jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We introduce SCALAR (Structural Consistency And Logic Across Regimes), a benchmark for evaluating geometric scale generalization and its connection to structural hallucination, consistency, and reasoning in materials foundation models. Given canonical crystal representations, models must reason about derived nanoparticle structures obtained through supercell expansion and geometric truncation across length scales spanning a few atoms to over 18,000 atoms, totaling $\approx$100,000 structures from DFT-validated unit cells. SCALAR defines three tasks. (i) CIF to property prediction. (ii) A Chain-of-Thought variant with explicit physics-grounded reasoning. (iii) Inverse retrieval identifying crystals from candidates given target properties. Outputs are evaluated via structured metrics capturing numeric error, hallucination, cross-prompt consistency, monotonic reasoning, output validity, and retrieval regret. Experiments across diverse foundation models reveal large, model-dependent shifts under explicit reasoning, often reducing hallucination and error, but frequently destabilizing consistency or validity. These results demonstrate that geometric scale generalization cannot be inferred from accuracy alone. Supplementary materials are available at https://github.com/KurbanIntelligenceLab/SCALAR. Generalization ; Consistency (knowledge bases) ; Scalar (mathematics) ; Property (philosophy) ; Foundation (evidence) ; Benchmark (surveying) ; Scale (ratio) ; Monotonic function Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topological and Geometric Data Analysis
Authors: Roxane Elias Mallouhy | Haitham Alqahtani | Sheikha Lardhi
Source: IEEE Access (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Powered Standard Operating Procedure Generation and Optimization Using Large Language Models for Chemical Laboratory Applications This paper presents an AI-powered framework that automates the generation of Standard Operating Procedures (SOPs) for chemistry laboratories. The proposed solution integrates Generative AI, large language models (LLMs), and a Retrieval-Augmented Generation (RAG) architecture enhanced with a Chroma database and prompt engineering techniques. Together, these components enable the creation of accurate, customizable, and context-aware SOPs tailored to specific laboratory equipment, materials, and processes. SOPs play a vital role in ensuring safety, consistency, and regulatory compliance in chemical operations. Our system allows users to select and customize sections, such as safety guidelines, calibration steps, or procedural details, while automatically retrieving verified information from trusted sources, including equipment manuals and scientific literature. This approach minimizes hallucinations and ensures factual precision, which is crucial in chemistry workflows. By combining LLMs with semantic retrieval, the framework significantly reduces the time and effort required for SOP preparation while maintaining high standards of accuracy and safety. The results highlight how GenAI can transform laboratory documentation, offering a scalable solution for research and industrial chemical applications. Scalability ; Calibration ; Chemical laboratory ; Architecture ; Chemical plant ; Modeling language ; Laboratory safety Machine Learning in Materials Science ; Scientific Computing and Data Management ; Chemical Safety and Risk Management
Authors: Xun Zhang | Xuyi Zhang | Wenqi Du | Xiaojuan Liu | Wuping Liao
Source: ACS Omega (article, 2026-04-14)
Field: Chemistry — Inorganic Chemistry

Deep Insight into Fine-Tuning the Structure Affecting the Extraction Sequence of Metal Cations by Phosphorylcarboxylic Acids: A Quantum Chemistry Study Sequence (biology) ; Metal ; Quantum chemistry ; Extraction (chemistry) ; Quantum Radioactive element chemistry and processing ; Extraction and Separation Processes ; Electrochemical Analysis and Applications
Authors: Jiayun Pang | Ahmed M. Zaitoun | Xacobe Couso Cambeiro | Ivan Vulić
Source: Open MIND (preprint, 2026-02-11)
Field: Materials Science — Materials Chemistry

Modular Multi-Task Learning for Chemical Reaction Prediction Adapting large language models (LLMs) trained on broad organic chemistry to smaller, domain-specific reaction datasets is a key challenge in chemical and pharmaceutical R&amp;D. Effective specialisation requires learning new reaction knowledge while preserving general chemical understanding across related tasks. Here, we evaluate Low-Rank Adaptation (LoRA) as a parameter-efficient alternative to full fine-tuning for organic reaction prediction on limited, complex datasets. Using USPTO reaction classes and challenging C-H functionalisation reactions, we benchmark forward reaction prediction, retrosynthesis and reagent prediction. LoRA achieves accuracy comparable to full fine-tuning while effectively mitigating catastrophic forgetting and better preserving multi-task performance. Both fine-tuning approaches generalise beyond training distributions, producing plausible alternative solvent predictions. Notably, C-H functionalisation fine-tuning reveals that LoRA and full fine-tuning encode subtly different reactivity patterns, suggesting more effective reaction-specific adaptation with LoRA. As LLMs continue to scale, our results highlight the practicality of modular, parameter-efficient fine-tuning strategies for their flexible deployment for chemistry applications. Retrosynthetic analysis ; Modular design ; Adaptation (eye) ; Forgetting ; Key (lock) ; Benchmark (surveying) ; Software deployment ; Reagent ; Reactivity (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Kwanghee Lee | Chanhyun Jung | Jaeuk Sim | Young Hee Lee | Ji-Youn Yim | Jiwon Park | Narges sadat Hosseininasab | Subramaniyan Prasanna Kumari | Mayavan Viji | Jae-Hwan Kwak | Soonsil Hyun | Jae-Kyung Jung
Source: The Journal of Organic Chemistry (article, 2026-02-12)
Field: Chemistry — Organic Chemistry

Fine-Tuned Imino-Diels–Alder Reaction for the Sustainable Synthesis of Nicotinonitriles via Push–Pull Vinylogous Enamino Nitriles Nicotinonitriles are valuable bioisosteres of nicotinic acid demonstrating diverse biological activities, but mild and versatile synthetic methods remain elusive. Imino-Diels-Alder reaction has been used as a pivotal tool in preparing nitrogen-containing heterocycles, such as pyridines. However, existing strategies for the imino-Diels-Alder reaction have suffered from challenges such as installing a nitrile at the C3-position of pyridines and preventing undesired autoxidation leading to 3-hydroxypyridines. Herein, we demonstrate an acid- and metal-free imino-Diels-Alder reaction between vinylogous enaminonitriles and iminium salts, providing access to 2,4-disubstituted nicotinonitriles. Aromatization proceeds smoothly under air/TEMPO conditions, which effectively suppress undesired autoxidation. The operationally simple one-pot protocol employs reusable ethanol as solvent, equimolar reagents and a substoichiometric TEMPO, minimizing waste. Vitamin B<sub>3</sub> and 3-aminopyridine derivatives can also be accessed through simple modifications of nicotinonitriles. This strategy features characteristics of sustainable chemistry and, moreover, highlights the potential of vinylogous enaminonitriles as activated dienes in hetero-Diels-Alder reaction while circumventing the limitations of existing synthetic approaches. Aromatization ; Iminium ; Nitrile ; Reagent ; Autoxidation ; Reaction conditions Catalytic C–H Functionalization Methods ; Synthesis and Characterization of Pyrroles ; Synthesis of heterocyclic compounds
Authors: Jingkang Liang | Niklas Groll | Gürkan Sin
Source: Open MIND (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

Large Language Model Agent for User-friendly Chemical Process Simulations Modern process simulators enable detailed process design, simulation, and optimization; however, constructing and interpreting simulations is time-consuming and requires expert knowledge. This limits early exploration by inexperienced users. To address this, a large language model (LLM) agent is integrated with AVEVA Process Simulation (APS) via Model Context Protocol (MCP), allowing natural language interaction with rigorous process simulations. An MCP server toolset enables the LLM to communicate programmatically with APS using Python, allowing it to execute complex simulation tasks from plain-language instructions. Two water-methanol separation case studies assess the framework across different task complexities and interaction modes. The first shows the agent autonomously analyzing flowsheets, finding improvement opportunities, and iteratively optimizing, extracting data, and presenting results clearly. The framework benefits both educational purposes, by translating technical concepts and demonstrating workflows, and experienced practitioners by automating data extraction, speeding routine tasks, and supporting brainstorming. The second case study assesses autonomous flowsheet synthesis through both a step-by-step dialogue and a single prompt, demonstrating its potential for novices and experts alike. The step-by-step mode gives reliable, guided construction suitable for educational contexts; the single-prompt mode constructs fast baseline flowsheets for later refinement. While current limitations such as oversimplification, calculation errors, and technical hiccups mean expert oversight is still needed, the framework's capabilities in analysis, optimization, and guided construction suggest LLM-based agents can become valuable collaborators. Process (computing) ; Context (archaeology) ; Task (project management) ; Natural language ; Protocol (science) ; Interaction protocol ; Multi-agent system ; Natural language understanding Machine Learning in Materials Science ; Process Optimization and Integration ; Multi-Agent Systems and Negotiation
Authors: Jingkang Liang | Niklas Groll | Gürkan Sin
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Large Language Model Agent for User-friendly Chemical Process Simulations Modern process simulators enable detailed process design, simulation, and optimization; however, constructing and interpreting simulations is time-consuming and requires expert knowledge. This limits early exploration by inexperienced users. To address this, a large language model (LLM) agent is integrated with AVEVA Process Simulation (APS) via Model Context Protocol (MCP), allowing natural language interaction with rigorous process simulations. An MCP server toolset enables the LLM to communicate programmatically with APS using Python, allowing it to execute complex simulation tasks from plain-language instructions. Two water-methanol separation case studies assess the framework across different task complexities and interaction modes. The first shows the agent autonomously analyzing flowsheets, finding improvement opportunities, and iteratively optimizing, extracting data, and presenting results clearly. The framework benefits both educational purposes, by translating technical concepts and demonstrating workflows, and experienced practitioners by automating data extraction, speeding routine tasks, and supporting brainstorming. The second case study assesses autonomous flowsheet synthesis through both a step-by-step dialogue and a single prompt, demonstrating its potential for novices and experts alike. The step-by-step mode gives reliable, guided construction suitable for educational contexts; the single-prompt mode constructs fast baseline flowsheets for later refinement. While current limitations such as oversimplification, calculation errors, and technical hiccups mean expert oversight is still needed, the framework's capabilities in analysis, optimization, and guided construction suggest LLM-based agents can become valuable collaborators. Process (computing) ; Context (archaeology) ; Task (project management) ; Natural language ; Protocol (science) ; Interaction protocol ; Multi-agent system ; Natural language understanding Machine Learning in Materials Science ; Process Optimization and Integration ; Multi-Agent Systems and Negotiation
Authors: Jiawen Zou | Zirui Lv | Weimin Tan | Taoyang Wang | Runfeng Lin | Zhongyao Wang | Yi Yang | Qiaowei Li | Xiaomin Li | Bo Yan | Dongyuan Zhao
Source: Nature Communications (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

A data-efficient foundation model for porous materials based on expert-guided supervised learning Foundation models require massive amounts of training data, which are often costly to obtain in materials science. Meanwhile, long-established physical knowledge such as molecular force fields and geometric analysis provides direct guidance for material behavior, but remains insufficiently leveraged. Here we demonstrate that expert knowledge can directly supervise pre-training and substantially reduce data requirements. A set of potential energy surface (PES) basis functions, which encode guest-host interaction energetics, is developed as unified descriptors for different guest molecules. A multi-modal architecture is designed to fuse information from both material structure and PES. Pre-training is achieved by learning comprehensive geometric features spanning different spatial scales. Consequently, a foundation model for porous materials is developed under limited data regimes, named SpbNet. SpbNet is evaluated on over 50 downstream tasks, including adsorption, separation, and intrinsic properties, etc. SpbNet consistently outperforms models pre-trained on datasets nearly 20 times larger, reducing the relative errors by over 20%. In addition, SpbNet demonstrates strong generalization capabilities across both in-distribution and out-of-distribution materials, such as Metal Organic Frameworks, Covalent Organic Frameworks, and zeolites. Fuse (electrical) ; Generalization ; Foundation (evidence) ; Set (abstract data type) ; ENCODE ; Supervised learning ; Feature (linguistics) ; Deep learning ; Basis (linear algebra) Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Ali Riza Durmaz | James D. Lamb | McLean P. Echlin | Tresa M. Pollock
Source: ChemRxiv (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

FOUNDATION MODELS FOR MULTIMODAL IMAGE DATA FUSION IN MATERIALS SCIENCE This work comprehensively assesses the capabilities of recently emerging foundation models for multimodal image matching and registration in the materials science and engineering (MSE) domain. A distinctive feature in correlative materials microscopy is that the images, which need to be spatially associated, commonly not only have limited mutual information but also distinct length scales. To date, it is largely unknown how foundational multimodal matchers, such as MatchAnything RoMa or ELoFTR, which were trained predominantly with macroscale images from the human environment, generalize to image pairs in such an out-of-domain setting-especially given the pronounced information and field of view disparities in image pairs. To evaluate these models, we use the recently published AmalgaMatch dataset which covers 187 image pairs partitioned into six groups which represent distinct image matching tasks commonly faced in correlative materials microscopy and 19 subsets representing different materials. This dataset with its diverse microscopy modalities and broad range of depicted metals, alloys, and ceramics facilitates evaluation of these models in a rather representative manner. We observe that MatchAnything RoMa, as opposed to the ELoFTR variant, attains satisfactory matching results for many matching tasks and materials represented in this dataset. In contrast, image pairs within the dislocation characterization, slip partitioning and multi-scale groups pose a difficult challenge for these models. These pairs often exhibit limited mutual information and strong field of view mismatch, with field of view ratios reaching down to 2%. We propose inference strategies and workflows which embed a foundation model in an image processing pipeline to increase the matching quality and robustness and ultimately overcome aforementioned challenges inherent to MSE matching tasks. Robustness (evolution) ; Matching (statistics) ; Correlative ; Image fusion ; Inference ; Image processing ; Field (mathematics) ; Image registration ; Workflow Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced Neural Network Applications
Authors: Michiko Yoshitake | Yuta Suzuki | Ryo Igarashi | Yoshitaka Ushiku | Keisuke Nagato
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models We present MaterialFigBench, a benchmark dataset designed to evaluate the ability of multimodal large language models (LLMs) to solve university-level materials science problems that require accurate interpretation of figures. Unlike existing benchmarks that primarily rely on textual representations, MaterialFigBench focuses on problems in which figures such as phase diagrams, stress-strain curves, Arrhenius plots, diffraction patterns, and microstructural schematics are indispensable for deriving correct answers. The dataset consists of 137 free-response problems adapted from standard materials science textbooks, covering a broad range of topics including crystal structures, mechanical properties, diffusion, phase diagrams, phase transformations, and electronic properties of materials. To address unavoidable ambiguity in reading numerical values from images, expert-defined answer ranges are provided where appropriate. We evaluate several state-of-the-art multimodal LLMs, including ChatGPT and GPT models accessed via OpenAI APIs, and analyze their performance across problem categories and model versions. The results reveal that, although overall accuracy improves with model updates, current LLMs still struggle with genuine visual understanding and quantitative interpretation of materials science figures. In many cases, correct answers are obtained by relying on memorized domain knowledge rather than by reading the provided images. MaterialFigBench highlights persistent weaknesses in visual reasoning, numerical precision, and significant-digit handling, while also identifying problem types where performance has improved. This benchmark provides a systematic and domain-specific foundation for advancing multimodal reasoning capabilities in materials science and for guiding the development of future LLMs with stronger figure-based understanding. Benchmark (surveying) ; Ambiguity ; Interpretation (philosophy) ; Reading (process) ; Strengths and weaknesses ; Range (aeronautics) ; Schematic Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Multimodal Machine Learning Applications
Authors: Chengqian Zhang | Duo Zhang | Anyang Peng | Mingyu Guo | Yuzhi Zhang | Lei Wang | Guolin Ke | Linfeng Zhang | Tiejun Li | Han Wang
Source: arXiv (Cornell University) (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-distribution accuracy after fine-tuning, their reliability under out-of-distribution (OOD) conditions remains unclear. We identify a critical failure mode in downstream adaptation: standard fine-tuning induces representation collapse, erasing pretrained chemical and structural priors and severely degrading OOD performance. To address this limitation, we propose multi-task fine-tuning (MFT), which jointly optimizes downstream property prediction with a physically grounded force-field objective inherited from pretraining. This approach preserves essential chemical priors while enabling task-specific adaptation. Across molecular and materials benchmarks, MFT consistently improves OOD generalization, approaching the theoretical limit set by in-distribution accuracy, while outperforming standard fine-tuning, training from scratch, and state-of-the-art task-specific models. These results establish safe adaptation as a central requirement for large atomistic models and position MFT as a practical and data-efficient pathway toward robust molecular and materials discovery. Generalization ; Prior probability ; Reliability (semiconductor) ; Representation (politics) ; Property (philosophy) ; Set (abstract data type) ; Limit (mathematics) ; Position (finance) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: Jonas Hänseroth | Aaron Flötotto | Muhammad Nawaz Qaisrani | Christian Dreßler
Source: Figshare (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Fine-TuningUnifies Foundational Machine-Learned InteratomicPotential Architectures at ab initio Accuracy This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-<i>ab initio</i> accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, and ORB) across seven chemically diverse compounds reveals that fine-tuning universally enhances force predictions by factors of 5-15 and improves energy accuracy by 2-4 orders of magnitude. The investigated models span both equivariant and invariant, as well as conservative and non-conservative, architectures. While general-purpose foundation models are robust, they exhibit architecture-dependent deviations from <i>ab initio</i> reference data; specialized system-specific training (fine-tuning) eliminates these discrepancies, enabling quantitatively accurate predictions of atomistic and structural properties. Using datasets constructed from 2000 equidistantly sampled frames of short <i>ab initio</i> molecular dynamics trajectories, fine-tuning reduces force errors by an order of magnitude and harmonizes performance across all architectures. These findings establish fine-tuning as a universal route to achieving system-specific predictive accuracy while preserving the computational efficiency of MLIPs. To promote widespread adoption, we introduce the <i>aMACEing Toolkit</i>, which provides a unified and reproducible interface for fine-tuning workflows across multiple MLIP frameworks. Benchmarking ; Work (physics) ; Interatomic potential ; Workflow ; Ab initio ; Molecular dynamics ; Energy (signal processing) ; Interface (matter) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions