jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lin Huang | Chengxiang Huang | Ziang Wang | Yiyue Du | Chu Wang | Haocheng Lu | Yunyang Li | Xiaoli Liu | Arthur Jiang | Jia Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectures face critical scalability bottlenecks due to the explicit construction of geometric features or dense tensor products on \textit{every} edge. To overcome this, we introduce \textbf{E2Former-V2}, a scalable architecture that integrates algebraic sparsity with hardware-aware execution. We first propose \textbf{E}quivariant \textbf{A}xis-\textbf{A}ligned \textbf{S}parsification (EAAS). EAAS builds on Wigner-$6j$ convolution by exploiting an $\mathrm{SO}(3) \rightarrow \mathrm{SO}(2)$ change of basis to transform computationally expensive dense tensor contractions into efficient, sparse parity re-indexing operations. Building on this representation, we introduce \textbf{On-the-Fly Equivariant Attention}, a fully node-centric mechanism implemented via a custom fused Triton kernel. By eliminating materialized edge tensors and maximizing SRAM utilization, our kernel achieves a \textbf{20$\times$ improvement in TFLOPS} compared to standard implementations. Extensive experiments on the SPICE and OMol25 datasets demonstrate that E2Former-V2 maintains comparable predictive performance while notably accelerating inference. This work demonstrates that large equivariant transformers can be trained efficiently using widely accessible GPU platforms. The code is avalible at https://github.com/IQuestLab/UBio-MolFM/tree/e2formerv2. Equivariant map ; Scalability ; Algebraic number ; Convolution (computer science) ; Kernel (algebra) ; Tensor product ; Tensor (intrinsic definition) ; Satisfiability modulo theories ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Lin Huang | Chengxiang Huang | Ziang Wang | Yiyue Du | Chu Wang | Haocheng Lu | Yunyang Li | Xiaoli Liu | Arthur Jiang | Jia Zhang
Source: ArXiv.org (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectures face critical scalability bottlenecks due to the explicit construction of geometric features or dense tensor products on \textit{every} edge. To overcome this, we introduce \textbf{E2Former-V2}, a scalable architecture that integrates algebraic sparsity with hardware-aware execution. We first propose \textbf{E}quivariant \textbf{A}xis-\textbf{A}ligned \textbf{S}parsification (EAAS). EAAS builds on Wigner-$6j$ convolution by exploiting an $\mathrm{SO}(3) \rightarrow \mathrm{SO}(2)$ change of basis to transform computationally expensive dense tensor contractions into efficient, sparse parity re-indexing operations. Building on this representation, we introduce \textbf{On-the-Fly Equivariant Attention}, a fully node-centric mechanism implemented via a custom fused Triton kernel. By eliminating materialized edge tensors and maximizing SRAM utilization, our kernel achieves a \textbf{20$\times$ improvement in TFLOPS} compared to standard implementations. Extensive experiments on the SPICE and OMol25 datasets demonstrate that E2Former-V2 maintains comparable predictive performance while notably accelerating inference. This work demonstrates that large equivariant transformers can be trained efficiently using widely accessible GPU platforms. The code is avalible at https://github.com/IQuestLab/UBio-MolFM/tree/e2formerv2. Equivariant map ; Scalability ; Algebraic number ; Convolution (computer science) ; Kernel (algebra) ; Tensor product ; Tensor (intrinsic definition) ; Satisfiability modulo theories ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Jianyu Yang | Xingchen Qiu | Ruian Tang | Hang Zheng | Chen Shao | Lin Sui | Genqi Xu | Yu Gu | Yanling Song | Chunxian Guo
Source: Analytical Chemistry (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Atomic Fe-Centered Hemin–TiO <sub>2</sub> Graphdiyne Nanozyme Sensor for Colorimetric Fingerprinting of Bacterial Extracellular Vesicles Accurate identification of urinary pathogens is essential for the timely diagnosis and effective management of urinary tract infections (UTIs). Bacterial extracellular vesicles (bEVs), which carry pathogen-specific surface antigens, represent an attractive diagnostic target. However, their clinical translation is impeded by the time-consuming and labor-intensive isolation process (typically requiring up to 72 h) from complex biological samples such as urine. To overcome this limitation, we report an intelligent colorimetric sensor array capable of decoding pathogen-specific biochemical signatures for the direct, accurate and rapid identification of bEVs in UTIs. It is realized by inventing a three-dimensional biomimetic nanozyme comprising TiO<sub>2</sub> nanoparticles-bridged Hemin molecules anchored on graphdiyne with a sandwich configuration, thus enabling both selective capture of bEVs and enhanced peroxidase (POD)-like catalytic activity. The specificity arises from the atomic Fe centers in Hemin molecules, which establish stable coordination interactions with the O-antigens of lipopolysaccharides on the bEV surface. Upon bEV recognition, the biomimetic nanozyme exhibits pH-dependent suppression of POD-like activity, generating distinctive colorimetric fingerprints. Coupled with machine learning, the platform accurately classifies bEV subtypes. Clinical validations demonstrate a diagnostic accuracy of 100.0% within 30 min, underscoring the promising point-of-care UTI management. Extracellular vesicles ; Extracellular ; Vesicle ; Colorimetry Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Polydiacetylene-based materials and applications
Authors: Mengyao Si | Hongliu Ding | Xiaoying Wang
Source: Chemical Engineering Journal (article, 2026-01-09)
Field: Chemistry — Electrochemistry

Electrochemiluminescence sensors of heavy metal detection in complex matrices: Detection mechanism, operational mode, and future perspectives Electrochemiluminescence ; Analyte ; Heavy metals ; Key (lock) ; Complex matrix ; Multiplexing Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Jianyu Yang (651828) | Xingchen Qiu (18467701) | Ruian Tang | Hang Zheng (657361) | Chen Shao | Lin Sui | Dong Xu (21616) | Yu Gu (162342) | Yanling Song (131908) | Chunxian Guo
Source: Figshare (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Atomic Fe-CenteredHemin–TiO<sub>2</sub> GraphdiyneNanozyme Sensor for Colorimetric Fingerprinting of Bacterial ExtracellularVesicles Accurate identification of urinary pathogens is essential for the timely diagnosis and effective management of urinary tract infections (UTIs). Bacterial extracellular vesicles (bEVs), which carry pathogen-specific surface antigens, represent an attractive diagnostic target. However, their clinical translation is impeded by the time-consuming and labor-intensive isolation process (typically requiring up to 72 h) from complex biological samples such as urine. To overcome this limitation, we report an intelligent colorimetric sensor array capable of decoding pathogen-specific biochemical signatures for the direct, accurate and rapid identification of bEVs in UTIs. It is realized by inventing a three-dimensional biomimetic nanozyme comprising TiO<sub>2</sub> nanoparticles-bridged Hemin molecules anchored on graphdiyne with a sandwich configuration, thus enabling both selective capture of bEVs and enhanced peroxidase (POD)-like catalytic activity. The specificity arises from the atomic Fe centers in Hemin molecules, which establish stable coordination interactions with the O-antigens of lipopolysaccharides on the bEV surface. Upon bEV recognition, the biomimetic nanozyme exhibits pH-dependent suppression of POD-like activity, generating distinctive colorimetric fingerprints. Coupled with machine learning, the platform accurately classifies bEV subtypes. Clinical validations demonstrate a diagnostic accuracy of 100.0% within 30 min, underscoring the promising point-of-care UTI management. Hemin ; Extracellular vesicles ; Identification (biology) ; Atomic force microscopy ; Isolation (microbiology) ; Clinical diagnosis ; Characterization (materials science) ; Process (computing) Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: M. Raja Nayak | G. Radhika | M. Saritha | Prabhakara Sharma Pidatala | Pradeep Panthagani | B. Devulal | Harish Sesham
Source: Electric Power Systems Research (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Optimization of corona ring design dimensions for extra high-voltage substation bus post insulators using a linear regression approach Busbar ; Corona ring ; Linear regression ; Corona (planetary geology) ; Field (mathematics) ; Voltage ; Electric field ; Optimal design High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Lightning and Electromagnetic Phenomena
Authors: Wencai Ren | Peter Boggild | Joan M. Redwing | Konstantin S Novoselov | Luzhao Sun | Yue Qi | Kaicheng Jia | Zhongfan Liu | Oliver J. Burton | Jack Allen Alexander-Webber | Stephan Hofmann | Yang Cao | Yu Long | Quan Hong Yang | Dan Li | Su Jin Lee | Ki Kang Kim | Young Hee Lee | Mian Li | Qing Huang | Yury Gogotsi | Nick Clark | Amy Carl | Roman Gorbachev | Thomas Olsen | Johanna Rosen | Kristian Sommer Thygesen | Prof Dr Dmitri K Efetov | Bjarke S Jessen | Matthew Yankowitz | Julien Barrier | Roshan Krishna Kumar | Frank Koppens | Hui Deng | Xiaoqin Li | S. Dai | Dmitri Basov | Xinran Wang | Saptarshi Das | Duan, Xiangfeng | Zhihao Yu | Markus Borsch | Andrea C Ferrari | R. Huber | Mackillo Kira | Fengnian Xia | Xiao Wang | Zhong-Shuai Wu | Xinliang Feng | Patrice Simon | Hui-Ming Cheng | Bilu Liu | Yi Xie | Wanqin Jin | Rahul R. Nair | Yan Xu | Hao-Bin Zhang | Vittorio Pellegrini | Bill Qu | Max C. Lemme | Ajit K. Katiyar | Jong-Hyun Ahn | Igor Aharonovich | Mark C. Hersam | S. Roche | Qilin Hua | Guozhen Shen | Tian-Ling Ren | Chong Min Koo | Nikhil A Koratkar | Robert J. Young | Andrew Pollard
Source: Apollo (University of Cambridge) (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

The 2D Materials Roadmap Abstract The advent of 2D materials has revolutionized condensed matter physics and materials science, offering unprecedented opportunities to explore exotic physical phenomena, engineer novel functionalities, and address critical technological challenges across diverse fields. Over the past two decades, the exploration of 2D materials has expanded beyond graphene, encompassing a vast library of atomically thin crystals and their heterostructures. These materials exhibit extraordinary electronic, optical, thermal, mechanical, and chemical properties, and hold promise for breakthroughs in electronics, optoelectronics, quantum technologies, energy storage, catalysis, thermal management, filtration and separation, and beyond. Many exciting new physics and phenomena continue to emerge, while select 2D materials, such as graphene, h-BN, and the semiconducting transition metal dichalcogenides (TMDCs), are transitioning from laboratory-scale demonstrations to industrial applications. In this context, a holistic understanding of synthesis, structure-property relationships, integration, and performance optimization is essential. This roadmap reviews the multifaceted challenges and opportunities in 2D materials research, focusing on the synthesis, properties and applications of representative systems including graphene and its derivatives, TMDCs, MXenes as well as their heterostructures and moiré systems. MXenes ; Energy transition ; Emerging technologies ; Expansive ; Graphene ; Underpinning 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Dong Fan | Felipe Lopes Oliveira | Satyanarayana Bonakala | Mohammad Wahiduzzaman | Guillaume Maurin
Source: Nature Communications (article, 2026-02-26)
Field: Chemistry — Inorganic Chemistry

Decoding local framework dynamics in the ultra-small pore MOF MIL-120(Al) CO2 adsorbent using machine-learning potential Metal-organic frameworks (MOFs) with ultra-small pores offer an optimal environment to effectively capture guest molecules such as CO<sub>2</sub>. Subtle local dynamics of their frameworks, either throughout reorientation of functional groups grafted to the organic linkers or those present in their inorganic nodes, is expected to play a major role in their sorption behaviours. Herein, we investigated the local dynamics of bridging hydroxyl group (μ<sub>2</sub>-OH) in the ultra-small pore MOF MIL-120(Al) using DFT combined with a purpose-trained machine-learning potential (MLP). Six distinct μ<sub>2</sub>-OH configurations were identified with low interconversion barriers (0.07-0.19 eV), indicating significant dynamic behaviour at room temperature. Grand canonical Monte Carlo and hybrid GCMC-MD simulations driven by the MLP demonstrate that adsorption isotherms and low-pressure behaviour are sensitive to μ<sub>2</sub>-OH ordering and whether framework and cell relaxation are considered. While standard rigid force-field simulations overestimated the heat of adsorption, MLP-driven GCMC-MD simulations successfully captured framework relaxation and dynamic μ<sub>2</sub>-OH reorientation under CO<sub>2</sub> loading. This work establishes that a reliable description of the local structure, such as reorientation/flipping of bridging hydroxyl groups, is a key feature to gain an accurate description of the guest locations and energetics in ultra-small pore MOFs. Bridging (networking) ; Molecular dynamics ; Adsorption ; Relaxation (psychology) ; Monte Carlo method ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Irshad Ahmad | Huan Li | Ayman Al‐Qattan | Mohammed T. Alotaibi | Gao Li
Source: Polyoxometalates (article, 2026-02-01)
Field: Chemistry — Inorganic Chemistry

Metal cluster confinement in MOFs and COFs: Advanced synthesis strategies and applications in photocatalysis Atomically precise metal nanoclusters, consisting of tens to hundreds of metal atoms, represent a unique class of catalytic materials with well-defined electronic structures and tunable surface chemistry. These features enable nanoclusters to act as versatile components in photocatalytic systems, where they regulate light absorption, charge separation, and interfacial reaction dynamics. Photocatalysis provides a sustainable pathway for converting solar energy into fuels and value-added chemicals; however, its practical application is limited by intrinsic thermodynamic and kinetic barriers, as well as catalyst stability and selectivity challenges. Recent advances have spotlighted the integration of nanoclusters with extended porous frameworks, including metal–organic frameworks (MOFs) and covalent organic frameworks (COFs), as a powerful strategy to overcome these limitations. These hybrid architectures allow precise control over active-site geometry, electronic environments, and substrate accessibility, while promoting synergistic effects such as enhanced charge transport and stabilization of reactive intermediates. This review highlights emerging synthetic methodologies, modification strategies, and recent photocatalytic applications (CO<sub>2</sub> reduction, H<sub>2</sub> evolution, H<sub>2</sub>O<sub>2</sub> production, organic synthesis, pollutant removal, CH<sub>4</sub> conversion, <em>etc</em>) reported over the past three years. We discuss mechanistic insights, structure–function relationships, and critical challenges, including conductivity, robustness, and scalability. Finally, we propose integrated design principles for constructing hybrid nanocluster–framework photocatalysts with optimized efficiency, selectivity, and durability, offering a roadmap for the rational development of next-generation energy-conversion and chemical-synthesis platforms. Nanoclusters ; Photocatalysis ; Catalysis ; Rational design ; Metal-organic framework ; Charge carrier ; Substrate (aquarium) ; Cluster (spacecraft) Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Amir H. Ashouri | Shayan Shirahmad Gale Bagi | Kavin Satheeskumar | Tejas Srikanth | Jonathan Zhao | Ibrahim Saidoun | Ziwen Wang | Bryan Chan | Tomasz S. Czajkowski
Source: Open MIND (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering The phase ordering problem has been a long-standing challenge since the late 1970s, yet it remains an open problem due to having a vast optimization space and an unbounded nature, making it an open-ended problem without a finite solution, one can limit the scope by reducing the number and the length of optimizations. Traditionally, such locally optimized decisions are made by hand-coded algorithms tuned for a small number of benchmarks, often requiring significant effort to be retuned when the benchmark suite changes. In the past 20 years, Machine Learning has been employed to construct performance models to improve the selection and ordering of compiler optimizations, however, the approaches are not baked into the compiler seamlessly and never materialized to be leveraged at a fine-grained scope of code segments. This paper presents Protean Compiler: An agile framework to enable LLVM with built-in phase-ordering capabilities at a fine-grained scope. The framework also comprises a complete library of more than 140 handcrafted static feature collection methods at varying scopes, and the experimental results showcase speedup gains of up to 4.1% on average and up to 15.7% on select Cbench applications wrt LLVM's O3 by just incurring a few extra seconds of build time on Cbench. Additionally, Protean compiler allows for an easy integration with third-party ML frameworks and other Large Language Models, and this two-step optimization shows a gain of 10.1% and 8.5% speedup wrt O3 on Cbench's Susan and Jpeg applications. Protean compiler is seamlessly integrated into LLVM and can be used as a new, enhanced, full-fledged compiler. We plan to release the project to the open-source community in the near future. Compiler ; Speedup ; Benchmark (surveying) ; Agile software development ; Optimizing compiler ; Suite ; Scope (computer science) ; Machine code ; Key (lock) Block Copolymer Self-Assembly ; Parallel Computing and Optimization Techniques ; Machine Learning in Materials Science
Authors: Amir H. Ashouri | Shayan Shirahmad Gale Bagi | Kavin Satheeskumar | Tejas Srikanth | Jonathan Zhao | Ibrahim Saidoun | Ziwen Wang | Bryan Chan | Tomasz S. Czajkowski
Source: arXiv (Cornell University) (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering The phase ordering problem has been a long-standing challenge since the late 1970s, yet it remains an open problem due to having a vast optimization space and an unbounded nature, making it an open-ended problem without a finite solution, one can limit the scope by reducing the number and the length of optimizations. Traditionally, such locally optimized decisions are made by hand-coded algorithms tuned for a small number of benchmarks, often requiring significant effort to be retuned when the benchmark suite changes. In the past 20 years, Machine Learning has been employed to construct performance models to improve the selection and ordering of compiler optimizations, however, the approaches are not baked into the compiler seamlessly and never materialized to be leveraged at a fine-grained scope of code segments. This paper presents Protean Compiler: An agile framework to enable LLVM with built-in phase-ordering capabilities at a fine-grained scope. The framework also comprises a complete library of more than 140 handcrafted static feature collection methods at varying scopes, and the experimental results showcase speedup gains of up to 4.1% on average and up to 15.7% on select Cbench applications wrt LLVM's O3 by just incurring a few extra seconds of build time on Cbench. Additionally, Protean compiler allows for an easy integration with third-party ML frameworks and other Large Language Models, and this two-step optimization shows a gain of 10.1% and 8.5% speedup wrt O3 on Cbench's Susan and Jpeg applications. Protean compiler is seamlessly integrated into LLVM and can be used as a new, enhanced, full-fledged compiler. We plan to release the project to the open-source community in the near future. Compiler ; Speedup ; Benchmark (surveying) ; Agile software development ; Optimizing compiler ; Suite ; Scope (computer science) ; Machine code ; Key (lock) Block Copolymer Self-Assembly ; Parallel Computing and Optimization Techniques ; Machine Learning in Materials Science
Authors: Zheng Fang | Chen Yang | Haitao Yu | Haoming Luo | Haitao He | Jiaqing Xie | Zhuo Yang | Jun Xia
Source: arXiv (Cornell University) (preprint, 2026-01-22)
Field: Materials Science — Materials Chemistry

NMRGym: A Comprehensive Benchmark for Nuclear Magnetic Resonance Based Molecular Structure Elucidation Nuclear Magnetic Resonance (NMR) spectroscopy is the cornerstone of small-molecule structure elucidation. While deep learning has demonstrated significant potential in automating structure elucidation and spectral simulation, current progress is severely impeded by the reliance on synthetic datasets, which introduces significant domain shifts when applied to real-world experimental spectra. Furthermore, the lack of standardized evaluation protocols and rigorous data splitting strategies frequently leads to unfair comparisons and data leakage. To address these challenges, we introduce \textbf{NMRGym}, the largest and most comprehensive standardized dataset and benchmark derived from high-quality experimental NMR data to date. Comprising \textbf{269,999} unique molecules paired with high-fidelity $^1$H and $^{13}$C spectra, NMRGym bridges the critical gap between synthetic approximations and real-world diversity. We implement a strict quality control pipeline and unify data formats to ensure fair comparison. To strictly prevent data leakage, we enforce a scaffold-based split. Additionally, we provide fine-grained peak-atom level annotations to support future usage. Leveraging this resource, we establish a comprehensive evaluation suite covering diverse downstream tasks, including structure elucidation, functional group prediction from NMR, toxicity prediction from NMR, and spectral simulation, benchmarking representative state-of-the-art methodologies. Finally, we release an open-source leadboard with an automated leaderboard to foster community collaboration and standardize future research. The dataset, benchmark and leaderboard are publicly available at \textcolor{blue}{https://AIMS-Lab-HKUSTGZ.github.io/NMRGym/}. Benchmark (surveying) ; Benchmarking ; Pipeline (software) ; Suite ; Nuclear data ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality