jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanxian Tao | Lingyun Wan | Xiongzhi Zeng | Yingdi Jin | Liu Jie | Zhenyu Li | Jinlong Yang
Source: ArXiv.org (preprint, 2025-11-11)
Field: Materials Science — Materials Chemistry

Quantum-centric machine learning for molecular dynamics Accurate and efficient prediction of electronic wavefunctions is central to ab initio molecular dynamics (AIMD) and electronic structure theory. However, conventional ab initio methods require self-consistent optimization of electronic states at every nuclear configuration, leading to prohibitive computational costs, especially for large or strongly correlated systems. Here, we introduce a quantum-centric machine learning (QCML) model-a hybrid quantum-classical framework that integrates parameterized quantum circuits (PQCs) with Transformer-based machine learning to directly predict molecular wavefunctions and quantum observables. By pretraining the Transformer on a diverse dataset of molecules and ansatz types and subsequently fine-tuning it for specific systems, QCML learns transferable mappings between molecular descriptors and PQC parameters, eliminating the need for iterative variational optimization. The pretrained model achieves chemical accuracy in potential energy surfaces, atomic forces, and dipole moments across multiple molecules and ansatzes, and enables efficient AIMD simulations with infrared spectra prediction. This work establishes a scalable and transferable quantum-centric machine learning paradigm, bridging variational quantum algorithms and modern deep learning for next-generation molecular simulation and quantum chemistry applications. Wave function ; Ansatz ; Electronic structure ; Quantum ; Ab initio ; Molecular dynamics ; Parameterized complexity ; Quantum machine learning ; Dipole Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Erik Svensson Grape | Carl K. Brozek
Source: ChemRxiv (preprint, 2025-05-14)
Field: Chemistry — Inorganic Chemistry

Turn-on Conductivity with Proton-Coupled Electron Transport in Metal–Organic Frameworks Proton-coupled electron transfer (PCET) has been studied for decades in the context of molecular reactivity, but its impact on long-range electron transport is barely understood. When defined broadly as ion-coupled charge transport (ICCT), relevant systems include lithium-ion battery electrodes, electrochromic coatings, and myriad electrocatalysts. Despite ample evidence that ion- electron coupling enhances or diminishes the performance of these devices, little is known about the experimental signatures of ICCT and the microscopic factors that govern its mechanism. We expect that ion-electron coupling becomes especially relevant in high surface area materials, such as the layered electrodes of intercalation batteries, due to the close proximity of itinerant electrons and electrolyte. Here, we report an electrochemical investigation into a family of metal-organic frameworks (MOFs) that serves as a well-defined platform for understanding the effect of ICCT on both electronic and ionic conductivity. Through photochemical doping of e-–H+ pairs and introduction of solvent guest molecules, the Ti-containing MOFs convert from electronic-only insulators conductors (σe ≈ 10-12 S cm-1) to mixed ion-electron semiconductors (σe ≈ 10-7 S cm-1, σion ≈ 10-5 S cm-1). Direct current and alternating current techniques support the existence of proton-electron coupling and, critically, that improved ionic conductivity enhances electronic conductivity. Taken together, these results provide direct evidence that PCET enables long-range charge transport and generalized electrochemical tools and synthetic methods for studying ion- electron coupling in materials broadly. Conductivity ; Proton ; Metal-organic framework ; Electron ; Electron transport chain ; Metal ; Materials science ; Chemistry ; Physics ; Physical chemistry ; Nuclear physics ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Electron and X-Ray Spectroscopy Techniques ; Electrochemical Analysis and Applications
Authors: Jiayu Peng | Chuanyu Liu | Yiwen Luo | Kritarth Dandapat
Source: AI Agent (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Accelerating multimetallic catalyst discovery with robotics and agentic AI The design space of catalyst materials spans composition, processing, atomistic structure, and microstructure. As materials become more complex, the dimensionality of this parameter space for catalyst design grows combinatorially. Conventional active learning approaches operate on a single data stream and stay decoupled from the messy reality of experiments, limiting their efficiency and reproducibility in real-world catalyst optimization. To tackle this limitation, in a recent issue of Nature, Li et al. developed a robotic platform, Copilot for Real-world Experimental Scientists (CRESt), which facilitates multimetallic catalyst discovery in a multiplex parameter space by combining multimodal large vision-language models, knowledge-assisted Bayesian optimization, and robotic automation of synthesis, characterization, and electrochemical tests. Deployed on a direct formate fuel cell use case, CRESt efficiently explored hundreds of compositions and thousands of tests in months to deliver an octonary multimetallic electrocatalyst with excellent device-level performance at reduced noble-metal loading. In this Commentary, we highlight CRESt’s technical merits, while also outlining a forward agenda to translate systems such as CRESt from proof-of-concept, bespoke demonstrations to widely adoptable, scientifically robust agentic artificial intelligence for self-driving laboratories. Robotics ; Bespoke ; Applications of artificial intelligence ; Automation ; Workflow ; Space (punctuation) ; Curse of dimensionality ; Chemical space Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Akshat Chaudhari | Janghoon Ock | Amir Barati Farimani
Source: ChemRxiv (preprint, 2025-04-29)
Field: Materials Science — Materials Chemistry

Modular Large Language Model Agents for Multi-Task Computational Materials Science The integration of large language models (LLMs) with domain-specific computational tools offers a promising pathway to streamline and enhance materials science workflows. This paper presents MatSciAgent, a multi-agent framework capable of performing key tasks such as materials data retrieval, continuum simulation, crystal structure generation, and molecular dynamics simulation. At the core of the framework is the master agent, which interprets the user’s natural language query, identifies the task type, and delegates it to a corresponding task-specific agent equipped with appropriate computational tools. Leveraging databases such as the Materials Project and MatWeb, the framework retrieves and summarizes materials data with grounded, factual responses—addressing a key limitation of vanilla LLMs. In cases where the target material is not found in existing databases, a generative task-specific agent can propose plausible crystal structures. For simulation tasks, dedicated agents extract relevant parameters from the user query to conduct continuum simulations (e.g., Cellular Automata and Monte Carlo Annealing) and atomistic simulations (e.g., Molecular Dynamics) using both established software and custom code. The modular design of these agents and their associated tools enables seamless extensibility, allowing the framework to evolve as new capabilities are integrated. Modular design ; Task (project management) ; Computer science ; Programming language ; Artificial intelligence ; Systems engineering ; Engineering Machine Learning in Materials Science
Authors: Tiffany J. Callahan | Nathaniel H. Park | Sara Capponi
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Agentic Mixture-of-Workflows for Multi-Modal Chemical Search The vast and complex materials design space demands innovative strategies to integrate multidisciplinary scientific knowledge and optimize materials discovery. While large language models (LLMs) have demonstrated promising reasoning and automation capabilities across various domains, their application in materials science remains limited due to a lack of benchmarking standards and practical implementation frameworks. To address these challenges, we introduce Mixture-of-Workflows for Self-Corrective Retrieval-Augmented Generation (CRAG-MoW) - a novel paradigm that orchestrates multiple agentic workflows employing distinct CRAG strategies using open-source LLMs. Unlike prior approaches, CRAG-MoW synthesizes diverse outputs through an orchestration agent, enabling direct evaluation of multiple LLMs across the same problem domain. We benchmark CRAG-MoWs across small molecules, polymers, and chemical reactions, as well as multi-modal nuclear magnetic resonance (NMR) spectral retrieval. Our results demonstrate that CRAG-MoWs achieve performance comparable to GPT-4o while being preferred more frequently in comparative evaluations, highlighting the advantage of structured retrieval and multi-agent synthesis. By revealing performance variations across data types, CRAG-MoW provides a scalable, interpretable, and benchmark-driven approach to optimizing AI architectures for materials discovery. These insights are pivotal in addressing fundamental gaps in benchmarking LLMs and autonomous AI agents for scientific applications. Benchmarking ; Workflow ; Benchmark (surveying) ; Multidisciplinary approach ; Orchestration ; Chemical space ; Automation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Anurag Acharya | Anshu Kiran Sharma | Donald J. Parker | Timothy Vega | Rizwan A. Ashraf | Natalie M. Isenberg | J. Strube | Robert Ralló
Source:  (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

LABMATE: Language Model Based Multi-Agent System to Accelerate Catalysis Experiments As Large Language Models (LLMs) have become increasingly commonplace, they have been shown to be useful to domains far beyond just linguistic tasks. However, they are still unreliable to make decisions due to their potential to hallucinate, and unable to perform complex tasks like running simulations that are essential to a field like Material Science. To solve this, we introduce LABMATE (LAnguage model Based Multi-agent system to Accelerate caTalysis Experiments), a human-in-the-loop copilot framework that utilizes LLM agents to make catalysis research faster. LABMATE allows the human experts to submit simulation runs, track particle sizes, run data analysis, perform literature review, and generate potential hypothesis all in one framework, thereby expediting the research process. When evaluated on the Chemistry and Material Science segments of major benchmarks, LABMATE performs comparable to or better than most frontier LLMs, showing that in addition to accelerating the experimental process, our framework is also on par in domain knowledge compared to using a simple LLM. These results show that the system is able to significantly aid and improve the research process for human scientists. Furthermore, since the core architecture of the system is domain-agnostic, it can easily be adapted to other domains. Expediting ; Process (computing) ; Field (mathematics) ; Domain (mathematical analysis) ; Simple (philosophy) ; Complex system ; Matching (statistics) ; Core (optical fiber) Machine Learning in Materials Science ; Language and cultural evolution ; Scientific Computing and Data Management
Authors: Tuba Dolar | Nicolò Maria della Ventura | Florent Mignerot | Zihan Wang | H. C. Howard | Christopher T. Kassner | H.N.G. Wadley | Daniel S. Gianola | Wei Chen
Source: Materials & Design (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Accelerating materials discovery in heterogeneous composition-property design spaces via collaborative Bayesian optimization • Consensus-based multi-agent Bayesian optimization (BO) explores heterogeneous spaces. • Collaboration boosts efficiency, benchmarks show when simpler BO suffices. • Multi-agent BO accelerates discovery of HfTiTaNb alloys with target properties. Adaptive learning implementations for materials design are challenged by the complex, nonlinear relationships between composition and properties, particularly in high-performance applications such as high-temperature compositionally complex refractory alloys. Traditional Bayesian optimization (BO) methods, which typically rely on a single Gaussian Process (GP) surrogate, often struggle to model heterogenous behaviors across the design domain. To address this limitation, we introduce collaborative BO as a multi-agent framework for materials discovery. In the context of optimizing compositions for desired properties, each agent models a specific subregion of the design space, where subregions share similar property trends, and exchanges information with the other agents to expedite exploration and design optimization. Comparative evaluations demonstrate that, when compared to single-agent BO and other approaches discussed in this article multi-agent BO allows flexible information-sharing protocols and effectively reduces iterations of adaptive learning while reliably delivering designs that meet the targeted mechanical properties. These findings provide novel insights into the behavior of refractory multi-component alloys, using the Hf-Ti-Ta-Nb system as a case study, and illustrate the potential of adaptive multi-agent learning in efficiently screening extensive materials libraries. Moreover, the framework is broadly applicable to other problems characterized by diverse data sources, where advanced optimization strategies are essential for accelerated materials discovery. Bayesian optimization ; Context (archaeology) ; Implementation ; Gaussian process ; Bayesian probability ; Process (computing) ; Property (philosophy) ; Probabilistic logic Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Srijit Seal | Dinh Long Huynh | Moudather Chelbi | Sepehr Khosravi | Ankur Kumar | Mattson Thieme | Isaac Wilks | Mark Davies | Jessica Mustali | Ying Sun | John N. Edwards | Daniil A. Boiko | Andrei S. Tyrin | Douglas W. Selinger | Ayaan Parikh | R. S. K. Vijayan | Shoman Kasbekar | D. Reid | Andreas Bender | Ola Spjuth
Source: ArXiv.org (preprint, 2025-10-31)
Field: Materials Science — Materials Chemistry

AI Agents in Drug Discovery Artificial intelligence (AI) agents are emerging as transformative tools in drug discovery, with the ability to autonomously reason, act, and learn through complicated research workflows. Building on large language models (LLMs) coupled with perception, computation, action, and memory tools, these agentic AI systems could integrate diverse biomedical data, execute tasks, carry out experiments via robotic platforms, and iteratively refine hypotheses in closed loops. We provide a conceptual and technical overview of agentic AI architectures, ranging from ReAct and Reflection to Supervisor and Swarm systems, and illustrate their applications across key stages of drug discovery, including literature synthesis, toxicity prediction, automated protocol generation, small-molecule synthesis, drug repurposing, and end-to-end decision-making. To our knowledge, this represents the first comprehensive work to present real-world implementations and quantifiable impacts of agentic AI systems deployed in operational drug discovery settings. Early implementations demonstrate substantial gains in speed, reproducibility, and scalability, compressing workflows that once took months into hours while maintaining scientific traceability. We discuss the current challenges related to data heterogeneity, system reliability, privacy, and benchmarking, and outline future directions towards technology in support of science and translation. Workflow ; Implementation ; Key (lock) ; Drug discovery ; Transformative learning ; Reflection (computer programming) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Xu Huang | Junwu Chen | Yuxing Fei | Zhuohan Li | Philippe Schwaller | Ceder Gerbrand
Source: Open MIND (preprint, 2025-12-29)
Field: Materials Science — Materials Chemistry

CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution Large language model (LLM) agents currently depend on predefined tools or early-stage tool generation, limiting their adaptability and scalability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition". CASCADE enables agents to master complex external tools and codify knowledge through two meta-skills: continuous learning via web search, code extraction, and memory utilization; self-reflection via introspection, knowledge graph exploration, and others. We evaluate CASCADE on SciSkillBench, a benchmark of 116 materials science and chemistry research tasks. CASCADE achieves a 93.3% success rate using GPT-5, compared to 35.4% without evolution mechanisms. We further demonstrate real-world applications in computational analysis, autonomous laboratory experiments, and selective reproduction of published papers. Along with human-agent collaboration and memory consolidation, CASCADE accumulates executable skills that can be shared across agents and scientists, moving toward scalable AI-assisted scientific research. Executable ; Scalability ; Cascade ; Adaptability ; Limiting ; Benchmark (surveying) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: L. Yao | Suman Samantray | Ayana Ghosh | Kevin M. Roccapriore | Libor Kovarik | Sarah I. Allec | Maxim Ziatdinov
Source: ArXiv.org (preprint, 2025-08-07)
Field: Materials Science — Materials Chemistry

Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loop The history of science is punctuated by serendipitous discoveries, where unexpected observations, rather than targeted hypotheses, opened new fields of inquiry. While modern autonomous laboratories excel at accelerating hypothesis testing, their optimization for efficiency risks overlooking these crucial, unplanned findings. To address this gap, we introduce SciLink, an open-source, multi-agent artificial intelligence framework designed to operationalize serendipity in materials research by creating a direct, automated link between experimental observation, novelty assessment, and theoretical simulations. The framework employs a hybrid AI strategy where specialized machine learning models perform quantitative analysis of experimental data, while large language models handle higher-level reasoning. These agents autonomously convert raw data from materials characterization techniques into falsifiable scientific claims, which are then quantitatively scored for novelty against the published literature. We demonstrate the framework's versatility across diverse research scenarios, showcasing its application to atomic-resolution and hyperspectral data, its capacity to integrate real-time human expert guidance, and its ability to close the research loop by proposing targeted follow-up experiments. By systematically analyzing all observations and contextualizing them, SciLink provides a practical framework for AI-driven materials research that not only enhances efficiency but also actively cultivates an environment ripe for serendipitous discoveries, thereby bridging the gap between automated experimentation and open-ended scientific exploration. Workflow ; Operationalization ; Novelty ; Bridging (networking) ; Serendipity ; Raw data ; Modularity (biology) ; Characterization (materials science) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhucong Li | Bowei Zhang | Xiao Jin | Zhijian Zhou | Fenglei Cao | Jiaqing Liang | Qi Yuan
Source: ArXiv.org (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools Large Language Model (LLM)-based agents have demonstrated the ability to improve performance in chemistry-related tasks by selecting appropriate tools. However, their effectiveness remains limited by the inherent prediction errors of chemistry tools. In this paper, we take a step further by exploring how LLMbased agents can, in turn, be leveraged to reduce prediction errors of the tools. To this end, we propose ChemHAS (Chemical Hierarchical Agent Stacking), a simple yet effective method that enhances chemistry tools through optimizing agent-stacking structures from limited data. ChemHAS achieves state-of-the-art performance across four fundamental chemistry tasks, demonstrating that our method can effectively compensate for prediction errors of the tools. Furthermore, we identify and characterize four distinct agent-stacking behaviors, potentially improving interpretability and revealing new possibilities for AI agent applications in scientific research. Our code and dataset are publicly available at https: //anonymous.4open.science/r/ChemHAS-01E4/README.md. Interpretability ; Stacking ; Simple (philosophy) ; Code (set theory) ; Source code Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Artificial Intelligence in Healthcare and Education
Authors: Heng Ma | Alexander Brace | Carlo Siebenschuh | Ian Foster | Arvind Ramanathan
Source:  (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

LangChain-Parsl: Connect Large Language Model Agents to High Performance Computing Resource Large Language Models (LLMs) can improve performance in answering questions beyond their contextual understanding by running external tools, such as a calculator for arithmetics, an online query for real-time weather, et al. For scientific applications, this enables the LLM to perform and analyze simulation runs for more accurate answers. However, the increasing scale of scientific computing requires high-performance computers (HPCs), which are managed by job schedulers. In this work, we implemented Parsl to the LangChain tool calling to bridge the gap between the LLM agent and the HPC resource. Two implementations were set up and tested on a local Nvidia GPU workstation and the Polaris/ALCF HPC system. The first setup was implemented by modifying the LangChain tool calling, which converts the LangChain tool calls to Parsl functions and queues them to the Parsl workers for parallel execution. The second approach was achieved by designing a Parsl ensemble function as an LLM tool, which performed parallel tasks. With these implementations, the LLM agent workflow was prompted to run molecular dynamics simulations, with different protein structures and simulation conditions. The results show that our Parsl implementations enable parallel execution of scientific tools that invoked by LLM agents on both local GPU workstations and HPC platforms. Workflow ; Workstation ; Implementation ; Supercomputer ; Set (abstract data type) ; Bridge (graph theory) ; Resource (disambiguation) ; Calculator Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education