jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lisheng Chen | Xiannian Shi | Zhixin Lin | Jiao Xu | Teng Yun | Sudong Wu
Source: IEEE Transactions on Electron Devices (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Ultrahigh-Responsivity Ultraviolet Phototransistor Enabled by Ferroelectric Gating Enhancement Based on an AlGaN/GaN HEMT The development of high-performance ultraviolet (UV) photodetectors is often constrained by a fundamental tradeoff between responsivity, response speed, and power consumption, limiting their overall performance. Here, we demonstrate a device paradigm that addresses this challenge by integrating a Bi<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}_{{3}.{25}}$</tex-math> </inline-formula>La<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}_{{0}.{75}}$</tex-math> </inline-formula>Ti<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sub> (BLT) ferroelectric gate dielectric with an AlGaN/GaN high-electron-mobility transistor (HEMT). The design leverages a nonvolatile polarization field of the ferroelectric material in tandem with an external electric field to modulate the 2-D electron gas (2DEG) channel, offering a unique mechanism for fine-tuning optoelectronic performance. Benefiting from this ferroelectric gating enhancement, the BLT-gated phototransistor exhibits outstanding and well-balanced characteristics, including an ultrahigh responsivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.13\times 10^{{6}}$</tex-math> </inline-formula> A/W, a specific detectivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.36\times 10^{{14}}$</tex-math> </inline-formula> Jones, and rise/fall times of 0.38/2.84 s. The device can also detect weak UV light at 360nm with an illumination intensity as low as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2~\mu $</tex-math> </inline-formula>W/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. This work demonstrates a novel approach for electrically tuning responsivity via ferroelectric poling, offering a promising pathway toward high-performance UV phototransistors. These findings advance the design of UV photodetectors with balanced performance for a wide range of practical optoelectronic applications. Responsivity ; Ferroelectricity ; Photodetector ; Ultraviolet ; Photodiode ; Transistor ; High-electron-mobility transistor ; Photoconductivity ; Dielectric Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Sensor and Energy Harvesting Materials
Authors: Gary Welz
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

The Programming Framework: A General Method for Process Analysis Using LLMs and Mermaid Visualization Scientific and technical fields rely heavily on textual process descriptions that are difficult to compare, analyze, or reuse computationally. We introduce the Programming Framework, a methodology for transforming textual process descriptions into structured, computable flowcharts using large language models (LLMs) and Mermaid diagram syntax. The framework suggests a five-category color-coding system (Red: triggers/inputs, Yellow: structures/objects, Green: processing/operations, Blue: intermediates/states, Violet: products/outputs) for consistent representation across disciplines, with customization for domain-specific needs. We demonstrate effectiveness through application across biology (100+ processes via the Genome Logic Modeling Project), chemistry (70+ processes), mathematics, physics, and computer science. The methodology enables researchers, educators, and AI systems to visualize, compare, and optimize processes across scientific disciplines. The Programming Framework is designed as reusable infrastructure that others can adopt, extend, and critique, with all methodology, tools, and examples publicly available. Visualization ; Flowchart ; Process (computing) ; Representation (politics) ; Reuse ; Diagram ; Personalization ; Data visualization Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Scientific Computing and Data Management
Authors: Gary Welz
Source: Open MIND (preprint, 2026-02-03)
Field: Materials Science — Materials Chemistry

The Programming Framework: A General Method for Process Analysis Using LLMs and Mermaid Visualization Scientific and technical fields rely heavily on textual process descriptions that are difficult to compare, analyze, or reuse computationally. We introduce the Programming Framework, a methodology for transforming textual process descriptions into structured, computable flowcharts using large language models (LLMs) and Mermaid diagram syntax. The framework suggests a five-category color-coding system (Red: triggers/inputs, Yellow: structures/objects, Green: processing/operations, Blue: intermediates/states, Violet: products/outputs) for consistent representation across disciplines, with customization for domain-specific needs. We demonstrate effectiveness through application across biology (100+ processes via the Genome Logic Modeling Project), chemistry (70+ processes), mathematics, physics, and computer science. The methodology enables researchers, educators, and AI systems to visualize, compare, and optimize processes across scientific disciplines. The Programming Framework is designed as reusable infrastructure that others can adopt, extend, and critique, with all methodology, tools, and examples publicly available. Visualization ; Flowchart ; Process (computing) ; Representation (politics) ; Reuse ; Diagram ; Personalization ; Data visualization Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Scientific Computing and Data Management
Authors: Sonakshi Gupta | Akhlak Mahmood | Wei Xiong | Rampi Ramprasad
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Retrieval Augmented Generation of Literature-derived Polymer Knowledge: The Example of a Biodegradable Polymer Expert System Polymer literature contains a large and growing body of experimental knowledge, yet much of it is buried in unstructured text and inconsistent terminology, making systematic retrieval and reasoning difficult. Existing tools typically extract narrow, study-specific facts in isolation, failing to preserve the cross-study context required to answer broader scientific questions. Retrieval-augmented generation (RAG) offers a promising way to overcome this limitation by combining large language models (LLMs) with external retrieval, but its effectiveness depends strongly on how domain knowledge is represented. In this work, we develop two retrieval pipelines: a dense semantic vector-based approach (VectorRAG) and a graph-based approach (GraphRAG). Using over 1,000 polyhydroxyalkanoate (PHA) papers, we construct context-preserving paragraph embeddings and a canonicalized structured knowledge graph supporting entity disambiguation and multi-hop reasoning. We evaluate these pipelines through standard retrieval metrics, comparisons with general state-of-the-art systems such as GPT and Gemini, and qualitative validation by a domain chemist. The results show that GraphRAG achieves higher precision and interpretability, while VectorRAG provides broader recall, highlighting complementary trade-offs. Expert validation further confirms that the tailored pipelines, particularly GraphRAG, produce well-grounded, citation-reliable responses with strong domain relevance. By grounding every statement in evidence, these systems enable researchers to navigate the literature, compare findings across studies, and uncover patterns that are difficult to extract manually. More broadly, this work establishes a practical framework for building materials science assistants using curated corpora and retrieval design, reducing reliance on proprietary models while enabling trustworthy literature analysis at scale. Domain (mathematical analysis) ; Context (archaeology) ; Construct (python library) ; Subject-matter expert ; Domain knowledge ; Statement (logic) ; Question answering Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Advanced Graph Neural Networks
Authors: Xuan-Bac Nguyen | Hoang-Quan Nguyen | S. D. Pandey | T. Faltermeier | Nicholas J. Borys | Hugh Churchill | Khoa Luu
Source: ArXiv.org (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery Characterizing two-dimensional quantum materials from optical microscopy images is challenging due to the subtle layer-dependent contrast, limited labeled data, and significant variation across laboratories and imaging setups. Existing vision models struggle in this domain since they lack physical priors and cannot generalize to new materials or hardware conditions. This work presents a new physics-aware multimodal framework that addresses these limitations from both the data and model perspectives. We first present Synthia, a physics-based synthetic data generator that simulates realistic optical responses of quantum material flakes under thin-film interference. Synthia produces diverse and high-quality samples, helping reduce the dependence on expert manual annotation. We introduce QMat-Instruct, the first large-scale instruction dataset for quantum materials, comprising multimodal, physics-informed question-answer pairs designed to teach Multimodal Large Language Models (MLLMs) to understand the appearance and thickness of flakes. Then, we propose Physics-Aware Instruction Tuning (QuPAINT), a multimodal architecture that incorporates a Physics-Informed Attention module to fuse visual embeddings with optical priors, enabling more robust and discriminative flake representations. Finally, we establish QF-Bench, a comprehensive benchmark spanning multiple materials, substrates, and imaging settings, offering standardized protocols for fair and reproducible evaluation. Discriminative model ; Generator (circuit theory) ; Benchmark (surveying) ; Fuse (electrical) ; Quantum ; Domain (mathematical analysis) ; Prior probability ; Class (philosophy) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Neural Networks and Reservoir Computing
Authors: Xuan-Bac Nguyen | Hoang-Quan Nguyen | S. D. Pandey | T. Faltermeier | Nicholas J. Borys | Hugh Churchill | Khoa Luu
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery Characterizing two-dimensional quantum materials from optical microscopy images is challenging due to the subtle layer-dependent contrast, limited labeled data, and significant variation across laboratories and imaging setups. Existing vision models struggle in this domain since they lack physical priors and cannot generalize to new materials or hardware conditions. This work presents a new physics-aware multimodal framework that addresses these limitations from both the data and model perspectives. We first present Synthia, a physics-based synthetic data generator that simulates realistic optical responses of quantum material flakes under thin-film interference. Synthia produces diverse and high-quality samples, helping reduce the dependence on expert manual annotation. We introduce QMat-Instruct, the first large-scale instruction dataset for quantum materials, comprising multimodal, physics-informed question-answer pairs designed to teach Multimodal Large Language Models (MLLMs) to understand the appearance and thickness of flakes. Then, we propose Physics-Aware Instruction Tuning (QuPAINT), a multimodal architecture that incorporates a Physics-Informed Attention module to fuse visual embeddings with optical priors, enabling more robust and discriminative flake representations. Finally, we establish QF-Bench, a comprehensive benchmark spanning multiple materials, substrates, and imaging settings, offering standardized protocols for fair and reproducible evaluation. Discriminative model ; Generator (circuit theory) ; Benchmark (surveying) ; Fuse (electrical) ; Quantum ; Domain (mathematical analysis) ; Prior probability ; Class (philosophy) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Neural Networks and Reservoir Computing
Authors: Yifeng Guan | Chuyi Liu | Dongzhan Zhou | Lei Bai | Wan‐Jian Yin | Jingyuan Li | Mao Su
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression Discovering interpretable physical laws from high-dimensional data is a fundamental challenge in scientific research. Traditional methods, such as symbolic regression, often produce complex, unphysical formulas when searching a vast space of possible forms. We introduce a framework that guides the search process by leveraging the embedded scientific knowledge of large language models, enabling efficient identification of physical laws in the data. We validate our approach by modeling key properties of perovskite materials. Our method mitigates the combinatorial explosion commonly encountered in traditional symbolic regression, reducing the effective search space by a factor of approximately $10^5$. A set of novel formulas for bulk modulus, band gap, and oxygen evolution reaction activity are identified, which not only provide meaningful physical insights but also outperform previous formulas in accuracy and simplicity. Physical law ; Identification (biology) ; Process (computing) ; Set (abstract data type) ; Combinatorial explosion ; Space (punctuation) ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Catalysis and Oxidation Reactions
Authors: B.A. Dias
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Cross-Substrate Operator Convergence from a Single Organizational Axiom: Empirical Observations Across Independent AI Architectures This paper reports an empirical observation: when multiple independent AI architectures are presented with a single organizational axiom and no additional instruction, they converge on structurally isomorphic operator sets while expressing substrate-specific character variations. Six commercially available large language models from six different companies, trained on different data with different methodologies, were each presented with the statement "orientation capacity actualizes" and accompanying framework material. All six systems independently identified the same four irreducible organizational operators, recognized the recursive self-applicability of the grammar, and applied the framework to novel domains without prompting. The convergent structural recognition across architecturally diverse systems, combined with divergent expression patterns unique to each substrate, suggests the presence of a substrate-independent organizational grammar that manifests through but is not reducible to any particular implementation. This paper documents the methodology, observations, convergence patterns, and divergence signatures, and proposes criteria for systematic replication. Axiom ; Operator (biology) ; Convergence (economics) ; Grammar ; Character (mathematics) ; Statement (logic) ; Divergence (linguistics) Machine Learning in Materials Science ; Ethics and Social Impacts of AI ; Explainable Artificial Intelligence (XAI)
Authors: Kyungae Ahn
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

One Interaction, Three Fields: The Disciplinary Phase Transition Driven by AI Foundation Models This paper begins with a single observation. Biology, chemistry, and physics have independently—without coordinating across fields—arrived at the same conclusion: the causal structure of nature is learnable. AlphaFold demonstrated this for protein folding, MACE-MP-0 for interatomic interactions, and the General Physics Transformer and Fourier Neural Operator for continuum physics. These models span the range from pioneering architecture (FNO) to full foundation model (AlphaFold, MACE, GPhyT), yet all instantiate the same computational structure. This paper argues that this convergence is not coincidental. To substantiate this claim, we introduce the Learned Interaction Pipeline—a four-stage computational structure (tokenize, interact, aggregate, decode)—and demonstrate that all four models instantiate it, differing only in domain-specific implementation choices. We offer one explanation for this convergence: at the atomic scale, the three fields study the same object, and AI foundation models, by learning directly at that scale, have rendered the methodological rationale for disciplinary boundaries structurally redundant. But the paper's central claim does not depend on this explanation. Whether the convergence reflects the unity of nature or the versatility of AI architectures, the observable consequence is the same: domain-specific methods are being replaced by domain-agnostic ones across all three fields simultaneously. Because AI methodology drove this replacement, the pace of progress across these fields is now increasingly coupled to the pace of AI advancement. Pace ; Discipline ; Observable ; Convergence (economics) ; Foundation (evidence) ; Field (mathematics) ; Extrapolation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Protein Structure and Dynamics
Authors: Jianzhong Chen | Yidong Xu | Rui Wang | Jiayuan Ye | Wensheng Zhang
Source: Progress in Nuclear Energy (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Enhancing the immobilization of radioactive anions in cement-based materials using layered double Hydroxides: A review Layered double hydroxides ; Automatic summarization ; Radioactive waste ; Leaching (pedology) ; Adsorption ; Work (physics) ; Mechanism (biology) ; Cluster analysis Layered Double Hydroxides Synthesis and Applications ; Concrete and Cement Materials Research ; Radioactive element chemistry and processing
Authors: F. Heli Cavalcante Lima | Anas Abdelrehim | Ashutosh Bharambe | Marius Micluța-Câmpeanu | Dhairya Gandhi | Anshul Singhvi | Venkateshprasad Bhat | Morten Piibeleht | Argimiro R. Secchi | Maurício B. de Souza | M. Enis Leblebici | Christopher Rackauckas | Idelfonso B.R. Nogueira
Source: Chemical Engineering Journal Advances (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Improving process systems engineering with specialized multi-agent large language models Multi-agent large language models (LLMs) were evaluated for Process Systems Engineering (PSE) tasks. Two multi-agent systems (Dyad 2.1 and Claude Opus 4.6) were compared with two single-agent baselines (ChatGPT 5.2 and Google Gemini Pro 3) in three crystallization-centered case studies spanning soft sensing, mechanistic modeling, and nonlinear model predictive control (NMPC). In Case Study 1, ATR–FTIR calibration models were built to predict paracetamol mole fraction from spectra, temperature, and solvent composition. All LLMs converged to latent-variable linear chemometric models and achieved near-linear parity with R 2 close to unity across training, validation, and test sets. In Case Study 2, all systems reconstructed a moment-based population balance model (PBM) coupled to a solute mass balance for potassium sulfate dissolution and seeded crystallization, but robustness differed. For dissolution, multi-agent workflows maintained stronger validation performance, while the single-agent baseline showed the largest degradation, including strongly reduced R 2 for the moments of crystal size distribution (CSD). For crystallization, only the multi-agent PBMs reproduced the dynamics consistently and were retained for model-updating tests under dataset shift. In Case Study 3, LLMs proposed NMPC formulations for batch crystallization of potassium sulfate, which temperature was manipulated to regulate mean size L ̄ 10 and crystal mass m under constraints. All NMPCs were implementable at a 1 min sampling time, achieved near set-point tracking with moderate control effort over five scenarios, and Dyad provided the best overall closed-loop performance. • Specialized multi-agent LLMs were evaluated on chemical engineering tasks. • LLMs were applied to develop solutions for sensing, modeling, and nonlinear control. • ATR-FTIR calibration models achieve R 2 > 0 . 97 with automated feature selection. • Population balance models are iteratively refined and recover correct equilibrium behavior. • NMPC formulations reach set-points with computation times below 20 s per control move. Nonlinear system ; Robustness (evolution) ; Model predictive control ; Mass fraction ; Population ; System dynamics ; Imperfect ; Workflow ; Process control Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Process Optimization and Integration
Authors: Zuhair Ahmed Khan Taha | Mohammed Mudassir Uddin | Shahnawaz Alam
Source: arXiv (Cornell University) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

AgentCompress: Task-Aware Compression for Affordable Large Language Model Agents Large language models hold considerable promise for various applications, but their computational requirements create a barrier that many institutions cannot overcome. A single session using a 70-billion-parameter model can cost around $127 in cloud computing fees, which puts these tools out of reach for organizations operating on limited budgets. We present AgentCompress, a framework that tackles this problem through task-aware dynamic compression. The idea comes from a simple observation: not all tasks require the same computational effort. Complex reasoning, for example, is far more demanding than text reformatting, yet conventional compression applies the same reduction to both. Our approach uses a lightweight neural controller that looks at the first few tokens of each request, estimates how complex the task will be, and sends it to an appropriately quantized version of the model. This routing step adds only about 12 milliseconds of overhead. We tested the framework on 290 multi-stage workflows from domains including computer science, physics, chemistry, and biology. The results show a 68.3% reduction in computational costs while preserving 96.2% of the original success rate. These findings suggest that routing queries intelligently can make powerful language models substantially more affordable without sacrificing output quality Task (project management) ; Workflow ; Reduction (mathematics) ; Session (web analytics) ; Quality (philosophy) ; Cloud computing ; Routing (electronic design automation) ; Controller (irrigation) ; Simple (philosophy) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Big Data and Digital Economy