jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexius Wadell | Anoushka Bhutani | Victor Azumah | Austin Ellis‐Mohr | Caroline Kelly | Hancheng Zhao | Anuj K. Nayak | Kareem Hegazy | Alexander Brace | Hongyi Lin | Murali Emani | Venkatram Vishwanath | Kevin L. Gering | M. Alkan | Tom Gibbs | J. C. Wells | Lav R. Varshney | Bharath Ramsundar | Karthik Duraisamy | Michael W. Mahoney | Arvind Ramanathan | Venkatasubramanian Viswanathan
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Foundation Models for Discovery and Exploration in Chemical Space Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches lack the scalability required to efficiently navigate chemical space. Scientific foundation models trained on large unlabeled datasets offer a path toward exploring chemical space across diverse application domains. Here we develop MIST, a family of molecular foundation models with up to an order of magnitude more parameters and data than prior works. Trained using a novel tokenization scheme that comprehensively captures nuclear, electronic, and geometric information, MIST learns from a diverse range of molecules. MIST models have been fine-tuned to predict more than 400 structure -- property relationships and match or exceed state-of-the-art performance across benchmarks spanning physiology, electrochemistry, and quantum chemistry. We demonstrate the ability of these models to solve real-world problems across chemical space, including multiobjective electrolyte solvent screening, olfactory perception mapping, isotope half-life prediction, stereochemical reasoning for chiral organometallic compounds, and binary and multi-component mixture property prediction. Probing MIST models using mechanistic interpretability methods reveals identifiable patterns and trends not explicitly present in the training data, suggesting that the models learn generalizable scientific concepts. We formulate hyperparameter-penalized Bayesian neural scaling laws and use them to reduce the computational cost of model development by an order of magnitude. The methods and findings presented here represent a significant step toward accelerating materials discovery, design, and optimization using foundation models and provide valuable guidance for training compute-optimal scientific foundation models. Interpretability ; Chemical space ; Property (philosophy) ; Scalability ; Foundation (evidence) ; Range (aeronautics) ; Computational model ; Multiscale modeling ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yi Yang | Zhimeng Liu | Wei Wu | Yanli Zhang | Hongbo Zhang | Jing Lin | Siyu Wu | Zihan Chen | Ye Sun | Ruiming Li | Nan Wang | Rui Wang | Zipeng Liu | Huobin Tan | Lei Shi | Yue Zhang | Hongyi Gao | Ge Wang
Source: Journal of Chemical Information and Modeling (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

MaterialBrain: High-Performance Material Synthesis Extraction via Human–AI-Curated Few-Shot Large Language Models The extraction of the metal-organic framework synthesis route from the literature has been crucial for the rational MOFs design with desirable functionality. The recent advent of large language models (LLMs) provides a disruptive new solution to this long-standing problem. While the latest research on chemical data extraction mostly adopts zero-shot LLMs lacking specialized material knowledge or fine-tuned LLMs inducing high cost and inflexibility in our scenario, we introduce in this work the MaterialBrain pipeline that optimizes the few-shot LLMs in-context learning technique to accurately extract synthesis routes and design high-performance materials. First, a batch-epoch-iteration-based human-AI data curation approach is proposed to optimize both the quantity and quality of annotation database for the synthesis extraction task, which are pivotal to MaterialBrain's performance. Second, an information retrieval algorithm is applied to pick and quantify a few-shot demonstrations from the annotation database for each extraction. Over three data sets randomly sampled from nearly 90,000 well-defined MOFs, we conduct triple evaluations to validate our pipeline. The synthesis extraction, structure inference, and material design performance of MaterialBrain significantly outplay zero-shot LLMs and baseline methods. The specific surface area of the lab-synthesized material guided by LLMs surpasses that of 99.2% of MOFs of the same class reported in the literature. Pipeline (software) ; Annotation ; Class (philosophy) ; Quality (philosophy) ; Information extraction ; Quality by Design Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Runze Wang | Min Yang | Yanming Shen
Source: ArXiv.org (preprint, 2025-03-05)
Field: Materials Science — Materials Chemistry

Bridging Molecular Graphs and Large Language Models While Large Language Models (LLMs) have shown exceptional generalization capabilities, their ability to process graph data, such as molecular structures, remains limited. To bridge this gap, this paper proposes Graph2Token, an efficient solution that aligns graph tokens to LLM tokens. The key idea is to represent a graph token with the LLM token vocabulary, without fine-tuning the LLM backbone. To achieve this goal, we first construct a molecule-text paired dataset from multisources, including CHEBI and HMDB, to train a graph structure encoder, which reduces the distance between graphs and texts representations in the feature space. Then, we propose a novel alignment strategy that associates a graph token with LLM tokens. To further unleash the potential of LLMs, we collect molecular IUPAC name identifiers, which are incorporated into the LLM prompts. By aligning molecular graphs as special tokens, we can activate LLM generalization ability to molecular few-shot learning. Extensive experiments on molecular classification and regression tasks demonstrate the effectiveness of our proposed Graph2Token. Security token ; Generalization ; Bridging (networking) ; Graph ; Construct (python library) ; Molecular graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Peng Liu | Shangde Gao | Yongqing Fu | Xiaoliang Wu | Stephen Tong | Ajitha Rajan | Hao Xu
Source:  (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Mat-Instructions: A Large-Scale Inorganic Material Instruction Dataset for Large Language Models Recent advancements in large language models (LLMs) have revolutionized research discovery across various scientific disciplines, including materials science. The discovery of novel materials, particularly crystal materials, is essential for achieving sustainable development goals (SDGs), as they drive breakthroughs in climate change mitigation, clean and affordable energy, and the promotion of industrial innovation. However, unlocking the full potential of LLMs in materials research remains challenging due to the lack of high-quality, diverse, and instruction-based datasets. Such datasets are crucial for guiding these models in understanding and predicting the structure, property, and function of materials across various tasks. To address this limitation, we introduce Mat-Instruction, a large-scale inorganic material instruction dataset, specifically designed to unlock the potential of LLMs in materials science. Extensive experiments on fine-tuning LLaMA with our Mat-Instruction dataset demonstrate its effectiveness in advancing progress for materials science. The code and dataset are available at https://github.com/zjuKeLiu/Mat-Instructions Function (biology) ; Promotion (chess) ; Code (set theory) ; Sustainable development ; Climate change ; Climate model Machine Learning in Materials Science
Authors: An Vuong | Minh-Hao Van | Prateek Verma | Chen Zhao | Xintao Wu
Source:  (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction Property (philosophy) ; Stability (learning theory) ; Work (physics) ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Generative Adversarial Networks and Image Synthesis
Authors: Nathaniel H. Park | Eduardo Soares | Victor Shirasuna | Tiffany J. Callahan | Sara Capponi | Emílio Vital Brazil
Source: ArXiv.org (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Understanding Structural Representation in Foundation Models for Polymers From the relative scarcity of training data to the lack of standardized benchmarks, the development of foundation models for polymers face significant and multi-faceted challenges. At the core, many of these issues are tied directly to the structural representation of polymers and here, we present a new foundation model using a SMILES-based polymer graph representation. This approach allows representation of critical polymer architectural features and connectivity that are not available in other SMILES-based representations. The developed polymer foundation model exhibited excellent performance on 28 different benchmark datasets. Critical evaluation of the developed representation against other variations in control experiments reveals this approach to be a highly performant method of representing polymers in language-based foundation models. These control experiments also reveal a strong invariance of all SMILES representations, with many variations achieving state-of-the-art or near state-of-the-art performance, including those which are chemically or semantically invalid. Examination of error sources and attention maps for the evaluated representations corroborate the findings of the control experiments, showing that chemistry language models based on SMILES interpolate over all sequence space for prediction tasks, not only those of semantically valid inputs. Overall, this work highlights the importance of control experiments as a check on human-imposed assumptions that can limit rational design of both chemistry foundation models and their underlying structural representations. Representation (politics) ; Foundation (evidence) ; Benchmark (surveying) ; Sequence (biology) ; Limit (mathematics) ; Control (management) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Tingting Wei | Chuin‐Shan Chen
Source: ArXiv.org (preprint, 2025-12-07)
Field: Materials Science — Materials Chemistry

Foundation Model for Polycrystalline Material Informatics We present a three-dimensional foundation model for polycrystalline materials based on a masked autoencoder trained via large-scale self-supervised learning. The model is pretrained on $100{,}000$ voxelized synthetic face-centered cubic (FCC) microstructures whose crystallographic textures systematically span the texture hull using hierarchical simplex sampling. The transferability of the learned latent representations is evaluated on two downstream tasks: homogenized elastic stiffness prediction and nonlinear stress-strain response prediction. For the nonlinear task, the pretrained encoder is coupled with an orientation-aware interaction-based deep material network (ODMN), where latent features are used to infer microstructure-dependent surrogate parameters. The inferred ODMNs are subsequently combined with crystal plasticity to predict stress--strain responses for previously unseen microstructures. In stiffness prediction, the pretrained model achieves validation $R^2$ values exceeding 0.8, compared to below 0.1 for non-pretrained baselines. In nonlinear response prediction, mean stress errors remain below 4\%. These results demonstrate that self-supervised pretraining yields physically meaningful and transferable microstructural representations, providing a scalable framework for microstructure-property inference in polycrystalline materials. Autoencoder ; Nonlinear system ; Artificial neural network ; Texture (cosmology) ; Inference ; Foundation (evidence) ; Stiffness ; Crystallite Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Estefanía Fernández | Patricia Pérez Bailac | Pablo G. Lustemberg | Ana B. Hungría | L. Pascual | Renato Cataluña | Alejandro Vidal‐Moya | Teresa Blasco | M. Verónica Ganduglia Pirovano | A. Martı́nez-Arias
Source: ChemRxiv (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

Fine-Tuning Catalysts: The Role of Support Nanomorphology in Shaping Cu/CeO2 CO-PROX Properties Catalysis ; Adsorption ; Selectivity ; Oxide ; Copper ; Heterogeneous catalysis ; Noble metal ; Redox ; Carbon monoxide Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Electrocatalysts for Energy Conversion
Authors: Xuan Lin | Long Chen | Yile Wang
Source: ArXiv.org (preprint, 2025-08-06)
Field: Materials Science — Materials Chemistry

AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models Large Language Models (LLMs) have shown promise in assisting molecular property prediction tasks but often rely on human-crafted prompts and chain-of-thought templates. While recent advanced large reasoning models like DeepSeek-R1 employ reinforcement learning for an extended ``thinking'' process, their reasoning can be verbose and lack relevance. We introduce AttriLens-Mol, an attribute-guided reinforcement learning framework for molecular property prediction with LLMs. AttriLens-Mol steers the model's reasoning by using: (1) a format reward encouraging attribute-based structured output, (2) a count reward to avoid enumerating irrelevant attributes, and (3) a rationality reward using advanced LLMs and RDKit to verify the relatedness of the generated attributes. This approach implicitly elicits the model's inherent knowledge of relevant molecular attributes during reasoning, enables making predictions for the molecular property more effectively. Experiments on both in-distribution and out-of-distribution datasets show that, training both 7B-size R1-Distilled-Qwen2.5 and R1-Distilled-LLaMA3.1 models on 4,000 samples with our proposed AttriLens-Mol method significantly boosts the performance, getting comparable or better results than supervised fine-tuning models (Mol-Instructions, ChemDFM, etc.) and advanced models (GPT-3.5, GPT-4o, DeepSeek-V3, DeepSeek-R1, etc.). Further, our extracted attributes for the target property, when used as features for an interpretable decision tree model, yield superior performance compared to attributes generated by prompting LLMs. This shows that AttriLens-Mol effectively elicits more relevant and predictive molecular attributes, leading to enhanced interpretability and performance for property prediction. We release the code in https://github.com/szu-tera/AttriLens-Mol. Interpretability ; Property (philosophy) ; Reinforcement learning ; Language model ; Code (set theory) ; Tree (set theory) ; Decision tree ; Word (group theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Dong-Xu Cui (313379) | Shi-Yu Long (21215855) | Yi-Xuan Tang (22120714) | Yue Zhao (186573) | Qiao Li (571252)
Source: Figshare (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Can Reasoning PowerSignificantly Improve the Knowledgeof Large Language Models for Chemistry?Based on Conversationswith LLMs This study presents a systematic evaluation of five reasoning-enhanced Large Language Models (LLMs)Deepseek-R1–0528, OpenAI-o4 mini, Gemini-2.5-pro, doubao-seed-1.6-thinking, and qwen-max-latestacross nine key chemistry tasks. By comparing these models with traditional LLMs and established computational tools, we systematically investigate the influence of reasoning capabilities and prompt engineering on chemical cognition. The results demonstrate that reasoning-enabled LLMs achieve significant performance improvements in fundamental tasks and that, in most cases, overly complex prompts are not beneficial for these models. However, domain-specific limitations persist; for instance, all five models exhibited structural inaccuracies in CIF file generation (such as incorrect bond topologies). Notably, while reasoning frameworks enhance logical coherence, they do not fundamentally resolve challenges in stereochemical identification or the recognition of rare symmetry groups. In essence, the spatial recognition capabilities of current Large Language Models remain insufficient. These findings underscore the necessity of developing domain-optimized training paradigms to bridge the gap between general reasoning capabilities and specialized chemical applications. Bridge (graph theory) ; Key (lock) ; Identification (biology) ; Automated reasoning ; Logical reasoning ; Computational model ; Deductive reasoning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sadikshya Gyawali | A.B. Mandal | Manish Dahal | Manish Awale | Sanjay Rijal | Shambhu Prasad Adhikari | Vaghawan Prasad Ojha
Source: ArXiv.org (preprint, 2025-03-27)
Field: Materials Science — Materials Chemistry

Integrating Large Language Models For Monte Carlo Simulation of Chemical Reaction Networks Chemical reaction network is an important method for modeling and exploring complex biological processes, bio-chemical interactions and the behavior of different dynamics in system biology. But, formulating such reaction kinetics takes considerable time. In this paper, we leverage the efficiency of modern large language models to automate the stochastic monte carlo simulation of chemical reaction networks and enable the simulation through the reaction description provided in the form of natural languages. We also integrate this process into widely used simulation tool Copasi to further give the edge and ease to the modelers and researchers. In this work, we show the efficacy and limitations of the modern large language models to parse and create reaction kinetics for modelling complex chemical reaction processes. Leverage (statistics) ; Monte Carlo method ; Chemical process ; Chemical reaction ; Chemical kinetics ; Stochastic simulation ; Simulation language ; Process (computing) ; Simulation modeling Machine Learning in Materials Science ; Business Process Modeling and Analysis ; Advanced Data Processing Techniques
Authors: Barbora Blahova Prudilova | Roman Gabriel | Eva Otyepková | Michal Otyepka
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Controlled Crystallization Enables Facile Fine-Tuning of Physical–Chemical Properties of Nicergoline Toward Easier Processability Background/Objectives: Crystallization is a key process in the manufacturing of active pharmaceutical ingredients (APIs), as it significantly affects the physical and chemical properties of the final product. Nicergoline, a clinically relevant ergot derivative, was chosen as a model compound to investigate how different crystallization strategies affect particle attributes. The objective of this study was to compare controlled and uncontrolled crystallization techniques and evaluate their impact on the physicochemical properties of nicergoline. Methods: Nicergoline was crystallized using controlled methods, including sonication-induced and seeding-induced crystallization, and uncontrolled methods, namely cubic and linear cooling, as well as acetone evaporation. The resulting powders were characterized by using a range of physicochemical techniques to assess particle morphology, size distribution, agglomeration behavior, and surface properties. Results: Uncontrolled crystallization methods produced particles prone to agglomeration, resulting in a broader particle size distribution ranging from 8 to 720 µm and heterogeneous surface characteristics. In contrast, controlled crystallization generated more uniform particles with reduced agglomeration and narrower particle size distributions. Among the evaluated methods, sonocrystallization provided the most effective control over particle size and morphology, demonstrated by a narrow size distribution ranging from 16 to 39 µm which correlated with improved flowability and surface energy. Conclusions: The study demonstrates that the choice of crystallization method significantly influences the structural and physicochemical properties of nicergoline. These findings highlight the importance of method selection for tailoring API properties to enhance downstream processing and product quality. Crystallization ; Economies of agglomeration ; Particle size ; Particle-size distribution ; Particle (ecology) ; Acetone Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Microencapsulation and Drying Processes
Authors: Mingxu Zhang | Dazhong Shen | Qi Zhang | Ying Sun
Source: arXiv (Cornell University) (preprint, 2025-12-22)
Field: Materials Science — Materials Chemistry

ChemATP: A Training-Free Chemical Reasoning Framework for Large Language Models Large Language Models (LLMs) exhibit strong general reasoning but struggle in molecular science due to the lack of explicit chemical priors in standard string representations. Current solutions face a fundamental dilemma. Training-based methods inject priors into parameters, but this static coupling hinders rapid knowledge updates and often compromises the model's general reasoning capabilities. Conversely, existing training-free methods avoid these issues but rely on surface-level prompting, failing to provide the fine-grained atom-level priors essential for precise chemical reasoning. To address this issue, we introduce ChemATP, a framework that decouples chemical knowledge from the reasoning engine. By constructing the first atom-level textual knowledge base, ChemATP enables frozen LLMs to explicitly retrieve and reason over this information dynamically. This architecture ensures interpretability and adaptability while preserving the LLM's intrinsic general intelligence. Experiments show that ChemATP significantly outperforms training-free baselines and rivals state-of-the-art training-based models, demonstrating that explicit prior injection is a competitive alternative to implicit parameter updates. Interpretability ; Prior probability ; Adaptability ; Face (sociological concept) ; String (physics) ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling