jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kan Hatakeyama‐Sato | Toshihiko Nishida | Koji Kitamura | Yoshitaka Ushiku | Koichi TAKAHASHI | Yuta Nabae | Teruaki Hayakawa
Source: Science and Technology of Advanced Materials Methods (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Perspective on utilizing foundation models for laboratory automation in materials research This review explores the potential of foundation models to advance laboratory automation in the materials and chemical sciences. We highlight their dual roles within experimental systems, spanning high-level cognitive activities – such as experimental planning, data analysis, and decision-making – and low-level physical operations involving hardware control, sensor integration, and robotic manipulation. While traditional laboratory automation has relied heavily on specialized, rigid systems, foundation models offer adaptability through their general-purpose intelligence and multimodal capabilities. Recent advancements have demonstrated the feasibility of using large language models (LLMs) and multimodal robotic systems to handle complex and dynamic laboratory tasks. However, significant challenges remain, including precision manipulation of hardware, integration of multimodal data, and ensuring operational safety. This paper outlines a roadmap highlighting future directions, advocating for close interdisciplinary collaboration, benchmark establishment, and strategic human-AI integration to realize fully autonomous experimental laboratories. Automation ; Laboratory automation ; Foundation (evidence) ; Perspective (graphical) ; Adaptability ; Benchmark (surveying) ; Robotics Machine Learning in Materials Science ; Scientific Computing and Data Management ; AI-based Problem Solving and Planning
Authors: Yefan Lin | Ziyuan Wang | Lujia Zhang | Chengwei Zhang | Xiaojun Hei
Source:  (article, 2025-03-28)
Field: Materials Science — Materials Chemistry

ChemAI: Empowering Robots to Automate Chemical Experiments with Large Language Models Chemical experimentation remains fundamental to material discovery and compound synthesis, yet conventional methods face critical limitations. Manual laboratory workflows require repetitive operations that prolong research timelines, while robotic automation platforms inadvertently shift the burden to chemists through complex programming requirements. Existing systems also struggle to adapt to novel experimental designs, constraining scientific innovation. To bridge these gaps, we present a large language model (LLM)-driven robotic framework that redefines automated experimentation. Our system interprets natural language instructions through advanced LLM processing, dynamically integrates real-time environmental data, and autonomously generates executable code utilizing a modular action library. A self-correcting validation mechanism ensures operational precision, systematically resolving execution errors through iterative feedback. Empirical validation across two benchmark experiments including the iodine clock reaction kinetics and the salt purification demonstrates transformative advantages: 1) intuitive interaction; 2) extended workflow handling efficiency gain in multi-step protocols; 3) adaptive code generation. This paradigm shift may enable chemists to focus on hypothesis-driven research rather than procedural implementation, establishing a scalable infrastructure for next-generation autonomous laboratories. Robot ; Computer science ; Programming language ; Human–computer interaction ; Artificial intelligence ; Natural language processing ; Software engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Bingling Dai | Yuhang Song | Yue Zhan | Yibin Jiang | Cheng Wang
Source: Angewandte Chemie International Edition (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

From Data to Physics: An Agentic Large Language Model Solves a Competitive Adsorption Puzzle Scientific modeling often requires navigating a trade-off between physical interpretability and empirical accuracy-a task that can take weeks of iteration, especially in systems with partial observability, structural complexity, and experimental errors. Here, we show how an agentic reasoning-and-coding large language model (LLM), OpenAI o3, autonomously solved a modeling challenge in surface chemistry that puzzled us for months: quantifying the competitive adsorption of carboxylic acids on metal-organic layers (MOLs). With experimental data and a concise problem formulation, o3 rapidly formulated a physically grounded adsorption model, derived the mathematical equations, implemented the corresponding codes to fit the experimental data, revised its assumptions, and ultimately derived a competitive adsorption model with three parameters that matched experimental data across more than a dozen tested molecules. The resulting model-simple, mechanistically transparent, and quantitatively robust-incorporates both classical Langmuir competition and structural constraints such as site accessibility. Beyond addressing this particular challenge, our findings highlight a transformative shift in scientific methodology: from manual trial-and-error approaches to AI-driven hypothesis generation and model refinement. This represents a new paradigm in research, wherein language models surpass the traditional roles of machine learning in data analysis and computational support, actively participating in scientific reasoning and hypothesis development. Interpretability ; Empirical modelling ; Language model ; Experimental data ; Mathematical model ; Computational model ; Transformative learning ; Modeling language Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Scientific Computing and Data Management
Authors: Morgan Thomas | Albert Bou | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2025-01-31)
Field: Chemistry — Physical and Theoretical Chemistry

Test-Time Training Scaling Laws for Chemical Exploration in Drug Design Chemical Language Models (CLMs) leveraging reinforcement learning (RL) have shown promise in de novo molecular design, yet often suffer from mode collapse, limiting their exploration capabilities. Inspired by Test-Time Training (TTT) in large language models, we propose scaling TTT for CLMs to enhance chemical space exploration. We introduce MolExp, a novel benchmark emphasizing the discovery of structurally diverse molecules with similar bioactivity, simulating real-world drug design challenges. Our results demonstrate that scaling TTT by increasing the number of independent RL agents follows a log-linear scaling law, significantly improving exploration efficiency as measured by MolExp. In contrast, increasing TTT training time yields diminishing returns, even with exploration bonuses. We further evaluate cooperative RL strategies to enhance exploration efficiency. These findings provide a scalable framework for generative molecular design, offering insights into optimizing AI-driven drug discovery. Test (biology) ; Scaling ; Training (meteorology) ; Drug ; Computer science ; Medicine ; Pharmacology ; Mathematics ; Geology ; Physics Various Chemistry Research Topics
Authors: Yukun Wu | Aikaterini Vriza | Doga Ozgulbas | Rafael Vescovi | Jianing Zhou | Zhiyang Wang | Shiyu Hu | Yuepeng Zhang | Qiaomu Yang | Anna M. Österholm | John R. Reynolds | Subramanian K. R. S. Sankaranarayanan | Maria K. Y. Chan | Ian Foster | Jianguo Mei | Henry Chan | Jie Xu
Source: Journal of the American Chemical Society (article, 2025-11-21)
Field: Materials Science — Polymers and Plastics

Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy The design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure-property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange. Comonomer ; Electrochromism ; Workflow ; Copolymer ; Polymer ; Key (lock) ; Inverse Conducting polymers and applications ; Machine Learning in Materials Science ; Transition Metal Oxide Nanomaterials
Authors: Florian Simperl | Wolfgang Werner
Source: Journal of Applied Physics (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

A transformer network for high-throughput materials characterization with x-ray photoelectron spectroscopy The rapid increase of x-ray photoelectron spectroscopy (XPS) as a reliable surface sensitive materials characterization instrument at large-scale synchrotron facilities and emerging autonomous labs calls for a standardized, low-latency, and reproducible quantitative analysis workflow. The notoriously time-consuming and challenging task of spectra interpretation is addressed by combining XPS spectra simulation based on the partial intensity approach with modern data science techniques. We train a transformer neural network on a standardized, open-access XPS survey-spectra repository generated with the NIST Standard Reference Database 100 Simulation of Electron Spectra for Surface Analysis (SESSA). Running SESSA in parallel on the Austrian Scientific Computing infrastructure, we simulate more than 8×106 survey spectra for 7587 synthesizable inorganic and organic bulk compounds under an AlKα excitation source and the magic-angle geometry. We apply experimentally informed data augmentations to account for varying experimentally observed noise, peak broadening, and chemical shifts. The model infers elemental composition from survey spectra correctly for 85% of compounds in the test set (90% when excluding hydrogen). Over 80% of predicted elemental atomic concentrations deviate by less than 20% from ground truth, meeting typical experimental quantification thresholds. We report the full training and hyperparameter tuning workflow and describe benefits relative to conventional peak fitting as the fully trained model retrieves instantaneously the material information without any preprocessing steps. The workflow is adaptable to customized experimental input parameters, establishing a foundation for high-throughput, standardized, and ultimately autonomous quantitative XPS analysis. X-ray photoelectron spectroscopy ; NIST ; Workflow ; Spectral line ; Characterization (materials science) ; Synchrotron ; Transformer ; Electron spectroscopy ; Bottleneck Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Rokas Elijošius | Emma King‐Smith | Felix A. Faber | Louise Bernier | Simon Berritt | William Farrell | Xinjun Hou | Jacquelyn Klug‐McLeod | Jason Mustakis | Neal W. Sach | Qingyi Yang | Roger M. Howard | Alpha A. Lee
Source: Nature Communications (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Predictive design of crystallographic chiral separation The efficient separation of chiral molecules is a fundamental challenge in the manufacture of pharmaceuticals and light-polarising materials. We developed an approach that combines machine learning with a physics-based representation to predict resolving agents for chiral molecules, using a transformer-based neural network. In retrospective tests, our approach reaches a four to six-fold improvement over the historical - trial and error based - hit rate. We further validate the model in a prospective experiment, where we use the model to design a resolution screen for six unseen racemates. We successfully resolved three of the six mixtures in a single round of experiments and obtained an overall 8-to-1 true positive to false negative ratio. Together with this study, we release a previously proprietary dataset of over 6000 resolution experiments, the largest diastereomeric salt crystallisation dataset to date. More broadly, our approach and open crystallisation data lay the foundation for accelerating and reducing the costs of chiral resolutions. Crystallography ; Materials science ; Separation (statistics) ; Chemistry ; Computer science Crystallization and Solubility Studies ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Haoyu Yin | Yizhe Chen | Xiaonan Wang
Source: Scientia Sinica Chimica (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

AI foundational models in low-carbon energy and chemical engineering and the intelligent discovery platform for novel materials 实现双碳目标需要能源、化工和材料等多个领域协同，但其核心科学问题解决与关键工程技术实现的速度受传统研发范式掣肘，亟需突破。以基于“小数据”的主动学习和基于“大数据”的基础模型为代表的人工智能（AI）技术深刻改变了领域研究范式，垂直领域AI基础模型重构并建立了低碳能源化工领域的新一代知识网络，新材料智能发现平台标志着AI for Science的研究新范式在低碳能源化工领域的落地。近年来，研究者在低碳能源化工领域的多层次多尺度主动学习框架和AI基础模型上开展了一系列研究，本文将详细介绍相关工作并对低碳能源化工与人工智能交叉领域的机遇、挑战与展望进行讨论。 Carbon fibers ; Nanotechnology ; Data science ; Engineering physics ; Computer science ; Engineering ; Materials science Machine Learning in Materials Science
Authors: Kangyu Ji | Sheng Fang | Tianran Liu | Basita Das | Tonio Buonassisi
Source: ArXiv.org (preprint, 2025-05-14)
Field: Materials Science — Materials Chemistry

Multimodal transformers with elemental priors for phase classification of X-ray diffraction spectra Classifying a crystalline solid's phase using X-ray diffraction (XRD) is a challenging endeavor, first because this is a poorly constrained problem as there are nearly limitless candidate phases to compare against a given experimental spectrum, and second because experimental signals are confounded by overlapping peaks, preferred orientations, and phase mixtures. To address this challenge, we develop Chem-XRD, a multimodal framework based on vision transformer (ViT) architecture with two defining features: (i) crystallographic data is constrained by elemental priors and (ii) phase is classified according to probabilities (not absolutes). Elemental information can be extracted from pre-synthesis precursors or post-synthesis elemental analysis. By combining structural and elemental modalities, Chem-XRD simultaneously predicts both the number and identity of phases of lead-halide perovskite materials and their decomposition products. Through integrated gradient calculations, we show that the model can adeptly adjust the contributions of structural and elemental modalities toward the final prediction of phase identification, achieving a level of interpretability beyond what self-attention mechanisms can achieve. Interpretability ; Elemental analysis ; Diffraction ; Prior probability ; Phase (matter) ; Pattern recognition (psychology) X-ray Diffraction in Crystallography ; Neural Networks and Applications ; Industrial Technology and Control Systems
Authors: Sanghoon Lee | Kevin Cruse | Viktoriia Baibakova | Gerbrand Ceder | Anubhav Jain
Source: Scientific Data (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model Solid-state synthesis is widely used to obtain various inorganic materials, such as battery materials and bulk thermoelectrics. Despite its prevalence, the process remains challenging due to the lack of a general theory and well-understood underlying reaction mechanisms. While prior works have successfully extracted structured datasets from literature, they often neglect product phase purity or yield. In this work, we construct a solid-state synthesis dataset consisting of 80,806 syntheses extracted with a large language model (LLM), including 18,869 reactions with impurity phase(s). Our dataset not only validates expected thermodynamic trends for impurity phase formation but also identifies challenging cases where impurity phases emerge even when the target phase is significantly more stable. Impurity ; Construct (python library) ; Process (computing) ; Phase (matter) ; Product (mathematics) Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials
Authors: Hossein Mashhadimoslem | Peyman Karimi | A. Elkamel | Aiping Yu
Source: npj Computational Materials (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Toward high entropy material discovery for energy applications using computational and machine learning methods Machine learning and computational methods can accelerate materials discovery by accurately predicting material properties at low cost. Nevertheless, input data to algorithms and structure model parameters remains a key obstacle. The limitations of conventional battery materials could be overcome by high-entropy materials, a unique class of special valuable materials. The knowledge of designing the crystal structure of high-entropy materials is advancing the design and fabrication of new materials for batteries and supercapacitors, even before chemical synthesis, through the use of learning algorithms and quantum computing. In this review, we first focus on quantum computing and the structure of high-entropy materials, especially high-entropy MXenes. We then discuss how to encode and decode the crystal structure of materials, which is a key factor in creating a database for high-entropy materials. We also discuss how to utilize deep learning algorithms for material discovery prior to synthesis, as well as how to employ these algorithms to identify high-entropy materials suitable for batteries and supercapacitors. Finally, we discuss the potential of new quantum computing and artificial intelligence approaches for determining the structure of high-entropy materials in the energy fields. Key (lock) ; Focus (optics) ; Quantum computer ; ENCODE ; Entropy (arrow of time) ; Deep learning ; Energy (signal processing) MXene and MAX Phase Materials ; Machine Learning in Materials Science ; High Entropy Alloys Studies
Authors: Ben Gao | Penghui Li | Di Zhang | Qian Tan | Wanquan Liu | Xunzhi Wang | Junxian Li | Shufei Zhang | Dongzhan Zhou | Yuqiang Li | Guoyin Yin
Source: ChemRxiv (preprint, 2025-07-16)
Field: Materials Science — Materials Chemistry

LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that demonstrates remarkable performance in predicting individual organic reactions while achieving unprecedented multitask capacity. This unified architecture enables accurate forecasting of diverse reaction types through a single integrated model - a critical advancement that addresses longstanding limitations in traditional prediction methodologies. Importantly, our investigation reveals that strategic improvements in the foundational model architecture can substantially enhance the system's predictive accuracy. The demonstrated versatility and scalability of the LoRA-Chem framework suggest transformative potential for next-generation reaction prediction systems, offering a robust platform for developing sophisticated artificial intelligence solutions in synthetic chemistry. Modular design ; Computer science ; Machine learning ; Artificial intelligence ; Multi-task learning ; Task (project management) ; Engineering ; Systems engineering ; Operating system Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Shahanaz Parvin | Lutfor Rahman | Faisal Islam Chowdhury | Bristy Biswas | Md. Farid Ahmed | Moksodur Rahman | Md. Al-Amin | Md. Kamrul Hossain | Shirin Akter Jahan | Nahid Sharmin
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of Ag@Co–NiFe <sub>2</sub> O <sub>4</sub> /GO nanocomposites for photocatalysis: X-ray peak profiling, electromagnetic and optical properties This study presents the successful synthesis of Co<sub>0.5</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub> nanoparticles and their composites with silver (Ag) and graphene oxide (GO) using a facile ultrasonic assisted co-precipitation method. Powder X-ray diffraction (PXRD) verified the creation of spinel ferrite structures, integrating Ag nanoparticles and graphene oxide (GO). Crystallite sizes were calculated by detailed X-ray peak profiling using different models including the classical Scherrer method, Munshi-Scherrer method, Williamson-Hall method, size-strain plot method, Halder-Wagner method and conventional Rietveld refinement using GSAS-II software as well as the crystal structure derived from VESTA software. Field emission scanning electron microscopy showed spherical nanoparticles, with energy dispersive X-ray spectroscopy (EDX) verifying the elemental composition. Magnetic analysis showed ferrimagnetic behaviour, with reduced saturation magnetization in Ag and GO-modified samples but increased coercivity for GO-modified system. Optical bandgap analysis <i>via</i> UV-DRS indicated bandgaps ranging from 1.95 to 2.03 eV for Ag and GO composites. Dielectric properties were measured, showing a sharp decrease in the dielectric constant at low frequencies, followed by stabilization. Photocatalytic tests revealed that Ag@Co<sub>0.5</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub>/GO achieved the highest methylene blue degradation efficiency (96.59%) under visible light, attributed to enhanced electron-hole separation and silver's plasmonic effects. These results suggest that Co<sub>0.5</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub>-based nanocomposites, particularly Ag@Co<sub>0.5</sub>Ni<sub>0.5</sub>Fe<sub>2</sub>O<sub>4</sub>/GO, have potential for photocatalytic and environmental remediation applications. Crystallite ; Ferrimagnetism ; Rietveld refinement ; Dielectric ; Spinel ; Graphene ; Nanocomposite ; Band gap ; Zinc ferrite ; Coercivity Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Pigment Synthesis and Properties