jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sakshi Ranjan | Dheeraj Mishra | Sanjay Kumar Singh
Source: IEEE Transactions on Computational Biology and Bioinformatics (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Mitigating Catastrophic Forgetting in Molecular Property Prediction via Refresh Learning and Pareto Optimization Continual Learning (CL) enables Large Language Models (LLMs) to adapt to new episodes and data streams without forgetting previously acquired knowledge, a critical requirement for dynamic fields like Molecular property Prediction (MP). LLMs, however, often face Catastrophic Forgetting (CF), where learning new information erodes prior knowledge, particularly when data distributions shift significantly between episodes, as seen in chemical, genomic, and proteomic datasets. To address CF, the existing replay-based techniques use memory buffers to store past episode data but often overlook the relationships between episodes, resulting in sub-optimal performance when revisiting earlier episodes. To this, the paper proposes a Multi-task Learning (MTL) framework that reconciles existing CL techniques into a unified hierarchical gradient aggregation framework. It builds a novel framework using the ChemBERTa model, namely MTL-PORL (Multi-task Learner-Pareto Optimized Refresh Learning), i.e., Refresh Learning (RL), inspired by neuroscience, where the brain discards outdated information to enhance retention and facilitate new learning with Pareto Optimization (PO) for MP. The hyper-gradient approach in the MTL-PORL leverages unlearning current data before relearning it, acting as a flexible plug-in that enhances existing CL methods. The MTL-PORL exhibits Anytime Average Accuracy (91.63%, 94.89%, and 92.67%), Test Accuracy (92.48%, 96.48%, and 96.86%), and Forgetting Measure (-0.0048, -0.0045, and -0.0063) on the BBBP, bitter, and sweet datasets, respectively. The comprehensive empirical analysis highlighted significant improvements in sequential learning compared to existing methods, addressing the stability-plasticity trade-off and effectiveness of RL. Forgetting ; Property (philosophy) ; Pareto principle ; Computer science ; Mathematical optimization ; Psychology ; Mathematics ; Cognitive psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Trinanjan Dey (18611318) | Dundappa Mumbaraddi (3968996) | Razieh Firouzi-Haji (13931924) | Dmitry Vrublevskiy (9914044) | Zhongqi Lin (20328502) | Alkiviathes Meldrum (6021191) | Arthur Mar (1637146)
Source: Figshare (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Rare-Earth Substitutionof La<sub>3</sub>Si<sub>2</sub>S<sub>8</sub>I for Efficient and Color-TunablePhosphor ConvertedWhite Light Emitting Diodes Mixed-anion compounds enable properties to be controlled to a greater degree by chemical substitution than single-anion compounds. La<sub>3</sub>Si<sub>2</sub>S<sub>8</sub>I was investigated as a host to develop phosphors based on solid solutions (La<sub>1–<i>x</i></sub><i>RE</i><sub><i>x</i></sub>)<sub>3</sub>Si<sub>2</sub>S<sub>8</sub>I (<i>RE</i> = Ce, Pr, Tb, Dy, Ho; <i>x</i> = 0–0.33) and (La<sub>1–<i>x</i>–<i>y</i></sub>Ce<sub><i>x</i></sub><i>RE</i><sub><i>y</i></sub>)<sub>3</sub>Si<sub>2</sub>S<sub>8</sub>I (<i>RE</i> = Tb, Dy; <i>x</i>, <i>y</i> = 0–0.33) that exhibit wide color tunability and white emission. Upon excitation with a 364 nm UV laser source, the phosphors emit through a down-conversion process over a wide visible color gamut. Depending on the <i>RE</i><sup>3+</sup> concentration, the spectra featured broad emission (cyan for Ce<sup>3+</sup>) or sharp 4f–4f emission peaks (red for Ho<sup>3+</sup>; green for Tb<sup>3+</sup>; cyan-green and red for Pr<sup>3+</sup>; yellow and cyan for Dy<sup>3+</sup>) which can be fine-tuned to a wider gamut. The cosubstituted (La<sub>1–<i>x</i>–<i>y</i></sub>Ce<sub><i>x</i></sub><i>RE</i><sub><i>y</i></sub>)<sub>3</sub>Si<sub>2</sub>S<sub>8</sub>I phosphors showed even more intense photoluminescence and wider color palettes within the cyan to green regions (Ce<sup>3+</sup>–Tb<sup>3+</sup>) and cool to warm white (Ce<sup>3+</sup>–Dy<sup>3+</sup>). The photometric parameters were appropriate for backlighting display and near UV-pumped phosphor-converted white light-emitting diodes (pc-wLEDs). Photoluminescence decay profiles follow biexponential behavior with lifetimes ranging from a few ns (for Ce<sup>3+</sup>) to 3–450 μs (for other <i>RE</i><sup>3+</sup>). Most of the optimized phosphors were moderately stable at 80–100 °C before the onset of thermal quenching effects. They gave absolute quantum yields up to 50%, which are favorable for efficient energy conversion. Prototype pc-wLEDs fabricated from a blend of three phosphors or from single-phase phosphors showed promising performance. Phosphor ; Cyan ; Photoluminescence ; Backlight ; Diode ; Light-emitting diode ; Quantum efficiency ; Quenching (fluorescence) Luminescence Properties of Advanced Materials ; Microwave Dielectric Ceramics Synthesis ; Advanced Photocatalysis Techniques
Authors: Shunya Minami | Tatsuya Ishigaki | Ikko Hamamura | Taku Mikuriya | Youmi Ma | Naoaki Okazaki | Hiroya Takamura | Yohichi Suzuki | Tadashi Kadowaki
Source: ArXiv.org (preprint, 2025-10-30)
Field: Materials Science — Materials Chemistry

QuantumBench: A Benchmark for Quantum Problem Solving Large language models are now integrated into many scientific workflows, accelerating data analysis, hypothesis generation, and design space exploration. In parallel with this growth, there is a growing need to carefully evaluate whether models accurately capture domain-specific knowledge and notation, since general-purpose benchmarks rarely reflect these requirements. This gap is especially clear in quantum science, which features non-intuitive phenomena and requires advanced mathematics. In this study, we introduce QuantumBench, a benchmark for the quantum domain that systematically examine how well LLMs understand and can be applied to this non-intuitive field. Using publicly available materials, we compiled approximately 800 questions with their answers spanning nine areas related to quantum science and organized them into an eight-option multiple-choice dataset. With this benchmark, we evaluate several existing LLMs and analyze their performance in the quantum domain, including sensitivity to changes in question format. QuantumBench is the first LLM evaluation dataset built for the quantum domain, and it is intended to guide the effective use of LLMs in quantum research. Benchmark (surveying) ; Quantum ; Domain (mathematical analysis) ; Space (punctuation) ; Sensitivity (control systems) Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Quantum Computing Algorithms and Architecture
Authors: Phi Anh Phan | Dhruv Agarwal | Kavitha Srinivas | Horst Samulowitz | Pavan Kapanipathi | Andrew McCallum
Source: ArXiv.org (preprint, 2025-08-12)
Field: Materials Science — Materials Chemistry

MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time Large language models (LLMs) are increasingly being applied to black-box optimization tasks, from program synthesis to molecule design. Prior work typically leverages in-context learning to iteratively guide the model towards better solutions. Such methods, however, often struggle to balance exploration of new solution spaces with exploitation of high-reward ones. Recently, test-time training (TTT) with synthetic data has shown promise in improving solution quality. However, the need for hand-crafted training data tailored to each task limits feasibility and scalability across domains. To address this problem, we introduce MiGrATe-a method for online TTT that uses GRPO as a search algorithm to adapt LLMs at inference without requiring external training data. MiGrATe operates via a mixed-policy group construction procedure that combines on-policy sampling with two off-policy data selection techniques: greedy sampling, which selects top-performing past completions, and neighborhood sampling (NS), which generates completions structurally similar to high-reward ones. Together, these components bias the policy gradient towards exploitation of promising regions in solution space, while preserving exploration through on-policy sampling. We evaluate MiGrATe on three challenging domains-word search, molecule optimization, and hypothesis+program induction on the Abstraction and Reasoning Corpus (ARC)-and find that it consistently outperforms both inference-only and TTT baselines, demonstrating the potential of online TTT as a solution for complex search tasks without external supervision. Task (project management) ; Abstraction ; Inference ; Scalability ; Adaptation (eye) ; Sampling (signal processing) ; Selection (genetic algorithm) ; Training set ; Synthetic data Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning and Algorithms
Authors: Vipul Gupta | Jonathan Paul | Ingo Schmitt | Florian Pyczak
Source: Advanced Intelligent Systems (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

SciLitMiner: An Intelligent System for Scientific Literature Mining and Knowledge Discovery Recent advances in data mining have enabled automation in literature‐based discovery (LBD), allowing synergistic evaluation of experimental findings reported in scientific publications. However, existing tools and digital libraries fall short in generating relevant literature collections and evaluating them for highly specific questions. This article presents SciLitMiner, an intelligent system to address this gap. SciLitMiner enables federated ingestion of literature from digital libraries; applies advanced retrieval techniques, including dataset‐aware retrieval from visual elements, to identify relevant studies; and leverages retrieval‐augmented generation (RAG) tailored to domain‐specific knowledge reasoning. The system is applied in materials science to study the creep behavior of γ‐TiAl alloys, revealing the intricate interplay between material, process, microstructure, and creep rate, represented through knowledge graphs. Two domain experts rate responses from the knowledge reasoning workflow with OpenAI large language models (LLMs) as the backbone above “good” (&gt;3 on a 5‐point Likert scale) in over 90% across qualitative criteria, indicating strong performance. In a case study, the workflow also outperforms gpt‐4.5‐turbo with web search and other leading tools in reliability. A second case study benchmarks open‐source LLMs as drop‐in replacements for proprietary models, demonstrating comparable‐to‐superior performance. The system's flexibility enables its use in automated LBD across diverse research domains. Workflow ; Domain (mathematical analysis) ; Knowledge extraction ; Scientific discovery ; Scientific literature ; Flexibility (engineering) ; Automation ; Domain knowledge Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Biomedical Text Mining and Ontologies
Authors: Y. Suzuki | Farnoush Banaei‐Kashani
Source: ArXiv.org (preprint, 2025-11-25)
Field: Materials Science — Materials Chemistry

Universe of Thoughts: Enabling Creative Reasoning with Large Language Models Reasoning based on Large Language Models (LLMs) has garnered increasing attention due to outstanding performance of these models in mathematical and complex logical tasks. Beginning with the Chain-of-Thought (CoT) prompting technique, numerous reasoning methods have emerged that decompose problems into smaller, sequential steps (or thoughts). However, existing reasoning models focus on conventional problem-solving and do not necessarily generate creative solutions by ``creative reasoning''. In domains where the solution space is expansive and conventional solutions are suboptimal, such as drug discovery or business strategization, creative reasoning to discover innovative solutions is crucial. To address this gap, first we introduce a computational framework for creative reasoning inspired by established cognitive science principles. With this framework, we propose three core creative reasoning paradigms, namely, \textit{combinational}, \textit{exploratory}, and \textit{transformative} reasoning, where each offers specific directions for systematic exploration of the universe of thoughts to generate creative solutions. Next, to materialize this framework using LLMs, we introduce the \textit{Universe of Thoughts} (or \textit{UoT}, for short), a novel set of methods to implement the aforementioned three creative processes. Finally, we introduce three novel tasks that necessitate creative problem-solving, along with an evaluation benchmark to assess creativity from three orthogonal perspectives: feasibility as constraint, and utility and novelty as metrics. With a comparative analysis against the state-of-the-art (SOTA) reasoning techniques as well as representative commercial models with reasoning capability, we show that UoT demonstrates superior performance in creative reasoning. Automated reasoning ; Analytic reasoning ; Novelty ; Computational creativity ; Qualitative reasoning ; Set (abstract data type) ; Reasoning system ; Expansive ; Deductive reasoning ; Creativity Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Creativity in Education and Neuroscience
Authors: Yukari Katsura | Tomoya Mato | Yu Takada | Eiji Koyama | Dewi Yana | Atsumi Tanaka | Masaya Kumagai
Source: Science and Technology of Advanced Materials Methods (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Development of LLM-assisted data curation tools for the Starrydata materials science database We developed Large Language Model (LLM)-assisted tools to accelerate curator-driven data collection from materials science publications for the Starrydata database project. We implemented two complementary tools with distinct design philosophies. The first tool is the Starrydata Auto-Suggestion for Sample Information, which generates concise English descriptions conforming to our existing database schema based on user-provided text from abstracts and experimental methods. The second tool is a schema-free dual-component system comprising the Starrydata Auto-Summary GPT and Starrydata Auto-Summary Viewer. The Auto-Suggestion tool is integrated into the Starrydata2 web platform and operates efficiently with lighter language models. The Auto-Summary GPT, demonstrated using GPT-5, processes PDF files of open-access papers and generates comprehensive JSON output capturing and summarizing all figures, tables, and experimental samples as they appear in the original papers. The companion viewer transforms this JSON data into interactive tables, enabling curators to understand complete paper structure, identify relevant data collection targets, and efficiently input figure and sample information while referencing the organized data. These tools enhance curation efficiency and represent a step toward automated scientific database construction. We developed two tools using LLMs to accelerate literature data collection in Starrydata2, including the target paper selection and the description of the figures, the samples and experimental process. JSON ; Data curation ; Schema (genetic algorithms) ; Database schema ; Sample (material) ; Data collection ; Database design ; Data modeling ; Data exploration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Liam Pond | Ichiro Fujinaga
Source: ArXiv.org (preprint, 2025-03-28)
Field: Materials Science — Materials Chemistry

Teaching LLMs Music Theory with In-Context Learning and Chain-of-Thought Prompting: Pedagogical Strategies for Machines This study evaluates the baseline capabilities of Large Language Models (LLMs) like ChatGPT, Claude, and Gemini to learn concepts in music theory through in-context learning and chain-of-thought prompting. Using carefully designed prompts (in-context learning) and step-by-step worked examples (chain-of-thought prompting), we explore how LLMs can be taught increasingly complex material and how pedagogical strategies for human learners translate to educating machines. Performance is evaluated using questions from an official Canadian Royal Conservatory of Music (RCM) Level 6 examination, which covers a comprehensive range of topics, including interval and chord identification, key detection, cadence classification, and metrical analysis. Additionally, we evaluate the suitability of various music encoding formats for these tasks (ABC, Humdrum, MEI, MusicXML). All experiments were run both with and without contextual prompts. Results indicate that without context, ChatGPT with MEI performs the best at 52%, while with context, Claude with MEI performs the best at 75%. Future work will further refine prompts and expand to cover more advanced music theory concepts. This research contributes to the broader understanding of teaching LLMs and has applications for educators, students, and developers of AI music tools alike. Chord (peer-to-peer) ; Key (lock) ; Music theory ; Music education ; Baseline (sea) ; Work (physics) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Explainable Artificial Intelligence (XAI)
Authors: Katsura, Yukari | Mato, Tomoya | Takada, Yu | Koyama, Eiji | Yana, Dewi | Tanaka, Atsumi | Kumagai, Masaya
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Development of LLM-assisted data curation tools for the Starrydata materials science database We developed Large Language Model (LLM)-assisted tools to accelerate curator-driven data collection from materials science publications for the Starrydata database project. We implemented two complementary tools with distinct design philosophies. The first tool is the Starrydata Auto-Suggestion for Sample Information, which generates concise English descriptions conforming to our existing database schema based on user-provided text from abstracts and experimental methods. The second tool is a schema-free dual-component system comprising the Starrydata Auto-Summary GPT and Starrydata Auto-Summary Viewer. The Auto-Suggestion tool is integrated into the Starrydata2 web platform and operates efficiently with lighter language models. The Auto-Summary GPT, demonstrated using GPT-5, processes PDF files of open-access papers and generates comprehensive JSON output capturing and summarizing all figures, tables, and experimental samples as they appear in the original papers. The companion viewer transforms this JSON data into interactive tables, enabling curators to understand complete paper structure, identify relevant data collection targets, and efficiently input figure and sample information while referencing the organized data. These tools enhance curation efficiency and represent a step toward automated scientific database construction. We developed two tools using LLMs to accelerate literature data collection in Starrydata2, including the target paper selection and the description of the figures, the samples and experimental process. JSON ; Data curation ; Schema (genetic algorithms) ; Data collection ; Database schema ; Sample (material) ; Database design ; Data modeling ; Data management Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Katsura, Yukari | Mato, Tomoya | Takada, Yu | Koyama, Eiji | Yana, Dewi | Tanaka, Atsumi | Kumagai, Masaya
Source: Figshare (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Development of LLM-assisted data curation tools for the Starrydata materials science database We developed Large Language Model (LLM)-assisted tools to accelerate curator-driven data collection from materials science publications for the Starrydata database project. We implemented two complementary tools with distinct design philosophies. The first tool is the Starrydata Auto-Suggestion for Sample Information, which generates concise English descriptions conforming to our existing database schema based on user-provided text from abstracts and experimental methods. The second tool is a schema-free dual-component system comprising the Starrydata Auto-Summary GPT and Starrydata Auto-Summary Viewer. The Auto-Suggestion tool is integrated into the Starrydata2 web platform and operates efficiently with lighter language models. The Auto-Summary GPT, demonstrated using GPT-5, processes PDF files of open-access papers and generates comprehensive JSON output capturing and summarizing all figures, tables, and experimental samples as they appear in the original papers. The companion viewer transforms this JSON data into interactive tables, enabling curators to understand complete paper structure, identify relevant data collection targets, and efficiently input figure and sample information while referencing the organized data. These tools enhance curation efficiency and represent a step toward automated scientific database construction. We developed two tools using LLMs to accelerate literature data collection in Starrydata2, including the target paper selection and the description of the figures, the samples and experimental process. JSON ; Data curation ; Schema (genetic algorithms) ; Data collection ; Database schema ; Sample (material) ; Database design ; Data modeling ; Data management Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Fatih KARAÇAM | Yunus ALTINOK
Source: INTERNATIONAL SCIENTIFIC CONFERENCE - UNITECH (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A COMPREHENSIVE OVERVIEW OF THE MECHANICS AND APPLICATIONS OF DOUBLE-BEAM SYSTEMS ABSTRACT Double-beam systems have attracted significant attention due to their unique mechanical behavior and wide range of engineering applications. This study offers an extensive review of the mechanics and applications of doublebeam systems, addressing both theoretical progress and practical uses. Initially, the theoretical foundations, including Euler-Bernoulli and Timoshenko beam theories, higher-order shear deformation models, and advanced non-local elasticity and strain gradient theories for micro- and nano-scale applications are reviewed. In addition, the role of elastic and viscoelastic interlayers-modeled by Kelvin-Voigt, Maxwell, Burgers, and Zener approaches is discussed, as well as the influence of different foundation models such as Winkler, Pasternak, and Kerr. The literature survey traces the historical evolution of double-beam research, from early vibration and stability studies in the 1970s to recent advances involving functionally graded materials, cracked beam analyses, and moving load problems. Future research directions include the development of smart material-based double-beam systems, broader adoption of computational methods, and coupling theoretical models with experimental approaches. These perspectives underline the importance of double-beam systems not only in structural mechanics but also in advanced engineering applications. Viscoelasticity ; Timoshenko beam theory ; Elasticity (physics) ; Structural mechanics ; Continuum mechanics ; Computational mechanics ; Applied mechanics ; Foundation (evidence) Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering