jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haonan Huang
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Research While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher. What distinguishes research from routine execution is the progressive accumulation of knowledge -- learning which approaches fail, recognizing patterns across systems, and applying understanding to new problems. However, the prevailing paradigm in AI-driven computational science treats each execution in isolation, largely discarding hard-won insights between runs. Here we present QMatSuite, an open-source platform closing this gap. Agents record findings with full provenance, retrieve knowledge before new calculations, and in dedicated reflection sessions correct erroneous findings and synthesize observations into cross-compound patterns. In benchmarks on a six-step quantum-mechanical simulation workflow, accumulated knowledge reduces reasoning overhead by 67% and improves accuracy from 47% to 3% deviation from literature -- and when transferred to an unfamiliar material, achieves 1% deviation with zero pipeline failures. Pipeline (software) ; Reflection (computer programming) ; Theory of computation ; Closing (real estate) ; Computational model ; Consolidation (business) ; Overhead (engineering) ; Knowledge integration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Frazier N. Baker | Trieu Nguyen | Reza Averly | Botao Yu | Daniel Adu-Ampratwum | Huan Sun | Xia Ning
Source: arXiv (Cornell University) (preprint, 2026-04-06)
Field: Materials Science — Materials Chemistry

MMORF: A Multi-agent Framework for Designing Multi-objective Retrosynthesis Planning Systems Multi-objective retrosynthesis planning is a critical chemistry task requiring dynamic balancing of quality, safety, and cost objectives. Language model-based multi-agent systems (MAS) offer a promising approach for this task: leveraging interactions of specialized agents to incorporate multiple objectives into retrosynthesis planning. We present MMORF, a framework for constructing MAS for multi-objective retrosynthesis planning. MMORF features modular agentic components, which can be flexibly combined and configured into different systems, enabling principled evaluation and comparison of different system designs. Using MMORF, we construct two representative MAS: MASIL and RFAS. On a newly curated benchmark consisting of 218 multi-objective retrosynthesis planning tasks, MASIL achieves strong safety and cost metrics on soft-constraint tasks, frequently Pareto-dominating baseline routes, while RFAS achieves a 48.6% success rate on hard-constraint tasks, outperforming state-of-the-art baselines. Together, these results show the effectiveness of MMORF as a foundational framework for exploring MAS for multi-objective retrosynthesis planning. Code and data are available at https://anonymous.4open.science/r/MMORF/. Retrosynthetic analysis ; Construct (python library) ; Benchmark (surveying) ; Modular design ; Baseline (sea) ; Task (project management) ; Plan (archaeology) Machine Learning in Materials Science ; Process Optimization and Integration ; Constraint Satisfaction and Optimization
Authors: Sojeong Park | Wei Liu | Simon Julian Lauw | Wooseop Kwak | Chandrabhan Verma | Hwee Kuan Lee
Source: Computer Physics Communications (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Scalable neural network driven molecular dynamics simulation Molecular dynamics (MD) simulation is an essential tool for condensed matter physics, materials science, structural/mechanistic biology, and multi-agent systems. Despite their successes, traditional numerical integration methods for solving Hamilton’s equations of motion are computationally intensive, limiting simulations to short time scales. Recent advancements in machine learning have opened new avenues for accelerating MD simulations. This work introduces the Local Update Function network (LUFnet), a transformer-based neural network designed to increase time integration step sizes significantly while maintaining simulation stability and accuracy. LUFnet integrates local spatial and temporal information, enabling efficient rollout for long-time-scale simulations. By preserving key symmetries such as translational invariance, Galilean coordinate transformation invariance, and particle exchange symmetry, LUFnet achieves robust performance across different thermodynamic states. LUFnet is designed to accommodate general physical models (e.g., Lennard-Jones, Coulomb potential, on lattice systems). Its framework allows the model to be trained on small systems and directly applied to larger systems, maintaining computation efficiency and memory usage that scales linearly with the number of particles. Benchmarked on Lennard-Jones systems, LUFnet demonstrated minimal accuracy degradation even after rollout over many large time integration steps, offering an effective approach to molecular dynamics simulations. Scalability ; Computation ; Artificial neural network ; Molecular dynamics ; Stability (learning theory) ; Transformation (genetics) ; Key (lock) ; Lattice (music) ; Limiting Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Todd C. Hufnagel | Pranav Addepalli | Anuruddha Bhattacharjee | Rohit Berlia | Jaafar El-Awady | David Elbert | L.W. Brady | Axel Krieger | Harichandana Neralla | T. Joseph Nkansah-Mahaney | Mostafa M. Omar | Hyun Sang Park | K. T. Ramesh | Matthew Shaeffer | Eric Walker | Piyush Wanchoo | Timothy P. Weihs
Source: ArXiv.org (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-throughput and autonomous laboratories. To date most demonstrations of such laboratories have focused on functional materials, with less attention paid to structural materials. We present here the Artificial Intelligence in Materials Design Laboratory (AIMD-L), an automated, high-throughput facility for characterizing the microstructure and properties of structural metals and ceramics, with an emphasis on materials in extreme environments. AIMD-L has two custom instruments for characterization of structural materials: HELIX for shock studies of materials, and MAXIMA for X-ray diffraction and X-ray fluorescence spectroscopy. Specifically designed for high-throughput studies, HELIX and MAXIMA are each capable of collecting data at rates two to three orders of magnitude faster than conventional systems. A third experimental station, SPHINX, is a commercial nanoindenter modified for integration into the automated workflow of AIMD-L. A user (which may be human or an AI agent) directs the experiments to be carried out by means of a centralized control program. The experimental stations are linked by a conveyance that moves samples around the lab, with a robot at each station for sample transfer in/out of the instrument. The experimental stations also communicate with a common data layer that streams data autonomously from each instrument to a data portal, where their arrival triggers automated workflows for data reduction and analysis. The processed data are immediately available to the human operator or agentic AI, forming a closed loop for rapid decision-making and experimental control. Workflow ; Characterization (materials science) ; Automation ; Robot ; Data processing ; Human-in-the-loop ; Instrumentation (computer programming) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Electron Microscopy Techniques and Applications
Authors: Todd C. Hufnagel | Pranav Addepalli | Anuruddha Bhattacharjee | Rohit Berlia | Jaafar El-Awady | David Elbert | L.W. Brady | Axel Krieger | Harichandana Neralla | T. Joseph Nkansah-Mahaney | Mostafa M. Omar | Hyun Sang Park | K. T. Ramesh | Matthew Shaeffer | Eric Walker | Piyush Wanchoo | Timothy P. Weihs
Source: Open MIND (preprint, 2026-03-06)
Field: Materials Science — Materials Chemistry

AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-throughput and autonomous laboratories. To date most demonstrations of such laboratories have focused on functional materials, with less attention paid to structural materials. We present here the Artificial Intelligence in Materials Design Laboratory (AIMD-L), an automated, high-throughput facility for characterizing the microstructure and properties of structural metals and ceramics, with an emphasis on materials in extreme environments. AIMD-L has two custom instruments for characterization of structural materials: HELIX for shock studies of materials, and MAXIMA for X-ray diffraction and X-ray fluorescence spectroscopy. Specifically designed for high-throughput studies, HELIX and MAXIMA are each capable of collecting data at rates two to three orders of magnitude faster than conventional systems. A third experimental station, SPHINX, is a commercial nanoindenter modified for integration into the automated workflow of AIMD-L. A user (which may be human or an AI agent) directs the experiments to be carried out by means of a centralized control program. The experimental stations are linked by a conveyance that moves samples around the lab, with a robot at each station for sample transfer in/out of the instrument. The experimental stations also communicate with a common data layer that streams data autonomously from each instrument to a data portal, where their arrival triggers automated workflows for data reduction and analysis. The processed data are immediately available to the human operator or agentic AI, forming a closed loop for rapid decision-making and experimental control. Workflow ; Characterization (materials science) ; Automation ; Robot ; Data processing ; Human-in-the-loop ; Instrumentation (computer programming) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Electron Microscopy Techniques and Applications
Authors: Aayushi Pratap
Source: C&EN Global Enterprise (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Chemical industry to bet on agentic AI Agentic artificial intelligence, a form of AI capable of making autonomous decisions, is gaining traction in the laboratory and on the factory floor. The chemical industry will increasingly adopt it in 2026.“AI agents are autonomous units, something that can think and do stuff on its own. But, of course, it needs to be instructed,” says Marc Feldmann, senior principal at Alexander Thamm, an AI consulting firm that has advised several chemical companies. Feldmann says that, for now, he is seeing adoption mainly among big firms such as BASF and Bayer. In a blog post published on the Chemical Industry Journal website, Feldmann cites the example of the chemical giant Celanese, which created JO.AI, a platform of specialized AI agents, for its plant operations.“One agent optimises shift checklists. Another flags anomalies—‘Corrosion might be an issue in heat exchanger 3.’ A third drafts work orders,” he writes about the Celanese agent.Traditional AI applications are often preprogrammed and operate with limited autonomy. In contrast, agentic AI is more autonomous and can independently review the literature, formulate hypotheses, design experiments, learn from feedback, and collaborate with humans through conversations.The advent of agentic AI is driven by the growing popularity of large language models, which can understand and generate human language. “The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative problem-solving,” write Yi Luo and colleagues in a paper about their creation of what they call a multiagent-driven robotic AI Popularity ; Workflow ; Principal (computer security) ; Factory (object-oriented programming) ; Task (project management) ; Big data ; Reuse Machine Learning in Materials Science ; Multi-Agent Systems and Negotiation ; Robotic Process Automation Applications
Authors: Kang Xu | Tao Yu | Kangwei Xu | Chenxu Liu | Rongjie Wu
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

EnzySeek: Efficient Exploration of Enzyme Reaction Pathways Using AI Agents Abstract With the rapid development of Large Language Models (LLMs) and Agent technologies, AI can assist in solving a variety of real-world problems across multiple domains, such as autonomous driving, drug discovery, and materials design. In this work, we present EnzySeek, an enzyme catalysis AI agent designed to assist researchers in enzyme catalysis simulations. First, we constructed a domain-specific knowledge base by curating thousands of papers related to enzyme catalysis. Second, we customized Model Context Protocol (MCP) interfaces for each step of the enzyme catalysis simulation workflow, enabling these functions to be invoked by LLMs. Finally, we configured an agent capable of simultaneously referencing past empirical studies on enzyme catalysis, autonomously executing tool calls, and analyzing as well as presenting the results. EnzySeek’s capabilities cover multiple aspects, including protein structure prediction, molecular docking, system preparation and parameterization, molecular dynamics (MD) simulations, and QM/MM calculations. The conclusions drawn by EnzySeek are primarily based on the results of QM/MM calculations. We employed the semi-empirical quantum mechanical method GFN2-xTB to calculate the QM region of the system. Benchmark results indicate that the GFN2-xTB method can achieve high efficiency while maintaining accuracy. The EnzySeek agent is designed to continuously learn from newly published literature and past computational tasks. During its operation, every AI decision is manually verified and scored by human experts. This human-in-the-loop validation provides the AI with sufficient case-based support, ultimately contributing to the full automation of enzyme catalysis computations. All data generated during the simulations are compiled into a dataset, which is used to establish evaluation criteria specific to enzyme catalysis computational results. Context (archaeology) ; Benchmark (surveying) ; Knowledge base ; Automation ; Protocol (science) ; Variety (cybernetics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Protein Structure and Dynamics
Authors: S. D. Pandey | Xuan-Bac Nguyen | Hoang-Quan Nguyen | Tim Faltermeier | Nicholas J. Borys | Hugh Churchill | Khoa Luu
Source: arXiv (Cornell University) (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials The transition from optical identification of 2D quantum materials to practical device fabrication requires dynamic reasoning beyond the detection accuracy. While recent domain-specific Multimodal Large Language Models (MLLMs) successfully ground visual features using physics-informed reasoning, their outputs are optimized for step-by-step cognitive transparency. This yields verbose candidate enumerations followed by dense reasoning that, while accurate, may induce cognitive overload and lack immediate utility for real-world interaction with researchers. To address this challenge, we introduce OpenQlaw, an agentic orchestration system for analyzing 2D materials. The architecture is built upon NanoBot, a lightweight agentic framework inspired by OpenClaw, and QuPAINT, one of the first Physics-Aware Instruction Multi-modal platforms for Quantum Material Discovery. This allows accessibility to the lab floor via a variety of messaging channels. OpenQlaw allows the core Large Language Model (LLM) agent to orchestrate a domain-expert MLLM,with QuPAINT, as a specialized node, successfully decoupling visual identification from reasoning and deterministic image rendering. By parsing spatial data from the expert, the agent can dynamically process user queries, such as performing scale-aware physical computation or generating isolated visual annotations, and answer in a naturalistic manner. Crucially, the system features a persistent memory that enables the agent to save physical scale ratios (e.g., 1 pixel = 0.25 μm) for area computations and store sample preparation methods for efficacy comparison. The application of an agentic architecture, together with the extension that uses the core agent as an orchestrator for domain-specific experts, transforms isolated inferences into a context-aware assistant capable of accelerating high-throughput device fabrication. Visual reasoning ; Question answering ; Variety (cybernetics) ; Identification (biology) ; Parsing ; Cognitive architecture ; Computation ; Process (computing) Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Quantum many-body systems
Authors: Zhengding Hu | Kuntal Talit | Zhen Wang | Haseeb Ahmad | Yichen Lin | Prabhleen Kaur | Christopher Lane | Elizabeth A. Peterson | Zhiting Hu | Elizabeth Nowadnick | Yufei Ding
Source: Open MIND (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

TritonDFT: Automating DFT with a Multi-Agent Framework Density Functional Theory (DFT) is a cornerstone of materials science, yet executing DFT in practice requires coordinating a complex, multi-step workflow. Existing tools and LLM-based solutions automate parts of the steps, but lack support for full workflow automation, diverse task adaptation, and accuracy-cost trade-off optimization in DFT configuration. To this end, we present TritonDFT, a multi-agent framework that enables efficient and accurate DFT execution through an expert-curated, extensible workflow design, Pareto-aware parameter inference, and multi-source knowledge augmentation. We further introduce DFTBench, a benchmark for evaluating the agent's multi-dimensional capabilities, spanning science expertise, trade0off optimization, HPC knowledge, and cost efficiency. TritonDFT provides an open user interface for real-world usage. Our website is at https://www.tritondft.com. Our source code and benchmark suite are available at https://github.com/Leo9660/TritonDFT.git. Workflow ; Suite ; Benchmark (surveying) ; Task (project management) ; Code (set theory) ; Interface (matter) ; Extensibility Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Chun Huang
Source: arXiv (Cornell University) (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

AI Cosplaying as Astrophysicists: A Controlled Synthetic-Agent Study of AI-Assisted Astrophysical Research Workflows Large Language Models (LLMs) are now widely used in astrophysics, but do they actually make our lives easier, or do they merely invent new physics with enough confidence to hide a minus sign? In a specialized field where checking fluent hallucinations is itself labor-intensive, AI assistance can demand as much work as the task it claims to simplify. To evaluate where AI genuinely improves scientific workflows, we bypassed human trials and instead forced AI agents to cosplay as astrophysicists. We simulated 144 synthetic researchers, varying in career stage, AI awareness, and willingness to verify outputs, across 2,592 daily astrophysics research assignments. Comparing solo work against four styles of AI assistance produced 12,960 scored episodes. No assisted policy universally outperformed unassisted work in the primary Qwen production run. Instead, performance depends strongly on the task, the style of AI use, and the identity of the actor. While cautious assistance helps on creative, extractive, and critique-oriented tasks, it can fail catastrophically on derivation-heavy physics. A full actor-swap DeepSeek rerun changes that picture materially: verification-heavy use becomes the strongest assisted policy, two assisted modes enter the higher-utility/lower-risk quadrant, and the derivation-heavy fragility that dominates the Qwen production run largely disappears. In its current form, AI is useful, but only conditionally, its value is uneven, task-specific, and shaped jointly by workflow, usage policy, and which LLM you are using. Workflow ; Task (project management) ; Field (mathematics) ; Work (physics) ; Production (economics) ; Style (visual arts) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Machine Learning and Data Classification
Authors: Zhuofan Shi | Hen A | Yun Shao | Huang, Dongliang | An, Hongxu | Xin, Chunxiao | Dongliang Huang | Wang, Zhenyu | C. J. Xin | Haiyang Shen | Jing, Xiang
Source: Open MIND (preprint, 2026-01-05)
Field: Materials Science — Materials Chemistry

MDAgent2: Large Language Model for Code Generation and Knowledge Q&amp;A in Molecular Dynamics Molecular dynamics (MD) simulations are essential for understanding atomic-scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time-consuming tasks. Although LLMs show promise in code generation and domain-specific question answering, their performance in MD scenarios is limited by scarce domain data, the high deployment cost of state-of-the-art LLMs, and low code executability. Building upon our prior MDAgent, we present MDAgent2, the first end-to-end framework capable of performing both knowledge Q&amp;A and code generation within the MD domain. We construct a domain-specific data-construction pipeline that yields three high-quality datasets spanning MD knowledge, question answering, and code generation. Based on these datasets, we adopt a three stage post-training strategy--continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL)--to train two domain-adapted models, MD-Instruct and MD-Code. Furthermore, we introduce MD-GRPO, a closed-loop RL method that leverages simulation outcomes as reward signals and recycles low-reward trajectories for continual refinement. We further build MDAgent2-RUNTIME, a deployable multi-agent system that integrates code generation, execution, evaluation, and self-correction. Together with MD-EvalBench proposed in this work, the first benchmark for LAMMPS code generation and question answering, our models and system achieve performance surpassing several strong baselines.This work systematically demonstrates the adaptability and generalization capability of large language models in industrial simulation tasks, laying a methodological foundation for automatic code generation in AI for Science and industrial-scale simulations. URL: https://github.com/FredericVAN/PKU_MDAgent2 Code generation ; Code (set theory) ; Scripting language ; Benchmark (surveying) ; Language model ; Pipeline (software) ; Domain (mathematical analysis) ; Construct (python library) Machine Learning in Materials Science ; Topic Modeling ; Domain Adaptation and Few-Shot Learning