jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christa P. Baker | Roland Bruderer | James Abbott | J. Simon C. Arthur | Alejandro J. Brenes
Source: Journal of Proteome Research (article, 2024-05-01)
Field: Chemistry — Spectroscopy

Optimizing Spectronaut Search Parameters to Improve Data Quality with Minimal Proteome Coverage Reductions in DIA Analyses of Heterogeneous Samples Data-independent acquisition has seen breakthroughs that enable comprehensive proteome profiling using short gradients. As the proteome coverage continues to increase, the quality of the data generated becomes much more relevant. Using Spectronaut, we show that the default search parameters can be easily optimized to minimize the occurrence of false positives across different samples. Using an immunological infection model system to demonstrate the impact of adjusting search settings, we analyzed <i>Mus musculus</i> macrophages and compared their proteome to macrophages spiked with<i>Candida albicans</i>. This experimental system enabled the identification of "false positives" as <i>Candida albicans</i> peptides and proteins should not be present in the <i>Mus musculus</i>-only samples. We show that adjusting the search parameters reduced "false positive" identifications by 89% at the peptide and protein level, thereby considerably increasing the quality of the data. We also show that these optimized parameters incurred a moderate cost, only reducing the overall number of "true positive" identifications across each biological replicate by <6.7% at both the peptide and protein level. We believe the value of our updated search parameters extends beyond a two-organism analysis and would be of great value to any DIA experiment analyzing heterogeneous populations of cell types or tissues. False positive paradox ; Proteome ; Replicate ; Computational biology ; Biology ; Candida albicans ; Computer science ; Identification (biology) ; Data quality ; Bioinformatics ; Mathematics ; Statistics ; Genetics ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection ; Mass Spectrometry Techniques and Applications
Authors: Wei Liu | Si Ran Wang | Jun Yan Dai | Lei Zhang | Q. M. Chen | Qiang Cheng | Tie Jun Cui
Source: Light Science & Applications (article, 2024-07-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Arbitrarily rotating polarization direction and manipulating phases in linear and nonlinear ways using programmable metasurface Independent controls of various properties of electromagnetic (EM) waves are crucially required in a wide range of applications. Programmable metasurface is a promising candidate to provide an advanced platform for manipulating EM waves. Here, we propose an approach that can arbitrarily control the polarization direction and phases of reflected waves in linear and nonlinear ways using a stacked programmable metasurface. Further, we extend the space-time-coding theory to incorporate the dimension of polarization, which provides an extra degree of freedom for manipulating EM waves. As proof-of-principle application examples, we consider polarization rotation, phase manipulation, and beam steering at linear and nonlinear frequencies. For validation, we design, fabricate, and measure a metasurface sample. The experimental results show good agreement with theoretical predictions and simulations. The proposed approach has a wide range of applications in various areas, such as imaging, data storage, and wireless communication. Polarization (electrochemistry) ; Nonlinear system ; Beam steering ; Physics ; Optics ; Computer science ; Beam (structure) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Kamal Choudhary | Daniel Wines | Kangming Li | Kevin F. Garrity | Vishu Gupta | A. Romero | Jaron T. Krogel | Kayahan Saritas | Addis Fuhr | Panchapakesan Ganesh | Paul R. C. Kent | Keqiang Yan | Yuchao Lin | Shuiwang Ji | Ben Blaiszik | Patrick Reiser | Pascal Friederich | Ankit Agrawal | Pratyush Tiwary | Eric R. Beyerle | Peter Minch | Trevor David Rhone | Ichiro Takeuchi | Robert B. Wexler | Arun Mannodi‐Kanakkithodi | Elif Ertekin | Avanish Mishra | Nithin Mathew | Mitchell Wood | Andrew Rohskopf | Jason Hattrick‐Simpers | Shih‐Han Wang | Luke E. K. Achenie | Hongliang Xin | Maureen Williams | Adam J. Biacchi | Francesca Tavazza
Source: npj Computational Materials (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

JARVIS-Leaderboard: a large scale benchmark of materials design methods Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/ Benchmarking ; Benchmark (surveying) ; Computer science ; NIST ; Computer engineering ; Set (abstract data type) ; Theoretical computer science ; Data science ; Data mining ; Computational science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Hongzhen Liu | Yuantian Zheng | Shengqiang Liu | Jing Zhao | Zhen Song | Dengfeng Peng | Quanlin Liu
Source: Advanced Functional Materials (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

Realizing Red Mechanoluminescence of ZnS: Mn<sup>2+</sup> Through Ferromagnetic Coupling Abstract Self‐recoverable mechanoluminescence (ML) is becoming a novel technology widely used in the fields of sensing, display, and artificial intelligence. The dominant ML material, ZnS: Mn 2+ , is reported to solely present a yellow emission color, which limits the applications of self‐recoverable ML materials to a large extent. Herein, an effective strategy to extend the ML emission range of ZnS: Mn 2+ by the ferromagnetic coupling of Mn 2+ ions are reported. Under the thermal carbon‐reduction atmosphere (TCRA), the emission ranges of ML, photoluminescence (PL), and persistent luminescence spectra of ZnS: Mn 2+ phosphors are all successfully broadened from yellow to red. Furthermore, as for the PL and ML intensities of ZnS: Mn 2+ , they are intensified to 1.76 and 3.23 folds larger under the TCRA treatment than those in pure nitrogen, respectively. Various spectra and magnetic test results reveal that the red emission bands of ZnS: Mn 2+ @TCRA phosphors originate from the ferromagnetic coupling of Mn 2+ ions. This study is the first to realize strong red emission and tunable multicolor luminescence in the conventional ZnS‐based phosphors, which introduces opportunities for discovering the multiband emissions of Mn 2+ ion in other compounds. Brightly multicolored ZnS: x Mn 2+ @TCRA elastic films have been fabricated to demonstrate their anti‐counterfeiting and security applications. Mechanoluminescence ; Phosphor ; Materials science ; Photoluminescence ; Luminescence ; Ion ; Ferromagnetism ; Analytical Chemistry (journal) ; Emission spectrum ; Spectral line ; Optoelectronics ; Condensed matter physics ; Chemistry ; Environmental chemistry Luminescence and Fluorescent Materials ; Advanced Sensor and Energy Harvesting Materials ; Luminescence Properties of Advanced Materials
Authors: Qiaolin Lang | Pengfei Lu | Xiaobo Yang | Valentin Valtchev
Source: Green Carbon (article, 2024-03-01)
Field: Chemistry — Inorganic Chemistry

Zeolites for the environment Zeolites, are characterized by their microporous, crystalline structures with a four-connected framework with variable compositions, predominantly aluminosilicates. They are extensively utilized as adsorbents, catalysts, and ion exchangers across domestic and industrial sectors. With the ongoing energy transition from fossil fuels to renewable sources and the pursuit of environmentally sustainable development, zeolites are increasingly being explored beyond their traditional application fields. They are investigated for their adsorption and catalytic capabilities in the protection and restoration of air, water, and soil quality, as well as in the environmentally friendly “green” production of chemicals. This review article details these novel and potential applications of zeolites, emphasizing the unique properties that render them suitable for each specific use case and discussing how these properties can be fine-tuned through material selection or purpose-driven synthesis methods. Environmentally friendly ; Microporous material ; Aluminosilicate ; Adsorption ; Fossil fuel ; Renewable energy ; Catalysis ; Environmental science ; Biochemical engineering ; Materials science ; Nanotechnology ; Process engineering ; Waste management ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Ecology Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization
Authors: S. Kumuda | Uma Gandhi | M. Umapathy | K. Rajanna
Source: Journal of Materials Science Materials in Electronics (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Synthesis and characterization of graphene oxide and reduced graphene oxide chemically reduced at different time duration Graphene ; Oxide ; Materials science ; Graphene oxide paper ; Characterization (materials science) ; Duration (music) ; Nanotechnology ; Chemical engineering ; Physics ; Engineering ; Metallurgy ; Acoustics Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: Shuyi Li | Shu‐Cong Fan | Peng Zhang | Wenyu Yuan | Ying Wang | Quan‐Guo Zhai
Source: Chem (article, 2024-05-15)
Field: Chemistry — Inorganic Chemistry

Metal-cluster-powered ultramicropore alliance in pore-space-partitioned metal-organic frameworks for benchmark one-step ethylene purification Ethylene ; Adsorption ; Metal-organic framework ; Cluster (spacecraft) ; Benchmark (surveying) ; Metal ; Ligand (biochemistry) ; Selectivity ; Materials science ; Metal ions in aqueous solution ; Ideal (ethics) ; Nanotechnology ; Chemistry ; Chemical engineering ; Combinatorial chemistry ; Organic chemistry ; Computer science ; Catalysis ; Engineering ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Fang Wei | Yan‐Dong Meng | Shu‐Ying Ding | Ju‐Yan Wang | Zheng‐Hao Pei | Meng‐Lan Shen | Chuan‐Zhi Yao | Qiankun Li | Zhenhua Gu | Jie Yu | Hua‐Jie Jiang
Source: Angewandte Chemie International Edition (article, 2024-12-03)
Field: Chemistry — Organic Chemistry

Asymmetric <i>S</i>‐Arylation of Sulfenamides to Access Axially Chiral Sulfilimines Enabled by Anionic Stereogenic‐at‐Cobalt(III) Complexes An efficient enantioselective coupling reaction between sulfenamides and cyclic diaryliodonium salts is established via adaptive Cu/anionic stereogenic-at-Co(III) complex combined catalysis, precisely synthesizing a broad range of axially chiral sulfilimines with excellent enantioselectivities, diastereoselectivities, regioselectivities, and chemoselectivities (67 examples under same conditions, up to 98 % ee). The following thermodynamically controlled pyramidal inversion enables efficient stereodivegent synthesis of all four stereoisomers. Mechanistic studies suggest that anionic stereogenic-at-cobalt(III) complexes serve as counteranions of diaryliodonium and anionic ligand of Cu(I) catalyst simultaneously, which could be regarded as an explanation for outstanding selectivities. Stereocenter ; Enantioselective synthesis ; Cobalt ; Chemistry ; Catalysis ; Combinatorial chemistry ; Ligand (biochemistry) ; Stereochemistry ; Organic chemistry ; Receptor Axial and Atropisomeric Chirality Synthesis ; Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods
Authors: Mingyue Yuan | Alan H. Weible | Fatemeh Azadi | Bangxin Li | Jiacheng Cui | Hualiang Lv | Renchao Che | Xiaoguang Wang
Source: Materials Horizons (review, 2024-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements in high-entropy materials for electromagnetic wave absorption Widespread electromagnetic (EM) interference and pollution have become major issues due to the rapid advancement of fifth-generation (5G) wireless communication technology and devices. Recent advances in high-entropy (HE) materials have opened new opportunities for exploring EM wave absorption abilities to address the issues. The lattice distortion effect of structures, the synergistic effect of multi-element components, and multiple dielectric/magnetic loss mechanisms can offer extensive possibilities for optimizing the balance between impedance matching and attenuation ability, resulting in superior EM wave absorption performance. This review gives a comprehensive review on the recent progress of HE materials for EM wave absorption. We begin with the fundamentals of EM wave absorption materials and the superiority of HE absorbers. Discussions of advanced synthetic methods, in-depth characterization techniques, and electronic properties, especially with regard to regulatable electronic structures through band engineering of HE materials are highlighted. This review also covers current research advancements in a wide variety of HE materials for EM wave absorption, including HE alloys, HE ceramics (mainly HE oxides, carbides, and borides), and other novel HE systems. Finally, insights into future directions for the further development of high-performance HE EM wave absorbers are provided. Materials science ; Absorption (acoustics) ; Electromagnetic radiation ; Entropy (arrow of time) ; Optics ; Physics ; Composite material ; Thermodynamics Electromagnetic wave absorption materials ; Thermal Radiation and Cooling Technologies ; Transition Metal Oxide Nanomaterials
Authors: Lin Tang | Mingshun Jia | Mukun He | Qiqi Liu | Yuhan Lin | Yiting Yi | Xiaolin Liu | Xi Liu | Yusheng Tang | Junwei Gu
Source: SusMat (article, 2024-10-10)
Field: Materials Science — Polymers and Plastics

Fabrication, applications, and prospects for poly(<i>p‐</i>phenylene benzobisoxazole) nanofibers Abstract Polymer nanofibers exhibit unique nanoscale effects, high specific strength and modulus, exceptional design flexibility, large aspect ratios, and substantial specific surface areas. These characteristics have drawn significant attention in emerging fields such as flexible electronics, 5G communications, and new energy vehicles. Notably, poly( p‐ phenylene benzobisoxazole) nanofibers (PNFs) present the best thermal stability and flame retardancy among all known polymer nanofibers. Furthermore, due to the highly oriented molecular chains and orderly structure, PNFs demonstrate superior thermal conductivity compared to conventional polymer nanofibers, thus garnering significant attention and favor from researchers. This paper summarizes the latest research progress of PNFs, detailing three preparation methods (electrospinning, mechanical dissociation, and protonation) along with their respective advantages and disadvantages. It also elucidates the current development status of PNFs in applications such as flame retardancy, thermal conduction, electrical insulation, electromagnetic shielding, and battery separators, and discusses the challenges and prospects faced by PNFs. This paper aims to provide theoretical guidance for the preparation and application of PNFs, enhancing their potential in advanced applications, and further expanding their application scope. Fabrication ; Nanofiber ; Materials science ; Poly(p-phenylene) ; Nanotechnology ; Medicine ; Composite material ; Polymer Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Fiber-reinforced polymer composites
Authors: Xiaolan Duan | Yang Li | Jiahua Zhao | Mengyuan Zhang | Xiaopeng Wang | Li Zhang | Xiaoxuan Ma | Ying Qu | Pengfei Zhang
Source: Journal of the American Chemical Society (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Discovery of Entropy-Stabilized Oxide Catalysts for Catalytic Oxidation The catalytic properties of unary to ternary metal oxides were already well experimentally explored, and the left space seems like only high entropy metal oxides (HEOs, element types ≥5). However, the countless element compositions make the trial-and-error method of discovering HEO catalysts impossible. Herein, based on the study of the crystal phase and catalytic performance of the ACr<sub>2</sub>O<sub><i>x</i></sub> catalyst system, the strong correlation between the single spinel phase and good catalytic activity of CH<sub>4</sub> oxidation was inferred owing to the similar element importance sequences, which were acquired by the corresponding high accuracy machine learning models (cross-validation score >0.7). Furthermore, searching for negative data and choosing the proper training data resulted in high-quality regression models to search for better catalysts. Finally, the screened irregular catalyst Ni<sub>0.04</sub>Co<sub>0.48</sub>Zn<sub>0.36</sub>V<sub>0.12</sub>Cr<sub>2</sub>O<sub><i>x</i></sub> with outstanding sulfur and moisture resistance and long-term stability (>7000 h, <i>T</i><sub>90</sub> = 345 °C) envisions the potential of applying the machine learning method to discover HEOs for target processes. Chemistry ; Catalysis ; Oxide ; Entropy (arrow of time) ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry ; Thermodynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Hao-ran Zhou | Hao Yang | Huaiqian Li | Yingchun Ma | Sen Yu | Jian Lin Shi | Jingchang Cheng | Peng Gao | Bo Yu | Zhiquan Miao | Yanpeng Wei
Source: China Foundry (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Advancements in machine learning for material design and process optimization in the field of additive manufacturing Additive manufacturing technology is highly regarded due to its advantages, such as high precision and the ability to address complex geometric challenges. However, the development of additive manufacturing process is constrained by issues like unclear fundamental principles, complex experimental cycles, and high costs. Machine learning, as a novel artificial intelligence technology, has the potential to deeply engage in the development of additive manufacturing process, assisting engineers in learning and developing new techniques. This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing, particularly in model design and process development. Firstly, it introduces the background and significance of machine learning-assisted design in additive manufacturing process. It then further delves into the application of machine learning in additive manufacturing, focusing on model design and process guidance. Finally, it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing. Process (computing) ; Field (mathematics) ; Manufacturing engineering ; Computer science ; Artificial intelligence ; Machine learning ; Advanced manufacturing ; Development (topology) ; Industrial engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Additive Manufacturing Materials and Processes
Authors: Sadman Sadeed Omee | Nihang Fu | Rongzhi Dong | Ming Hu | Jianjun Hu
Source: npj Computational Materials (article, 2024-07-04)
Field: Materials Science — Materials Chemistry

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study Abstract In real-world materials research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set. Traditional performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN’s significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN) as a case study for the perovskites dataset, and provide insights to improve their performance. Benchmark (surveying) ; Property (philosophy) ; Computer science ; Machine learning ; Generalization ; Redundancy (engineering) ; Artificial intelligence ; Data mining ; Set (abstract data type) ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods