistry (article, 2024-01-19)
Field: Materials Science — Materials Chemistry

Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with chemical intuition to redefine molecular design. As AI chemistry advances, it promises accelerated research, sustainability, and innovative solutions to chemistry’s pressing challenges. The fusion of AI and chemistry is poised to shape the field’s future profoundly, offering new horizons in precision and efficiency. This review encapsulates the transformation of AI in chemistry, marking a pivotal moment where algorithms and data converge to revolutionize the world of molecules. Intuition ; Chemistry ; Artificial intelligence ; Nanotechnology ; Computer science ; Data science ; Cognitive science ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yalong Zhang | Qiuxiang Zhu | Bobo Tian | Chun‐Gang Duan
Source: Nano-Micro Letters (review, 2024-06-25)
Field: Materials Science — Materials Chemistry

New-Generation Ferroelectric AlScN Materials Ferroelectrics have great potential in the field of nonvolatile memory due to programmable polarization states by external electric field in nonvolatile manner. However, complementary metal oxide semiconductor compatibility and uniformity of ferroelectric performance after size scaling have always been two thorny issues hindering practical application of ferroelectric memory devices. The emerging ferroelectricity of wurtzite structure nitride offers opportunities to circumvent the dilemma. This review covers the mechanism of ferroelectricity and domain dynamics in ferroelectric AlScN films. The performance optimization of AlScN films grown by different techniques is summarized and their applications for memories and emerging in-memory computing are illustrated. Finally, the challenges and perspectives regarding the commercial avenue of ferroelectric AlScN are discussed. Ferroelectricity ; Non-volatile memory ; Materials science ; Nanotechnology ; Scaling ; Engineering physics ; Polarization (electrochemistry) ; Optoelectronics ; Computer science ; Dielectric ; Chemistry ; Physics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Ferroelectric and Negative Capacitance Devices
Authors: Sehar Shahzadi | Mariam Akhtar | Muhammad Imran Arshad | Muhammad Hammad Ijaz | Muhammad Ramzan Saeed Ashraf Janjua
Source: RSC Advances (review, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

A review on synthesis of MOF-derived carbon composites: innovations in electrochemical, environmental and electrocatalytic technologies Carbon composites derived from Metal-Organic Frameworks (MOFs) have shown great promise as multipurpose materials for a range of electrochemical and environmental applications. Since carbon-based nanomaterials exhibit intriguing features, they have been widely exploited as catalysts or catalysts supports in the chemical industry or for energy or environmental applications. To improve the catalytic performance of carbon-based materials, high surface areas, variable porosity, and functionalization are thought to be essential. This study offers a thorough summary of the most recent developments in MOF-derived carbon composite synthesis techniques, emphasizing innovative approaches that improve the structural and functional characteristics of the materials. Their uses in electrochemical technologies, such as energy conversion and storage, and their function in environmental electrocatalysis for water splitting and pollutant degradation are also included in the debate. This review seeks to clarify the revolutionary effect of carbon composites formed from MOFs on sustainable technology solutions by analyzing current research trends and innovations, opening the door for further advancements in this rapidly evolving sector. Electrochemistry ; Metal-organic framework ; Materials science ; Carbon fibers ; Nanotechnology ; Composite material ; Chemistry ; Electrode ; Composite number ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Covalent Organic Framework Applications
Authors: Barbara Laïk | Morgane Richet | Nicolas Emery | S. Bach | Loïc Perrière | Yvan Cotrebil | V. Russier | Ivan Guillot | Pierre Dubot
Source: ACS Omega (article, 2024-09-18)
Field: Materials Science — Surfaces, Coatings and Films

XPS Investigation of Co–Ni Oxidized Compounds Surface Using Peak-On-Satellite Ratio. Application to Co<sub>20</sub>Ni<sub>80</sub> Passive Layer Structure and Composition XPS data processing for cobalt and nickel core-level peaks can be complicated. This is especially true when analyzing a mixture of oxide/oxyhydroxide/hydroxide compounds of these metals. The objective of this study is to develop a method for decomposing XPS spectra of 2p core levels for nickel and cobalt-oxidized compounds. This methodology was then employed to study the passivation layer of the Co<sub>20</sub>Ni<sub>80</sub> alloy. The analysis of Ni<sub>2p</sub> and Co<sub>2p</sub> photo peaks using a homemade code based on core-level peak structure and satellites enables us to determine the chemical composition of the surface layer, knowledge of which is particularly important because it is directly linked to the anticorrosive properties it confers on the surface of the oxidized alloy. The XPS analysis, coupled with sequential ion sputtering, revealed that the passive layer of the Co<sub>20</sub>Ni<sub>80</sub> alloy is heterogeneously covered with either oxidized cobalt compounds or oxidized nickel compounds. The method used also demonstrates that the chemical heterogeneity is associated with the thickness heterogeneity of the passive layer. X-ray photoelectron spectroscopy ; Satellite ; Composition (language) ; Layer (electronics) ; Analytical Chemistry (journal) ; Surface layer ; Materials science ; Environmental chemistry ; Chemistry ; Environmental science ; Chemical engineering ; Nanotechnology ; Physics Electron and X-Ray Spectroscopy Techniques ; Catalytic Processes in Materials Science ; Semiconductor materials and devices
Authors: Luyan Wu | Huihui Lin | Xiang Cao | Qiang Tong | Fangqi Yang | Yinxing Miao | Deju Ye | Quli Fan
Source: Angewandte Chemie International Edition (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Bioorthogonal Cu Single‐Atom Nanozyme for Synergistic Nanocatalytic Therapy, Photothermal Therapy, Cuproptosis and Immunotherapy Single-atom nanozymes (SAzymes) with atomically dispersed active sites are potential substitutes for natural enzymes. A systematic study of its multiple functions can in-depth understand SAzymes's nature, which remains elusive. Here, we develop a novel ultrafast synthesis of sputtered SAzymes by in situ bombarding-embedding technique. Using this method, sputtered copper (Cu) SAzymes (CuSA) is developed with unreported unique planar Cu-C<sub>3</sub> coordinated configuration. To enhance the tumor-specific targeting, we employ a bioorthogonal approach to engineer CuSA, denoted as CuSACO. CuSACO not only exhibits minimal off-target toxicity but also possesses exceptional ultrahigh catalase-, oxidase-, peroxidase-like multienzyme activities, resulting in reactive oxygen species (ROS) storm generation for effective tumor destruction. Surprisingly, CuSACO can release Cu ions in the presence of glutathione (GSH) to induce cuproptosis, enhancing the tumor treatment efficacy. Notably, CuSACO's remarkable photothermal properties enables precise photothermal therapy (PTT) on tumors. This, combined with nanozyme catalytic activities, cuproptosis and immunotherapy, efficiently inhibiting the growth of orthotopic breast tumors and gliomas, and lung metastasis. Our research highlights the potential of CuSACO as an innovative strategy to utilize multiple mechanism to enhance tumor therapeutic efficacy, broadening the exploration and development of enzyme-like behavior and physiological mechanism of action of SAzymes. Photothermal therapy ; Immunotherapy ; Bioorthogonal chemistry ; Chemistry ; Medicine ; Materials science ; Nanotechnology ; Combinatorial chemistry ; Immune system ; Immunology ; Click chemistry Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Giuseppe De Giorgio | Biagio Matera | Davide Vurro | Edoardo Manfredi | Vardan Galstyan | Giuseppe Tarabella | Benedetta Ghezzi | Pasquale D’Angelo
Source: Bioengineering (article, 2024-02-09)
Field: Materials Science — Biomaterials

Silk Fibroin Materials: Biomedical Applications and Perspectives The golden rule in tissue engineering is the creation of a synthetic device that simulates the native tissue, thus leading to the proper restoration of its anatomical and functional integrity, avoiding the limitations related to approaches based on autografts and allografts. The emergence of synthetic biocompatible materials has led to the production of innovative scaffolds that, if combined with cells and/or bioactive molecules, can improve tissue regeneration. In the last decade, silk fibroin (SF) has gained attention as a promising biomaterial in regenerative medicine due to its enhanced bio/cytocompatibility, chemical stability, and mechanical properties. Moreover, the possibility to produce advanced medical tools such as films, fibers, hydrogels, 3D porous scaffolds, non-woven scaffolds, particles or composite materials from a raw aqueous solution emphasizes the versatility of SF. Such devices are capable of meeting the most diverse tissue needs; hence, they represent an innovative clinical solution for the treatment of bone/cartilage, the cardiovascular system, neural, skin, and pancreatic tissue regeneration, as well as for many other biomedical applications. The present narrative review encompasses topics such as (i) the most interesting features of SF-based biomaterials, bare SF's biological nature and structural features, and comprehending the related chemo-physical properties and techniques used to produce the desired formulations of SF; (ii) the different applications of SF-based biomaterials and their related composite structures, discussing their biocompatibility and effectiveness in the medical field. Particularly, applications in regenerative medicine are also analyzed herein to highlight the different therapeutic strategies applied to various body sectors. Fibroin ; Biocompatible material ; Biocompatibility ; Biomaterial ; Regenerative medicine ; Tissue engineering ; Nanotechnology ; Materials science ; Regeneration (biology) ; Biomedical engineering ; Sericin ; Self-healing hydrogels ; 3D bioprinting ; SILK ; Chemistry ; Engineering Silk-based biomaterials and applications ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Tie Jun Cui | Shuang Zhang | Andrea Alù | Martin Wegener | J. B. Pendry | Jie Luo | Yun Lai | Zuojia Wang | Xiao Lin | Hongsheng Chen | Pïng Chen | Rui‐Xin Wu | Yuhang Yin | Pengfei Zhao | Huanyang Chen | Yue Li | Ziheng Zhou | Nader Engheta | Viktar Asadchy | Constantin Simovski | Sergei Tretyakov | Biao Yang | Sawyer D. Campbell | Yang Hao | Douglas H. Werner | Shulin Sun | Lei Zhou | Su Xu | Hong‐Bo Sun | Zhou Zhou | Zile Li | Guoxing Zheng | Xianzhong Chen | Tao Li | Shining Zhu | Junxiao Zhou | Junxiang Zhao | Zhaowei Liu | Yuchao Zhang | Qiming Zhang | Miṅ Gu | Shumin Xiao | Yongmin Liu | X. Zhang | Yutao Tang | Guixin Li | Thomas Zentgraf | Kirill Koshelev | Yuri S. Kivshar | Xin Li | Trevon Badloe | Lingling Huang | Junsuk Rho | Shuming Wang | Din Ping Tsai | Anton Yu. Bykov | Alexey V. Krasavin | Anatoly V. Zayats | Cormac McDonnell | Tal Ellenbogen | Xiangang Luo | Mingbo Pu | F. J. Garcı́a-Vidal | Liangliang Liu | Zhuo Li | Wenxuan Tang | Hui Feng | Jingjing Zhang | Yu Luo | Xuanru Zhang | Hao Chi Zhang | Pei Hang He | Le Peng Zhang | Xiang Wan | Hao Wu | Shuo Liu | Wei Jiang | Xin Ge Zhang | Cheng‐Wei Qiu | Qian Ma | Che Liu | Long Li | Jiaqi Han | Lianlin Li | Michele Cotrufo | Christophe Caloz | Z-L Deck-Léger | Amir Bahrami | Oscar Céspedes | Emanuele Galiffi | Paloma A. Huidobro | Qiang Cheng | Jun Yan Dai | Jun Ke | Lei Zhang | Vincenzo Galdi | Marco Di Renzo
Source: Journal of Physics Photonics (article, 2024-01-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Roadmap on electromagnetic metamaterials and metasurfaces Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAM Metamaterial ; Transformation optics ; Physics ; Negative refraction ; Superlens ; Split-ring resonator ; Computer science ; Optics ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Airan Li | Yuechu Wang | Yuzheng Li | Xinlei Yang | Pengfei Nan | Kai Liu | Binghui Ge | Chenguang Fu | Tiejun Zhu
Source: Nature Communications (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

High performance magnesium-based plastic semiconductors for flexible thermoelectrics Low-cost thermoelectric materials with simultaneous high performance and superior plasticity at room temperature are urgently demanded due to the lack of ever-lasting power supply for flexible electronics. However, the inherent brittleness in conventional thermoelectric semiconductors and the inferior thermoelectric performance in plastic organics/inorganics severely limit such applications. Here, we report low-cost inorganic polycrystalline Mg<sub>3</sub>Sb<sub>0.5</sub>Bi<sub>1.498</sub>Te<sub>0.002</sub>, which demonstrates a remarkable combination of large strain (~ 43%) and high figure of merit zT (~ 0.72) at room temperature, surpassing both brittle Bi<sub>2</sub>(Te,Se)<sub>3</sub> (strain ≤ 5%) and plastic Ag<sub>2</sub>(Te,Se,S) and organics (zT ≤ 0.4). By revealing the inherent high plasticity in Mg<sub>3</sub>Sb<sub>2</sub> and Mg<sub>3</sub>Bi<sub>2</sub>, capable of sustaining over 30% compressive strain in polycrystalline form, and the remarkable deformability of single-crystalline Mg<sub>3</sub>Bi<sub>2</sub> under bending, cutting, and twisting, we optimize the Bi contents in Mg<sub>3</sub>Sb<sub>2-x</sub>Bi<sub>x</sub> (x = 0 to 1) to simultaneously boost its room-temperature thermoelectric performance and plasticity. The exceptional plasticity of Mg<sub>3</sub>Sb<sub>2-x</sub>Bi<sub>x</sub> is further revealed to be brought by the presence of a dense dislocation network and the persistent Mg-Sb/Bi bonds during slipping. Leveraging its high plasticity and strength, polycrystalline Mg<sub>3</sub>Sb<sub>2-x</sub>Bi<sub>x</sub> can be easily processed into micro-scale dimensions. As a result, we successfully fabricate both in-plane and out-of-plane flexible Mg<sub>3</sub>Sb<sub>2-x</sub>Bi<sub>x</sub> thermoelectric modules, demonstrating promising power density. The inherent remarkable plasticity and high thermoelectric performance of Mg<sub>3</sub>Sb<sub>2-x</sub>Bi<sub>x</sub> hold the potential for significant advancements in flexible electronics and also inspire further exploration of plastic inorganic semiconductors. Materials science ; Thermoelectric effect ; Plasticity ; Crystallite ; Thermoelectric materials ; Brittleness ; Semiconductor ; Composite material ; Nanotechnology ; Metallurgy ; Optoelectronics ; Thermodynamics ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Thermal properties of materials
Authors: Marwa El‐Azazy | Ahmed I. Osman | Mahmoud Nasr | Yassmin Ibrahim | Nessreen Al‐Hashimi | Khalid Al‐Saad | Mohammad A. Al‐Ghouti | Mohamed F. Shibl | Ala’a H. Al‐Muhtaseb | David W. Rooney | Ahmed S. El‐Shafie
Source: Coordination Chemistry Reviews (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

The interface of machine learning and carbon quantum dots: From coordinated innovative synthesis to practical application in water control and electrochemistry Not long ago, carbon quantum dots (CQDs) came into view as a revolutionary class of materials, propelling advancements in water remediation and electrochemical technology. This comprehensive review explores the cutting-edge developments in CQDs-based materials and their applications, addressing critical challenges in water treatment and electrochemical processes. Synthesized as ultra-tiny, dispersed particles with dimensions less than 10 nm, CQDs exhibit remarkable optical properties, including adjustable fluorescence emission across various colors. With a surge in published scientific articles, CQDs have garnered significant attention, offering potential solutions in heavy metal sensing, remediation, and electrocatalytic hydrogen evolution reactions (HER). The review highlights the high sensitivity of CQDs as fluorescent sensors, detecting contaminants in water with limits of detection down to femtomolar concentrations. Moreover, CQDs demonstrate excellent adsorptive capabilities for heavy metal removal, surpassing traditional adsorbents in terms of removal efficiency. Furthermore, CQDs serve as promising electrocatalysts, enhancing reaction kinetics and enabling efficient water splitting for clean energy generation. Furthermore, this review emphasizes the importance of machine learning in advancing CQDs-based materials, supported by case studies and examples that illustrate how machine learning techniques optimize CQDs synthesis, enhance their properties, and broaden their applications. However, challenges remain in the precise synthesis of CQDs, scalability of production processes, and understanding the interactions between CQDs and pollutants. Overcoming these challenges will unlock the full potential of CQDs-based materials, leading to sustainable and efficient solutions in water control and electrochemical processes. Chemistry ; Electrochemistry ; Interface (matter) ; Quantum dot ; Nanotechnology ; Carbon fibers ; Carbon quantum dots ; Aqueous solution ; Organic chemistry ; Electrode ; Physical chemistry ; Composite number Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Ji Qi | Tsz Wai Ko | Brandon C. Wood | Tuan Anh Pham | Shyue Ping Ong
Source: npj Computational Materials (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study and design of materials. However, MLIPs are only as accurate and robust as the data on which they are trained. Here, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolates more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with the M3GNet universal potential can be used instead of expensive ab initio MD to rapidly create a large configuration space for target systems. We combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. This work paves the way for robust high-throughput development of MLIPs across any compositional complexity. Computer science ; Curse of dimensionality ; Dimensionality reduction ; Sampling (signal processing) ; Interatomic potential ; Machine learning ; Artificial intelligence ; Computational science ; Molecular dynamics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions