jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Honghao Chen | Jiangjie Qiu | Yi Shen Tew | Xiaonan Wang
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

CatMaster: An Agentic Autonomous System for Computational Heterogeneous Catalysis Research Density functional theory (DFT) is widely used to connect atomic structure with catalytic behavior, but computational heterogeneous catalysis studies often require long workflows that are costly, iterative, and sensitive to setup choices. Besides the intrinsic cost and accuracy limits of first-principles calculations, practical workflow issues such as keeping references consistent, preparing many related inputs, recovering from failed runs on computing clusters, and maintaining a complete record of what was done, can slow down projects and make results difficult to reproduce or extend. Here we present CatMaster, a large-language-model (LLM)-driven agent system that turns natural language requests into complete calculation workspaces, including structures, inputs, outputs, logs, and a concise run record. CatMaster maintains a persistent project record of key facts, constraints, and file pointers to support inspection and restartability. It is paired with a multi-fidelity tool library that covers rapid surrogate relaxations and high-fidelity DFT calculations for validation when needed. We demonstrate CatMaster on four demonstrations of increasing complexity: an O2 spin-state check with remote execution, BCC Fe surface energies with a protocol-sensitivity study and CO adsorption site ranking, high-throughput Pt--Ni--Cu alloy screening for hydrogen evolution reaction (HER) descriptors with surrogate-to-DFT validation, and a demonstration beyond the predefined tool set, including equation-of-state fitting for BCC Fe and CO-FeN4-graphene single-atom catalyst geometry preparation. By reducing manual scripting and bookkeeping while keeping the full evidence trail, CatMaster aims to help catalysis researchers focus on modeling choices and chemical interpretation rather than workflow management. Workflow ; Scripting language ; Focus (optics) ; Key (lock) ; Automation ; Bookkeeping ; Heterogeneous catalysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Rigoberto Advincula | Jihua Chen
Source: ArXiv.org (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Chemical Reaction Engineering and Catalysis: AI/ML Workflows and Self-Driving Laboratories Chemical reaction engineering is key to industrial might and sustainable chemistry. This will be enabled using smart, efficient catalysts or catalysis ecosystems. This is possible with advanced artificial intelligence and machine learning (AI/ML) workflows that need to be employed as agentic AI projects. The fundamentals of catalysis need to be emphasized. A strong focus on catalyst design, mechanistic studies, reaction engineering, and scale-up must use ML-driven workflows, along with high-throughput experimentation (HTE) and an autonomous, self-driving laboratory (SDL). Laboratory experience and data-driven approaches are valuable when working together to accelerate this development. Parametrize and create a virtuous circle for data-driven discovery across heterogeneous, homogeneous, and biocatalysts to enable utility in many chemical process industries as agentic AI tasks. This article builds the case for discovery science in catalysis and continuous improvement in chemical reaction engineering with this new ecosystem. Workflow ; Chemical reaction engineering ; Process (computing) ; Key (lock) ; Focus (optics) ; Chemical process Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Rigoberto C. Advincula | Jihua Chen
Source: arXiv (Cornell University) (article, 2026-02-16)
Field: Materials Science — Surfaces, Coatings and Films

Polymer Brushes and Grafted Polymers: AI/ML-Driven Synthesis, Simulation, and Characterization towards autonomous SDL Polymer brushes and grafted polymers have attracted significant interest at the intersection of polymers, interfacial chemistry, colloidal science, and nanostructuring. The confinement of high-density grafted polymers and differences in swelling regimes govern the synthetic challenges and the interesting physics underlying their macromolecular dynamics. In this article, we focus on another intersection, artificial intelligence and machine learning (AI/ML), and how workflows will enhance the microstructure and composition of these systems. It will also accelerate potential applications through high-throughput experimentation (HTE) and data-driven intelligence, enabling scientific discovery and optimization. Applications in microfluidics, sensors, bioimplants, drug delivery, and related areas may yet offer more opportunities for ML-driven optimization. There is also interest in applying these studies with self-driving laboratories (SDLs) that can leverage autonomous systems for synthesis screening, characterization, and application evaluation. Characterization (materials science) ; Polymer ; Leverage (statistics) ; Workflow ; Intersection (aeronautics) ; Focus (optics) Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications ; Advanced Polymer Synthesis and Characterization
Authors: AI Scientists | Xinyi Lin | Danqing Yin | Ying Guo
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation CO2 reduction requires efficient catalysts, yet materials discovery remains bottlenecked by 10-20 year development cycles requiring deep domain expertise. This paper demonstrates how large language models can assist the catalyst discovery process by helping researchers explore chemical spaces and interpret results when augmented with retrieval-based grounding. We introduce a retrieval-augmented generation framework that enables GPT-4 to navigate chemical space by accessing a database of 50,000+ known materials, adapting general-purpose language understanding for high-throughput materials design. Our approach generated over 250 catalyst candidates with an 82% thermodynamic stability rate while addressing multi-objective constraints: 68% achieved <$100/kg cost with metallic conductivity (band gap<0.1eV) and mechanical stability (B/G>1.75). The best-performing Fe0.2Co0.2Ni0.2Ir0.1Ru0.3 achieves 0.285V limiting potential (25% improvement over IrO2), while Cr0.2Fe0.2Co0.3Ni0.2Mo0.1 optimally balances performance-cost trade-offs at $18/kg. Volcano plot analysis confirms that 78% of LLM-generated catalysts cluster near the theoretical activity optimum, while our system achieves 200x computational efficiency compared to traditional high-throughput screening. By demonstrating that retrieval-augmented generation can ground AI creativity in physical constraints without sacrificing exploration, this work demonstrates an approach where natural language interfaces can streamline materials discovery workflows, enabling researchers to explore chemical spaces more efficiently while the LLM assists in result interpretation and hypothesis generation. Limiting ; Chemical space ; Domain (mathematical analysis) ; Process (computing) ; Interpretation (philosophy) ; Stability (learning theory) ; Space (punctuation) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Boshko Koloski | Senja Pollak | Sašo Džeroski | Aleksandar Kondinski
Source: Inorganic Chemistry Frontiers (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Digitalisation of inorganic chemistry with LLMs Over the past few years, large language models have become technologically ubiquitous and now offer a powerful route to accelerate discoveries in inorganic chemistry. Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Jihua Chen | Rigoberto C. Advíncula
Source:  (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Advanced Polymer Manufacturing Artificial intelligence (AI) is currently transforming the polymer lifecycle, from materials discovery and design to manufacturing and recycling processes. In this review, we will discuss the undergoing paradigm shift from conventional labor-intensive processes to machine learning and data driven automation, including the key machine learning models and techniques being applied across diverse polymer science application areas. Significant limitations and opportunities in AI-driven polymer manufacturing exist in data availability and model interpretability. Key (lock) ; Applications of artificial intelligence ; Advanced manufacturing ; Computer-integrated manufacturing ; Big data Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Polymer Synthesis and Characterization
Authors: Rigoberto C Advincula | Jihua Chen
Source: ChemRxiv (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Towards AI/ML-Driven Optimization of Dendrimer Design, Synthesis, and Applications Artificial intelligence and Machine learning (AI/ML) are of high interest for polymer chemistry and hybrid materials. The chemical composition, molecular, and macromolecular properties ultimately translate into their macroscopic properties and function. Various data sources, including scientific literature, are now augmented with large language models (LLM) via retrieval-augmented generation (RAG). These datasets serve as the basis for machine-readable descriptors, which can inform new molecular designs and synthesis routes. This perspective highlights directions and advances in dendrimer materials that can enhance data availability and the predictability of solutionand solid-state properties, as well as their applications in nanomaterials, energy, drug delivery, sensors, polymer processing and manufacturing. The theory and microstructure of dendrimers are revisited. AI/ML workflows, LLM and RAG applications, and the importance of the human-in-the-loop are emphasized. ML enables close computational Dendrimer ; Predictability ; Polymer ; Perspective (graphical) Machine Learning in Materials Science ; Dendrimers and Hyperbranched Polymers ; Computational Drug Discovery Methods
Authors: Jiayun Pang | Ahmed M. Zaitoun | Xacobe Couso Cambeiro | Ivan Vulić
Source: ArXiv.org (article, 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&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 ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Jinming Hu | Hassan Nawaz | Yi-Fan Hou | Yuting Rui | Lijie Chi | Yuxinxin Chen | Arif Ullah | Pavlo O. Dral
Source: ChemRxiv (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations We have developed Aitomia – a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. Aitomia combines LLM-based agents with the MLatom platform to support AI-driven atomistic simulations as well as conventional quantum-chemical calculations, including DFT, semiempirical methods such as GFN2-xTB, and selected high-level wavefunction-based methods, through interfaces to widely used programs such as Gaussian, ORCA, PySCF, and xtb, covering tasks from ground-state and excited-state calculations to geometry optimization, thermochemistry, and spectra simulations. The multi-agent implementation enables autonomous execution of complex computational workflows, such as reaction enthalpy calculations. Aitomia was the first intelligent assistant publicly launched on cloud computing platforms for broad-scope atomistic simulations (Aitomistic Lab@XMU at https://atom.xmu.edu.cn and Aitomistic Hub at https://aitomistic.xyz). Aitomia lowers the barrier to performing atomistic simulations, thereby democratizing simulations and accelerating research and development in relevant fields. Quantum chemical ; Molecular dynamics ; Cloud computing ; Graphical user interface ; Quantum ; Quantum computer Machine Learning in Materials Science ; Quantum many-body systems ; Scientific Computing and Data Management
Authors: Mert O. Astam | Ouassim L'Karkouri | Danqing Liu
Source: Advanced Functional Materials (article, 2026-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Intelligence as the Next Visionary in Liquid Crystal Research ABSTRACT Artificial intelligence (AI) is transforming the world of research with methods that condense months of empirical experiments into hours of computation and automation. Hence, applying AI in more specialized fields, namely liquid crystals (LCs) and liquid crystal polymers (LCPs), is of major interest to the scientific community. Therefore, in this perspective paper, we explore the use of AI in molecular and polymer property prediction and generative design, chemical reaction prediction, and synthesis route design; we will also review the use of AI to automate lab operations and analyze scientific literature. By dissecting the reviewed AI functions and inspecting their current limitations, such as hallucinations, we provide an outlook on the potential uses of AI for LC research, supplemented with a description of a fully automated LCP development process. We envision that AI‐driven assistants will facilitate the on‐demand design of LCs and LCPs for desired anisotropic responsive properties, accelerating both theoretical and application‐oriented research. Liquid crystal ; Perspective (graphical) ; Generative grammar ; Applications of artificial intelligence ; Computation ; Property (philosophy) Liquid Crystal Research Advancements ; Machine Learning in Materials Science ; Molecular spectroscopy and chirality
Authors: Xuefeng Bai | Zhiling Zheng | Xin Zhang | Hao-Tian Wang | Rui Yang | Jian-Rong Li
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

MOFReasoner: think like a scientist—a reasoning large language model <i>via</i> knowledge distillation MOFReasoner is a domain-specific LLM enhanced by chain-of-thought reasoning and knowledge distillation that, when compared to GPT-4.5, shows superior performance in chemical tasks like MOF adsorption prediction. Distillation ; Automated reasoning ; Model-based reasoning ; Knowledge acquisition ; Process (computing) ; Knowledge representation and reasoning ; Knowledge-based systems Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Jaewoong Lee | Taeun Bae | Jihan Kim
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Chemistry — Inorganic Chemistry

SimMOF: AI agent for Automated MOF Simulations Metal-organic frameworks (MOFs) offer a vast design space, and as such, computational simulations play a critical role in predicting their structural and physicochemical properties. However, MOF simulations remain difficult to access because reliable analysis require expert decisions for workflow construction, parameter selection, tool interoperability, and the preparation of computational ready structures. Here, we introduce SimMOF, a large language model based multi agent framework that automates end-to-end MOF simulation workflows from natural language queries. SimMOF translates user requests into dependency aware plans, generates runnable inputs, orchestrates multiple agents to execute simulations, and summarizes results with analysis aligned to the user query. Through representative case studies, we show that SimMOF enables adaptive and cognitively autonomous workflows that reflect the iterative and decision driven behavior of human researchers and as such provides a scalable foundation for data driven MOF research. Workflow ; Scalability ; Dependency (UML) ; Iterative refinement ; Natural language ; Computational model Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Abdoulatif Cissé | Max Cooper | Mengjia Zhu | Xenophon Evangelopoulos | Andrew I. Cooper
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Can we automate scientific reasoning in closed-loop experiments using large language models? Reasoning models (LLMs) were used as optimisers for a 10-D chemistry problem and a 7-D physics simulation. The best performance was achieved using iterative closed-loop reasoning with a batch size of one. Scientific reasoning ; Automated reasoning ; Model-based reasoning ; Automation ; Semantics (computer science) ; Computation Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Lev Krasnov | Sergei V. Tatarin | Stanislav I. Bezzubov
Source: ChemRxiv (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Can LLMs Reason About Solubility? The SoluBench Benchmark for Pure and Mixed Solvent Systems Solubility prediction is a fundamental task in chemistry and drug discovery, yet the capability of large language models (LLMs) to solve solubility-related problems remains underexplored. Here we present SoluBench, a benchmark comprising 9806 questions across four tasks of increasing complexity, designed to evaluate LLMs’ performance on solubility reasoning in both pure and mixed solvent systems. The tasks include pairwise solvent comparison (Task 1), single-best solvent selection (Task 2), co-solvent effect prediction (Task 3), and pairwise compound comparison (Task 4). The benchmark is grounded in experimental data from BigSolDB 2.0 and MixtureSolDB datasets. Systematic evaluation of over 20 proprietary and open-source LLMs reveals that frontier proprietary models achieve strong performance on solvent-oriented tasks, with Gemini 3 Flash reaching 90.6%, 66.2%, and 86.4% accuracy on Tasks 1, 2, and 3, respectively. Open-source models consistently underperform their proprietary counterparts. The solute-focused Task 4 is exceptionally challenging in standard settings, requiring explicit reasoning to achieve functional accuracy, particularly for polar aprotic solvents such as DMSO and DMF. Across all tasks, a universal performance bottleneck is identified for large solutes (MolWt > 500 Da). These findings demonstrate that while LLMs possess intrinsic chemical reasoning capabilities sufficient for qualitative solubility assessment in technological practice, significant limitations in molecular structure understanding persist. Benchmark (surveying) ; Bottleneck ; Pairwise comparison ; Task (project management) ; Solubility ; Order (exchange) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Harikrishna Sahu | Wei Xiong | Anagha Savit | Shivank S. Shukla | Rampi Ramprasad
Source: npj Artificial Intelligence (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

POLYT5: an encoder-decoder foundation chemical language model for generative polymer design Traditional machine learning has advanced polymer discovery, yet direct generation of chemically valid and synthesizable polymers without exhaustive enumeration remains a challenge. Here we present POLYT5, an encoder-decoder chemical language model based on the T5 architecture, trained to understand and generate polymer structures. POLYT5 enables both property prediction and the targeted generation of polymers conditioned on desired property values. We demonstrate its utility for dielectric polymer design, seeking candidates with dielectric constant >3, bandgap >4 eV, and glass transition temperature >400 K, alongside melt-processability and solubility requirements. From over 18,000 generated promising candidates, one was experimentally synthesized and validated, showing strong agreement with predictions. To further enhance usability, we integrated POLYT5 within an agentic AI framework that couples it with a general-purpose LLM, allowing natural language interaction for property prediction and generative design. Together, these advances establish a versatile and accessible framework for accelerated polymer discovery. Generative grammar ; Polymer ; Property (philosophy) ; Foundation (evidence) ; Natural language ; Dielectric ; Constant (computer programming) ; Chemical space Machine Learning in Materials Science ; Design Education and Practice ; Material Selection and Properties
Authors: Abdoulatif Cissé | M. J. Cooper | Mengjia Zhu | Xenophon Evangelopoulos | Alan Cooper
Source: ChemRxiv (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Can We Automate Scientific Reasoning in Closed-Loop Experiments using Large Language Models? We present here a detailed study of our hybrid optimisation framework, BORA, which integrates large language model (LLM) reasoning with Bayesian optimisation (BO) for accelerating scientific discovery using closed-loop experiments. We compare five modern LLMs (o4-mini, o3, gpt-5-mini, gpt-5, and gemini-2.5-flash) as optimisers for two benchmark problems: a 10-dimensional photocatalytic hydrogen-evolution experiment and a 7-dimensional physics-based pétanque simulation. The results show that LLM/BO hybrids outperform BO-only approaches, particularly in early-stage exploration where the search is warm-started by LLM-driven hypotheses. Among the models tested, o3 delivered the strongest and most consistent optimisation performance after 150 experiments. LLM-only optimisations without the BO component also matched or surpassed hybrid methods in some settings, locating optima with high repeatability. We demonstrated that appending human hypotheses, prior literature, or experimental datasets can improve convergence, and that LLM reasoning can recover in some cases from deliberately misleading prompts. We also explored outlier runs to understand the limitations and failure modes of these methods, as well as considering the energy implications of the LLM queries. The strongest LLM-only performance was observed with a batch size of one, suggesting that experiment-by-experiment machine reasoning is a viable strategy for certain automated scientific optimisation tasks. Benchmark (surveying) ; Scientific reasoning ; Outlier ; Bayesian probability ; Component (thermodynamics) ; Key (lock) ; Language model Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Scientific Computing and Data Management