jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luis M. Antunes | Keith T. Butler | Ricardo Grau‐Crespo
Source: Nature Communications (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Crystal structure generation with autoregressive large language modeling The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. However, most current methods for crystal structure prediction are computationally expensive, slowing the pace of innovation. Seeding structure prediction algorithms with quality generated candidates can overcome a major bottleneck. Here, we introduce CrystaLLM, a methodology for the versatile generation of crystal structures, based on the autoregressive large language modeling (LLM) of the Crystallographic Information File (CIF) format. Trained on millions of CIF files, CrystaLLM focuses on modeling crystal structures through text. CrystaLLM can produce plausible crystal structures for a wide range of inorganic compounds unseen in training, as demonstrated by ab initio simulations. Our approach challenges conventional representations of crystals, and demonstrates the potential of LLMs for learning effective models of crystal chemistry, which will lead to accelerated discovery and innovation in materials science. Bottleneck ; Computer science ; Crystal (programming language) ; Crystal structure prediction ; Autoregressive model ; Crystal structure ; Artificial intelligence ; Algorithm ; Machine learning ; Chemistry ; Crystallography ; Econometrics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Guanjie Wang | Changrui Wang | Xuanguang Zhang | Zefeng Li | Jianzhong Zhou | Zhimei Sun
Source: iScience (review, 2024-04-04)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported. Computer science ; Transferability ; Generalization ; Bridge (graph theory) ; Scale (ratio) ; Machine learning ; Artificial intelligence ; Interatomic potential ; Density functional theory ; Computational science ; Molecular dynamics ; Theoretical computer science ; Computational chemistry ; Chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Anthony K. Cheetham | Ram Seshadri
Source: Chemistry of Materials (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography. Perspective (graphical) ; Scaling ; Data science ; Deep learning ; Nanotechnology ; Artificial intelligence ; Computer science ; Cognitive science ; Materials science ; Psychology ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: X.-B. Liu | Kexin Fan | Xinmeng Huang | Jiankai Ge | Yujie Liu | Haisu Kang
Source: Chemical Engineering Journal (article, 2024-04-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances in artificial intelligence boosting materials design for electrochemical energy storage In the rapidly evolving landscape of electrochemical energy storage (EES), the advent of artificial intelligence (AI) has emerged as a keystone for innovation in material design, propelling forward the design and discovery of batteries, fuel cells, supercapacitors, and many other functional materials. This review paper elucidates the burgeoning role of AI in materials from foundational machine learning (ML) techniques to its current pivotal role in advancing the frontiers of materials science for energy storage, including enhancing the performance, durability, and safety of battery technologies, fuel cell efficiency and longevity, and the materials fine-tuning in supercapacitors to achieve superior energy storage capabilities. Collectively, we present a comprehensive overview of the recent AI advancements that have significantly accelerated the development of next-generation materials for EES, offering insights into future research trajectories and the potential for AI to unlock new horizons in materials science. Boosting (machine learning) ; Electrochemical energy storage ; Process engineering ; Energy storage ; Computer science ; Electrochemistry ; Materials science ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Engineering ; Environmental science ; Supercapacitor ; Chemistry ; Physics ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Yan Liu | Qihao Zhang | Aibin Huang | Keyi Zhang | Shun Wan | Hongyi Chen | Yuntian Fu | Wusheng Zuo | Yongzhe Wang | Xun Cao | Lianjun Wang | Uli Lemmer | Wan Jiang
Source: Nature Communications (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Fully inkjet-printed Ag2Se flexible thermoelectric devices for sustainable power generation Flexible thermoelectric devices show great promise as sustainable power units for the exponentially increasing self-powered wearable electronics and ultra-widely distributed wireless sensor networks. While exciting proof-of-concept demonstrations have been reported, their large-scale implementation is impeded by unsatisfactory device performance and costly device fabrication techniques. Here, we develop Ag<sub>2</sub>Se-based thermoelectric films and flexible devices via inkjet printing. Large-area patterned arrays with microscale resolution are obtained in a dimensionally controlled manner by manipulating ink formulations and tuning printing parameters. Printed Ag<sub>2</sub>Se-based films exhibit (00 l)-textured feature, and an exceptional power factor (1097 μWm<sup>-1</sup>K<sup>-2</sup> at 377 K) is obtained by engineering the film composition and microstructure. Benefiting from high-resolution device integration, fully inkjet-printed Ag<sub>2</sub>Se-based flexible devices achieve a record-high normalized power (2 µWK<sup>-2</sup>cm<sup>-2</sup>) and superior flexibility. Diverse application scenarios are offered by inkjet-printed devices, such as continuous power generation by harvesting thermal energy from the environment or human bodies. Our strategy demonstrates the potential to revolutionize the design and manufacture of multi-scale and complex flexible thermoelectric devices while reducing costs, enabling them to be integrated into emerging electronic systems as sustainable power sources. Microscale chemistry ; Inkwell ; Electronics ; Wearable technology ; Printed electronics ; Flexibility (engineering) ; Nanotechnology ; Thermoelectric effect ; Materials science ; Fabrication ; Energy harvesting ; Computer science ; Thermoelectric materials ; Wearable computer ; Power (physics) ; Electrical engineering ; Engineering physics ; Embedded system ; Engineering ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Quantum Dots Synthesis And Properties ; Perovskite Materials and Applications
Authors: Xu Chen | Jing‐Yi Song | Ji Zheng | Yumei Wang | Jie Luo | Puxin Weng | Bing‐Chen Cai | Xiao‐Chun Lin | Guo‐Hong Ning | Dan Li
Source: Journal of the American Chemical Society (article, 2024-07-01)
Field: Chemistry — Inorganic Chemistry

Metal Variance in Multivariate Metal–Organic Frameworks for Boosting Catalytic Conversion of CO<sub>2</sub> Developing efficient, low-cost, MOF catalysts for CO<sub>2</sub> conversion at low CO<sub>2</sub> concentrations under mild conditions is particularly interesting but remains highly challenging. Herein, we prepared an isostructural series of two-dimensional (2D) multivariate metal-organic frameworks (MTV-MOFs) containing copper- and/or silver-based cyclic trinuclear complexes (Cu-CTC and Ag-CTC). These MTV-MOFs can be used as efficient and reusable heterogeneous catalysts for the cyclization of propargylamine with CO<sub>2</sub>. The catalytic performance of these MTV-MOFs can be engineered by fine-tuning the Ag/Cu ratio in the framework. Interestingly, the induction of 10% Ag remarkably improved the catalytic efficiency with a turnover frequency (TOF) of 243 h<sup>-1</sup>, which is 20-fold higher than that of 100% Cu-based MOF (i.e., TOF = 10.8 h<sup>-1</sup>). More impressively, such a bimetallic MOF still exhibited high catalytic activity even for simulated flue gas with 10% CO<sub>2</sub> concentration. Furthermore, the reaction mechanism has been examined through the employment of NMR monitoring experiments and DFT calculations. Chemistry ; Multivariate statistics ; Boosting (machine learning) ; Metal ; Catalysis ; Metal-organic framework ; Multivariate analysis ; Variance (accounting) ; Inorganic chemistry ; Environmental chemistry ; Organic chemistry ; Statistics ; Artificial intelligence ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Hong‐Jing Zhu | Duan‐Hui Si | Hui Guo | Ziao Chen | Rong Cao | Yuan‐Biao Huang
Source: Nature Communications (article, 2024-02-17)
Field: Materials Science — Materials Chemistry

Oxygen-tolerant CO2 electroreduction over covalent organic frameworks via photoswitching control oxygen passivation strategy The direct use of flue gas for the electrochemical CO<sub>2</sub> reduction reaction is desirable but severely limited by the thermodynamically favorable oxygen reduction reaction. Herein, a photonicswitching unit 1,2-Bis(5'-formyl-2'-methylthien-3'-yl)cyclopentene (DAE) is integrated into a cobalt porphyrin-based covalent organic framework for highly efficient CO<sub>2</sub> electrocatalysis under aerobic environment. The DAE moiety in the material can reversibly modulate the O<sub>2</sub> activation capacity and electronic conductivity by the framework ring-closing/opening reactions under UV/Vis irradiation. The DAE-based covalent organic framework with ring-closing type shows a high CO Faradaic efficiency of 90.5% with CO partial current density of -20.1 mA cm<sup>-2</sup> at -1.0 V vs. reversible hydrogen electrode by co-feeding CO<sub>2</sub> and 5% O<sub>2</sub>. This work presents an oxygen passivation strategy to realize efficient CO<sub>2</sub> electroreduction performance by co-feeding of CO<sub>2</sub> and O<sub>2</sub>, which would inspire to design electrocatalysts for the practical CO<sub>2</sub> source such as flue gas from power plants or air. Electrocatalyst ; Passivation ; Electrochemistry ; Oxygen ; Chemistry ; Covalent organic framework ; Faraday efficiency ; Covalent bond ; Moiety ; Reversible hydrogen electrode ; Materials science ; Photochemistry ; Chemical engineering ; Nanotechnology ; Electrode ; Organic chemistry ; Physical chemistry Covalent Organic Framework Applications ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Tengfei Zhang | Peng Zheng | Jiajian Gao | Xiaolong Liu | Yongjun Ji | Junbo Tian | Yang Zou | Zhiyi Sun | Qiao Hu | Guokang Chen | Wenxing Chen | Xi Liu | Ziyi Zhong | Guangwen Xu | Tingyu Zhu | Fabing Su
Source: Nature Communications (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Simultaneously activating molecular oxygen and surface lattice oxygen on Pt/TiO2 for low-temperature CO oxidation Developing high-performance Pt-based catalysts with low Pt loading is crucial but challenging for CO oxidation at temperatures below 100 °C. Herein, we report a Pt-based catalyst with only a 0.15 wt% Pt loading, which consists of Pt-Ti intermetallic single-atom alloy (ISAA) and Pt nanoparticles (NP) co-supported on a defective TiO<sub>2</sub> support, achieving a record high turnover frequency of 11.59 s<sup>-1</sup> at 80 °C and complete conversion of CO at 120 °C. This is because the coexistence of Pt-Ti ISAA and Pt NP significantly alleviates the competitive adsorption of CO and O<sub>2</sub>, enhancing the activation of O<sub>2</sub>. Furthermore, Pt single atom sites are stabilized by Pt-Ti ISAA, resulting in distortion of the TiO<sub>2</sub> lattice within Pt-Ti ISAA. This distortion activates the neighboring surface lattice oxygen, allowing for the simultaneous occurrence of the Mars-van Krevelen and Langmuir-Hinshelwood reaction paths at low temperatures. Oxygen ; Molecular oxygen ; Lattice (music) ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Chemical physics ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Prashant Dubey | Vishal Shrivastav | Tribani Boruah | Giorgio Zoppellaro | Radek Zbořil | Aristides Bakandritsos | Shashank Sundriyal
Source: Advanced Energy Materials (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Unveiling the Potential of Covalent Organic Frameworks for Energy Storage: Developments, Challenges, and Future Prospects Abstract Covalent organic frameworks (COFs) are porous structures emerging as promising electrode materials due to their high structural diversity, controlled and wide pore network, and amenability to chemical modifications. COFs are solely composed of periodically arranged organic molecules, resulting in lightweight materials. Their inherent properties, such as extended surface area and diverse framework topologies, along with their high proclivity to chemical modification, have positioned COFs as sophisticated materials in the realm of electrochemical energy storage (EES). The modular structure of COFs facilitates the integration of key functions such as redox‐active moieties, fast charge diffusion channels, composite formation with conductive counterparts, and highly porous network for accommodating charged energy carriers, which can significantly enhance their electrochemical performance. However, ascribing intricate porosity and redox‐active functionalities to a single COF structure, while maintaining long‐term electrochemical stability, is challenging. Efforts to overcome these hurdles embrace strategies such as the implementation of reversible linkages for structural flexibility, stimuli‐responsive functionalities, and incorporating chemical groups to promote the formation of COF heterostructures. This review focuses on the recent progress of COFs in EES devices, such as batteries and supercapacitors, through a meticulous exploration of the latest strategies aimed at optimizing COFs as advanced electrodes in future EES technologies. Supercapacitor ; Electrochemical energy storage ; Nanotechnology ; Materials science ; Energy storage ; Flexibility (engineering) ; Electrochemistry ; Electrode ; Chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications
Authors: Bingwei Liu | Yan Peng | YuFan Hao | Yiming Zhu | Shengjiang Chang | Songlin Zhuang
Source: PhotoniX (article, 2024-04-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-wideband terahertz fingerprint enhancement sensing and inversion model supported by single-pixel reconfigurable graphene metasurface Abstract The molecular fingerprint sensing technology based on metasurface has unique attraction in the biomedical field. However, in the terahertz (THz) band, existing metasurface designs based on multi-pixel or angle multiplexing usually require more analyte amount or possess a narrower tuning bandwidth. Here, we propose a novel single-pixel graphene metasurface. Based on the synchronous voltage tuning, this metasurface enables ultra-wideband ( $$\sim$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>∼</mml:mo> </mml:math> 1.5 THz) fingerprint enhancement sensing of trace analytes, including chiral optical isomers, with a limit of detection (LoD) ≤ 0.64 μg/mm 2 . The enhancement of the fingerprint signal ( $$\sim$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>∼</mml:mo> </mml:math> 17.4 dB) originates from the electromagnetically induced transparency (EIT) effect excited by the metasurface, and the ideal overlap between the light field constrained by single-layer graphene (SLG) and ultra-thin analyte. Meanwhile, due to the unique nonlinear enhancement mechanism in graphene tuning, the absorption envelope distortion is inevitable. To solve this problem, a universal fingerprint spectrum inversion model is developed for the first time, and the restoration of standard fingerprints reaches R max 2 ≥ 0.99. In addition, the asynchronous voltage tuning of the metasurface provides an opportunity for realizing the dynamic reconfiguration of EIT resonance and the slow light modulation in the broadband range. This work builds a bridge for ultra-wideband THz fingerprint sensing of trace analytes, and has potential applications in active spatial light modulators, slow light devices and dynamic imaging equipments. Terahertz radiation ; Materials science ; Wideband ; Optoelectronics ; Graphene ; Pixel ; Fingerprint (computing) ; Computer science ; Physics ; Optics ; Nanotechnology ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Min‐Jie Zhou | Yulong Miao | Yanwei Gu | Yinjun Xie
Source: Advanced Materials (review, 2024-02-20)
Field: Materials Science — Materials Chemistry

Recent Advances in Reversible Liquid Organic Hydrogen Carrier Systems: From Hydrogen Carriers to Catalysts Liquid organic hydrogen carriers (LOHCs) have gained significant attention for large-scale hydrogen storage due to their remarkable gravimetric hydrogen storage capacity (HSC) and compatibility with existing oil and gas transportation networks for long-distance transport. However, the practical application of reversible LOHC systems has been constrained by the intrinsic thermodynamic properties of hydrogen carriers and the performances of associated catalysts in the (de)hydrogenation cycles. To overcome these challenges, thermodynamically favored carriers, high-performance catalysts, and catalytic procedures need to be developed. Here, significant advances in recent years have been summarized, primarily centered on regular LOHC systems catalyzed by homogeneous and heterogeneous catalysts, including dehydrogenative aromatization of cycloalkanes to arenes and N-heterocyclics to N-heteroarenes, as well as reverse hydrogenation processes. Furthermore, with the development of metal complexes for dehydrogenative coupling, a new family of reversible LOHC systems based on alcohols is described that can release H<sub>2</sub> under relatively mild conditions. Finally, views on the next steps and challenges in the field of LOHC technology are provided, emphasizing new resources for low-cost hydrogen carriers, high-performance catalysts, catalytic technologies, and application scenarios. Materials science ; Hydrogen ; Catalysis ; Energy carrier ; Nanotechnology ; Organic chemistry ; Chemistry Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Hybrid Renewable Energy Systems
Authors: David S. Roberts | Joseph A. Loo | Yury O. Tsybin | Xiaowen Liu | Si Wu | Julia Chamot‐Rooke | Jeffrey N. Agar | Ljiljana Paša‐Tolić | Lloyd M. Smith | Ying Ge
Source: Nature Reviews Methods Primers (article, 2024-06-13)
Field: Chemistry — Spectroscopy

Top-down proteomics Proteomics ; DECIPHER ; Computational biology ; Computer science ; Proteome ; Data science ; Top-down proteomics ; Identification (biology) ; Biomarker discovery ; Quantitative proteomics ; Biology ; Bioinformatics ; Tandem mass spectrometry ; Mass spectrometry ; Chemistry ; Genetics ; Selected reaction monitoring Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies