jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bilal Muhammed | Akash Bhattacharjee | B. P. Gautham | Amol Joshi
Source: International Journal of AI for Materials and Design (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

A multi-agent deep reinforcement learning framework for the generative design of alloys and processing routes The design of alloys and their manufacturing processes requires extensive exploration of a broad design space comprising various compositional and processing variables, many of which remain inadequately explored in practice. The existence of multiple viable processing routes for achieving desired alloy properties further complicates the design process. This paper presents a multi-agent deep reinforcement learning (DRL) framework for the in silico design of alloys and their processing routes/conditions tailored to specific property targets. The framework consists of distinct decentralized DRL agents, each responsible for making decisions regarding composition selection and the individual manufacturing steps involved in the process. These agents interact with their respective environments, which represent the assigned processes, and share responsibilities related to both process-specific outcomes and overall property satisfaction, as governed by the reward functions. The reward functions integrate considerations of sustainability, cost, and manufacturability into the decision-making process. A generative design step is proposed to leverage the capabilities of the trained DRL agents to produce multiple design alternatives for a given requirement. The framework is applied to the design of a hot-rolled steel sheet, exploring two feasible processing routes: Conventional casting and thin slab casting, resulting in several alternatives for each route. The framework&amp;rsquo;s performance is evaluated on two experimental cases from the literature, indicating its success in biasing the sample toward the preferred solution space. A benchmark study is conducted to evaluate the framework&amp;rsquo;s performance against designs produced by materials engineers for three distinct use cases, demonstrating the superior performance of the proposed framework. Reinforcement learning ; Design for manufacturability ; Leverage (statistics) ; Generative grammar ; Design of experiments ; Benchmark (surveying) Machine Learning in Materials Science ; Digital Transformation in Industry ; Topology Optimization in Engineering
Authors: Jisu Kim | Hyunsung Cho | Hyejin Jeon | Jisu Jung | Seungwu Han
Source: Chemical Reviews (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science In the era of artificial intelligence (AI), machine learning interatomic potentials (MLIPs) have revolutionized materials science and engineering, enabling large-scale and accurate atomistic modeling in various chemical systems. The performance of these potentials hinges on two key components: the model architecture, which defines the mathematical representation of atomic interactions, and the training data set, which supplies foundational knowledge. To scrutinize recent advances and chart the course for future development, this review presents a comprehensive analysis of reactive MLIPs, focusing on these two pillars. We first explore the evolution of model architectures, from descriptor-based models to state-of-the-art equivariant graph neural networks, critically assessing the essential requisites for achieving both physical accuracy and computational efficiency. Concurrently, we examine the sophisticated data acquisition strategies required to capture the complexities of chemical reactions, with a particular focus on uncertainty-driven active learning for sampling high-energy transition states and reaction pathways. Finally, beyond reviewing current methodologies, we provide an outlook on domain-specific challenges and discuss emerging opportunities in next-generation technologies, such as generative AI and cognitive autonomous agents. We believe this review will serve as a valuable guideline for practitioners in selecting and constructing MLIPs tailored for the study of chemical reactivity. Interatomic potential ; Statistical learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Iman Peivaste | Nicolas D. Boscher | Ahmed Makradi | Salim Belouettar
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks Covalent organic frameworks (COFs) are promising photocatalysts for solar hydrogen production, yet the most electronically favorable linkages, imines, hydrolyze rapidly in water, creating a stability--activity trade-off that limits practical deployment. Navigating the combinatorial design space of nodes, linkers, linkages, and functional groups to identify candidates that are simultaneously active and durable remains a formidable challenge. Here we introduce Ara, a large-language-model (LLM) agent that leverages pretrained chemical knowledge, donor--acceptor theory, conjugation effects, and linkage stability hierarchies, to guide the search for photocatalytic COFs satisfying joint band-gap, band-edge, and hydrolytic-stability criteria. Evaluated against random search and Bayesian optimization (BO) over a space consisting of candidates with various nodes, linkers, linkages, and r-groups, screened with a GFN1-xTB fragment pipeline, Ara achieves a 52.7\% hit rate (11.5$\times$ random, p = 0.006), finds its first hit at iteration 12 versus 25 for random search, and significantly outperforms BO (p = 0.006). Inspection of the agent's reasoning traces reveals interpretable chemical logic: early convergence on vinylene and beta-ketoenamine linkages for stability, node selection informed by electron-withdrawing character, and systematic R-group optimization to center the band gap at 2.0 eV. Exhaustive evaluation of the full search space uncovers a complementary exploitation--exploration trade-off between the agent and BO, suggesting that hybrid strategies may combine the strengths of both approaches. These results demonstrate that LLM chemical priors can substantially accelerate multi-criteria materials discovery. Workflow ; Bayesian optimization ; Chemical space ; Inverse ; Node (physics) ; Space (punctuation) ; Convergence (economics) ; Hydrolysis Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Xue Jia | Di Zhang | Yiming Lu | Qian Wang | Hao Li
Source: AI Agent (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

StableOx-Cat agent: an AI agent for exploring stable metal oxide electrocatalysts We introduce StableOx-Cat, an artificial intelligence (AI)-agent framework that enables systematic and reliable exploration of stable metal oxide (MO) electrocatalysts via a unified natural-language interface. StableOx-Cat integrates a large language model (LLM) for intent understanding and task orchestration with deterministic, physics-based analysis tools for electrocatalysis evaluation. User queries expressed in natural language are automatically parsed into structured actions, including database statistics, bulk thermodynamic stability screening based on energy-above-hull criteria, and aqueous electrochemical stability analysis under user-defined pH values and electrochemical potential windows. By applying the physical criteria to screen the stable MO electrocatalysts, StableOx-Cat avoids hallucinations and ensures a physically based stability analysis. This Agent enables the assessment of aqueous electrochemical stability across a wide range of reactions, with applied potentials spanning -2 to 2 V versus standard hydrogen electrode and pH values ranging from 0 to 14. Representative use cases demonstrate how StableOx-Cat enables flexible stability screening of MOs under both thermodynamic and aqueous environments. In addition, the agent architecture supports integration with different LLMs for task execution and query parsing. Overall, StableOx-Cat provides an accessible platform for stability-oriented materials exploration, offering a practical pathway to accelerate the discovery of experimentally relevant MO electrocatalysts for electrochemical applications, and can be generalized to other classes of electrocatalysts, such as alloys, metal nitrides, and carbides. Electrocatalyst ; Electrochemistry ; Aqueous solution ; Stability (learning theory) ; Electrode ; Oxide ; Parsing Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
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: arXiv (Cornell University) (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

MDAgent2: Large Language Model for Code Generation and Knowledge Q&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&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
Authors: Arpan Biswas | Hiroshi Funakubo | Yongtao Liu
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

Human-AI Collaborative Autonomous Experimentation With Proxy Modeling for Comparative Observation Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameters need a rapid strategic search through active learning such as Bayesian optimization (BO). However, such high-dimensional experimental physical descriptors are complex and noisy, from which realization of a low-dimensional mathematical scalar metrics or objective functions can be erroneous. Moreover, in traditional purely data-driven autonomous exploration, such objective functions often ignore the subtle variation and key features of the physical descriptors, thereby can fail to discover unknown phenomenon of the material systems. To address this, here we present a proxy-modelled Bayesian optimization (px-BO) via on-the-fly teaming between human and AI agents. Over the loop of BO, instead of defining a mathematical objective function directly from the experimental data, we introduce a voting system on the fly where the new experimental outcome will be compared with existing experiments, and the human agents will choose the preferred samples. These human-guided comparisons are then transformed into a proxy-based objective function via fitting Bradley-Terry (BT) model. Then, to minimize human interaction, this iteratively trained proxy model also acts as an AI agent for future surrogate human votes. Finally, these surrogate votes are periodically validated by human agents, and the corrections are then learned by the proxy model on-the-fly. We demonstrated the performance of the proposed px-BO framework into simulated and BEPS data generated from PTO sample. We find that our approach provided better control of the domain experts for an improved search over traditional data-driven exploration, thus, signifies the importance of human-AI teaming in an accelerated and meaningful material space exploration. Bayesian optimization ; Human-in-the-loop ; Proxy (statistics) ; Bayesian probability ; Key (lock) ; Voting ; Function (biology) ; Domain knowledge Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Arpan Biswas | Hiroshi Funakubo | Yongtao Liu
Source: arXiv (Cornell University) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Human-AI Collaborative Autonomous Experimentation With Proxy Modeling for Comparative Observation Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameters need a rapid strategic search through active learning such as Bayesian optimization (BO). However, such high-dimensional experimental physical descriptors are complex and noisy, from which realization of a low-dimensional mathematical scalar metrics or objective functions can be erroneous. Moreover, in traditional purely data-driven autonomous exploration, such objective functions often ignore the subtle variation and key features of the physical descriptors, thereby can fail to discover unknown phenomenon of the material systems. To address this, here we present a proxy-modelled Bayesian optimization (px-BO) via on-the-fly teaming between human and AI agents. Over the loop of BO, instead of defining a mathematical objective function directly from the experimental data, we introduce a voting system on the fly where the new experimental outcome will be compared with existing experiments, and the human agents will choose the preferred samples. These human-guided comparisons are then transformed into a proxy-based objective function via fitting Bradley-Terry (BT) model. Then, to minimize human interaction, this iteratively trained proxy model also acts as an AI agent for future surrogate human votes. Finally, these surrogate votes are periodically validated by human agents, and the corrections are then learned by the proxy model on-the-fly. We demonstrated the performance of the proposed px-BO framework into simulated and BEPS data generated from PTO sample. We find that our approach provided better control of the domain experts for an improved search over traditional data-driven exploration, thus, signifies the importance of human-AI teaming in an accelerated and meaningful material space exploration. Bayesian optimization ; Human-in-the-loop ; Proxy (statistics) ; Bayesian probability ; Key (lock) ; Voting ; Function (biology) ; Domain knowledge Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Mingxi Jiang | Mohamed AitTamerd | Ziqing He | Wenzhu Bi | Min Zhao | Shuran Li | Menghao Yang
Source: Materials Genome Engineering Advances (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

MatImageAgent: A Large Language Multimodal Agent for Materials Characterization Images ABSTRACT Our study introduces MatImageAgent, an agent‐based system that employs large language models (LLMs) to completely automate the image analysis workflow in materials science. MatImageAgent was developed to process images from scanning electron microscopy (SEM), X‐ray computed tomography (XCT), and atomic force microscopy (AFM) using properly engineered task descriptions (TDs) to execute an integrated process from image recognition and quantitative analysis to automated report generation. Its performance was evaluated using three primary tasks: detecting Li‐striped voids and estimating heights in SEM imaging (TASK 1), extracting greyscale values and analyzing lithium deposition in XCT data (TASK 2), and separating different phases in polymer films using AFM scans (TASK 3). MatImageAgent was coupled with three popular LLMs (GPT‐4.1, Claude 3.7, and DeepSeek‐R1). It exhibited high stability up to 10 independent cycles, yielding a mean absolute error of 0.11 μm for SEM measurements and an accuracy of 0.82 for AFM phase marking. Our AI agent improves analytical productivity by minimizing the requirement of advanced programming skills. Workflow ; Characterization (materials science) ; Grayscale ; Process (computing) ; Scanning electron microscope ; Phase (matter) ; Deposition (geology) ; Atomic force microscopy ; Task (project management) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Electron Microscopy Techniques and Applications
Authors: S. D. Pandey | Xuan-Bac Nguyen | Hoang-Quan Nguyen | Tim Faltermeier | Nicholas J. Borys | Hugh Churchill | Khoa Luu
Source: ArXiv.org (article, 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: Hongzhi Zhang | Yifan Hu | Tinghai Zhang | Jia Fu | Tao Wang | Junwei Jing | Fan, Zhaoxin | Qi Wang | Ruiming Tang | Han Li | Guorui Zhou | Kun Gai
Source: arXiv (Cornell University) (preprint, 2026-01-07)
Field: Materials Science — Materials Chemistry

DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retrieval synthesis stage--where agents must digest massive amounts of context and consolidate fragmented evidence into coherent, long-form reports--remains under-evaluated due to the subjectivity of open-ended writing. To bridge this gap, we introduce DeepSynth-Eval, a benchmark designed to objectively evaluate information consolidation capabilities. We leverage high-quality survey papers as gold standards, reverse-engineering research requests and constructing "Oracle Contexts" from their bibliographies to isolate synthesis from retrieval noise. We propose a fine-grained evaluation protocol using General Checklists (for factual coverage) and Constraint Checklists (for structural organization), transforming subjective judgment into verifiable metrics. Experiments across 96 tasks reveal that synthesizing information from hundreds of references remains a significant challenge. Our results demonstrate that agentic plan-and-write workflows significantly outperform single-turn generation, effectively reducing hallucinations and improving adherence to complex structural constraints. Workflow ; Leverage (statistics) ; Verifiable secret sharing ; Subjectivity ; Executable ; Context (archaeology) ; Consolidation (business) Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management
Authors: Hongzhi Zhang | Yifan Hu | Tinghai Zhang | Jia Fu | Tao Wang | Junwei Jing | Fan, Zhaoxin | Qi Wang | Ruiming Tang | Han Li | Guorui Zhou | Kun Gai
Source: ArXiv.org (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retrieval synthesis stage--where agents must digest massive amounts of context and consolidate fragmented evidence into coherent, long-form reports--remains under-evaluated due to the subjectivity of open-ended writing. To bridge this gap, we introduce DeepSynth-Eval, a benchmark designed to objectively evaluate information consolidation capabilities. We leverage high-quality survey papers as gold standards, reverse-engineering research requests and constructing "Oracle Contexts" from their bibliographies to isolate synthesis from retrieval noise. We propose a fine-grained evaluation protocol using General Checklists (for factual coverage) and Constraint Checklists (for structural organization), transforming subjective judgment into verifiable metrics. Experiments across 96 tasks reveal that synthesizing information from hundreds of references remains a significant challenge. Our results demonstrate that agentic plan-and-write workflows significantly outperform single-turn generation, effectively reducing hallucinations and improving adherence to complex structural constraints. Workflow ; Leverage (statistics) ; Verifiable secret sharing ; Subjectivity ; Executable ; Context (archaeology) ; Consolidation (business) Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management