jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Asraful Haque | Daniel T. Yimam | Jawad Chowdhury | Ralph Bulanadi | Ivan Vlassiouk | John Lasseter | Sujoy Ghosh | Christopher M. Rouleau | Kai Xiao | Yongtao Liu | Eva Zarkadoula | Rama K. Vasudevan | Sumner B. Harris
Source: ArXiv.org (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Human-AI collaborative autonomous synthesis with pulsed laser deposition for remote epitaxy Autonomous laboratories typically rely on data-driven decision-making, occasionally with human-in-the-loop oversight to inject domain expertise. Fully leveraging AI agents, however, requires tightly coupled, collaborative workflows spanning hypothesis generation, experimental planning, execution, and interpretation. To address this, we develop and deploy a human-AI collaborative (HAIC) workflow that integrates large language models for hypothesis generation and analysis, with collaborative policy updates driving autonomous pulsed laser deposition (PLD) experiments for remote epitaxy of BaTiO$_3$/graphene. HAIC accelerated the hypothesis formation and experimental design and efficiently mapped the growth space to graphene-damage. In situ Raman spectroscopy reveals that chemistry drives degradation while the highest energy plume components seed defects, identifying a low-O$_2$ pressure low-temperature synthesis window that preserves graphene but is incompatible with optimal BaTiO$_3$ growth. Thus, we show a two-step Ar/O$_2$ deposition is required to exfoliate ferroelectric BaTiO$_3$ while maintaining a monolayer graphene interlayer. HAIC stages human insight with AI reasoning between autonomous batches to drive rapid scientific progress, providing an evolution to many existing human-in-the-loop autonomous workflows. Workflow ; Pulsed laser deposition ; Deposition (geology) ; Monolayer ; Raman spectroscopy ; Graphene ; Window (computing) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Artificial Intelligence in Healthcare and Education
Authors: Aikaterini Vriza | Michael H. Prince | Tao Zhou | Henry Chan | Mathew J. Cherukara
Source: ArXiv.org (preprint, 2025-08-27)
Field: Materials Science — Materials Chemistry

Operating advanced scientific instruments with AI agents that learn on the job Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities. Pipeline (software) ; Automation ; Toolbox ; Focus (optics) ; Bridge (graph theory) ; Transformative learning ; Workflow Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: Qiang Xu | Shengyuan Bai | Leqing Chen | Zijing Liu | Yu Li
Source: ArXiv.org (preprint, 2025-11-20)
Field: Materials Science — Materials Chemistry

ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025 Olympiad-level benchmarks in mathematics and physics are crucial testbeds for advanced AI reasoning, but chemistry, with its unique multimodal symbolic language, has remained an open challenge. We introduce ChemO, a new benchmark built from the International Chemistry Olympiad (IChO) 2025. ChemO features two key innovations for automated assessment: Assessment-Equivalent Reformulation (AER), which converts problems requiring visual outputs (e.g., drawing molecules) into computationally tractable formats, and Structured Visual Enhancement (SVE), a diagnostic mechanism to disentangle a model's visual perception capabilities from its core chemical reasoning. To tackle this benchmark, we propose ChemLabs, a hierarchical multi-agent framework that mimics human expert collaboration through specialized agents for problem decomposition, perception, reasoning, and auditing. Experiments on state-of-the-art multimodal models demonstrate that combining SVE with our multi-agent system yields dramatic performance gains. Our top configuration achieves a score of 93.6 out of 100, surpassing an estimated human gold medal threshold and establishing a new state-of-the-art in automated chemical problem-solving. ChemO Dataset: https://huggingface.co/datasets/IDEA-AI4SCI/ChemO Benchmark (surveying) ; Key (lock) ; Perception ; Visualization ; Expert system ; Core (optical fiber) ; Olympiad Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Advanced Graph Neural Networks
Authors: Sebastian Rupprecht | Qinghe Gao | Tanuj Karia | Artur M. Schweidtmann
Source: ArXiv.org (preprint, 2025-08-11)
Field: Materials Science — Materials Chemistry

Multi-agent systems for chemical engineering: A review and perspective Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MAS within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous data modalities, development of foundation models with domain-specific modalities, and strategies for ensuring transparency, safety, and environmental impact. As a young but fast-moving field, MASs offer exciting opportunities to rethink chemical engineering workflows. Perspective (graphical) ; Workflow ; Foundation (evidence) ; Sociology of scientific knowledge ; Information system Machine Learning in Materials Science ; Multi-Agent Systems and Negotiation ; Process Optimization and Integration
Authors: In-Ja Lee | Junhyeong Lee | Jong‐Won Park | KyungTae Lim | Seunghwa Ryu
Source: ArXiv.org (preprint, 2025-11-27)
Field: Materials Science — Materials Chemistry

Enhanced Conditional Generation of Double Perovskite by Knowledge-Guided Language Model Feedback Double perovskites (DPs) are promising candidates for sustainable energy technologies due to their compositional tunability and compatibility with low-energy fabrication, yet their vast design space poses a major challenge for conditional materials discovery. This work introduces a multi-agent, text gradient-driven framework that performs DP composition generation under natural-language conditions by integrating three complementary feedback sources: LLM-based self-evaluation, DP-specific domain knowledge-informed feedback, and ML surrogate-based feedback. Analogous to how knowledge-informed machine learning improves the reliability of conventional data-driven models, our framework incorporates domain-informed text gradients to guide the generative process toward physically meaningful regions of the DP composition space. Systematic comparison of three incremental configurations, (i) pure LLM generation, (ii) LLM generation with LLM reasoning-based feedback, and (iii) LLM generation with domain knowledge-guided feedback, shows that iterative guidance from knowledge-informed gradients improves stability-condition satisfaction without additional training data, achieving over 98% compositional validity and up to 54% stable or metastable candidates, surpassing both the LLM-only baseline (43%) and prior GAN-based results (27%). Analyses of ML-based gradients further reveal that they enhance performance in in-distribution (ID) regions but become unreliable in out-of-distribution (OOD) regimes. Overall, this work provides the first systematic analysis of multi-agent, knowledge-guided text gradients for DP discovery and establishes a generalizable blueprint for MAS-driven generative materials design aimed at advancing sustainable technologies. Generative grammar ; Blueprint ; Iterative and incremental development ; Process (computing) ; Reliability (semiconductor) ; Domain (mathematical analysis) ; Compatibility (geochemistry) ; Weighting Machine Learning in Materials Science ; Perovskite Materials and Applications ; Ammonia Synthesis and Nitrogen Reduction
Authors: Soleymanibrojeni, Mohammad | Aydin, Roland | Guedes-Sobrinho, Diego | Dias, Alexandre C. | Piotrowski, Maurício J. | Wenzel, Wolfgang | Rêgo, Celso Ricardo Caldeira
Source: arXiv (Cornell University) (preprint, 2025-12-06)
Field: Materials Science — Materials Chemistry

GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits Integrated Computational Materials Engineering (ICME), where state-of-the-art codes exist but remain cumbersome for non-experts. We address this bottleneck with GENIUS, an AI-agentic workflow that fuses a smart Quantum ESPRESSO knowledge graph with a tiered hierarchy of large language models supervised by a finite-state error-recovery machine. Here we show that GENIUS translates free-form human-generated prompts into validated input files that run to completion on $\approx$80% of 295 diverse benchmarks, where 76% are autonomously repaired, with success decaying exponentially to a 7% baseline. Compared with LLM-only baselines, GENIUS halves inference costs and virtually eliminates hallucinations. The framework democratizes electronic-structure DFT simulations by intelligently automating protocol generation, validation, and repair, opening large-scale screening and accelerating ICME design loops across academia and industry worldwide. Debugging ; Workflow ; Bottleneck ; Protocol (science) ; Inference ; Automation ; Hierarchy ; Graph Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Soleymanibrojeni, Mohammad | Aydin, Roland | Guedes-Sobrinho, Diego | Dias, Alexandre C. | Piotrowski, Maurício J. | Wenzel, Wolfgang | Rêgo, Celso Ricardo Caldeira
Source: ArXiv.org (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits Integrated Computational Materials Engineering (ICME), where state-of-the-art codes exist but remain cumbersome for non-experts. We address this bottleneck with GENIUS, an AI-agentic workflow that fuses a smart Quantum ESPRESSO knowledge graph with a tiered hierarchy of large language models supervised by a finite-state error-recovery machine. Here we show that GENIUS translates free-form human-generated prompts into validated input files that run to completion on $\approx$80% of 295 diverse benchmarks, where 76% are autonomously repaired, with success decaying exponentially to a 7% baseline. Compared with LLM-only baselines, GENIUS halves inference costs and virtually eliminates hallucinations. The framework democratizes electronic-structure DFT simulations by intelligently automating protocol generation, validation, and repair, opening large-scale screening and accelerating ICME design loops across academia and industry worldwide. Debugging ; Workflow ; Bottleneck ; Protocol (science) ; Inference ; Automation ; Hierarchy ; Graph Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Anthony Carreon | Vikas Sharma | Venkat Raman
Source: ArXiv.org (preprint, 2025-11-27)
Field: Materials Science — Materials Chemistry

Automated Design Optimization via Strategic Search with Large Language Models Traditional optimization methods excel in well-defined search spaces but struggle with design problems where transformations and design parameters are difficult to define. Large language models (LLMs) offer a promising alternative by dynamically interpreting design spaces and leveraging encoded domain knowledge. To this end, we introduce AUTO, an LLM agent framework that treats design optimization as a gradient-free search problem guided by strategic LLM reasoning. The framework employs two collaborative agents: a Strategist that selects between exploration and exploitation strategies, and an Implementor that executes detailed designs. Applied to GPU code optimization -- a domain critical to fields from machine learning to scientific computing -- AUTO generates solutions competitive with expert implementations for chemical kinetics integration and dense matrix multiplication. The framework achieves 50-70% search efficiency relative to Bayesian optimization methodologies. It completes optimizations in approximately 8 hours at an estimated cost of up to \$159 per run, compared to an estimated cost of up to \$480 with median-wage software developers. These findings open the door to automating design optimization in ill-defined search spaces with limited prior information. Bayesian optimization ; Implementation ; Code (set theory) ; Domain (mathematical analysis) ; Software ; Program optimization ; Optimization problem ; Engineering optimization ; Strategist Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Multi-Objective Optimization Algorithms
Authors: Xiaoyu Wang | Guofeng He
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Optimizing the Emulsion Polymerization Reaction Process Using Artificial Intelligence (AI) Agents This study explores the integration of AI-powered agents into the emulsion polymerization process, aiming to reduce experimental iterations, minimize costs, and accelerate industrial scale-up. By leveraging AI-driven optimization algorithms and multi-agent systems, we demonstrate how historical experimental data and real-time process monitoring can be utilized to predict optimal reaction conditions, refine formulations, and dynamically adjust manufacturing parameters. Process (computing) ; Polymerization ; Emulsion polymerization ; Process control ; Emulsion ; Process optimization Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Pouria Dianati Souha
Source: Synthesis and Sintering (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

AI-agent–enhanced knowledge graphs and memory-augmented models as a new paradigm for intelligent sintering systems Sintering processes play a critical role in materials manufacturing; however, their optimization remains highly dependent on empirical knowledge, fragmented datasets, and costly experimental trials. Existing modeling and machine learning approaches often lack a unified structure for representing complex relationships among processing parameters, microstructural evolution, and final material properties. This perspective article argues that knowledge graphs can serve as a missing semantic layer for organizing sintering-related data, enabling structured representation of process–property relationships across heterogeneous databases. Furthermore, the integration of autonomous AI agents equipped with memory-augmented learning models is proposed as a promising direction for continuously constructing, updating, and reasoning over such knowledge graphs. By combining structured knowledge representation with adaptive learning and agent-based optimization, this framework has the potential to transform sintering research into a self-improving, data-driven ecosystem. This perspective highlights future research directions toward intelligent, explainable, and autonomous sintering systems for advanced materials engineering. Perspective (graphical) ; Representation (politics) ; Knowledge representation and reasoning ; Knowledge integration ; Semantic network ; Feature learning ; Artificial neural network ; Knowledge graph Machine Learning in Materials Science ; Iron and Steelmaking Processes ; Advanced materials and composites
Authors: Slautin, Boris N. | Barakati, Kamyar | Liu, Yu | Emery, Reece | Rack, Philip | Kalinin, Sergei V.
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Bayesian Co-Navigation of a Computational Physical Model and AFM Experiment to Autonomously Survey a Combinatorial Materials Library Building autonomous experiment workflows requires transcending beyond the data-driven surrogate models to incorporate and dynamically refine physical theory during exploration. Here we demonstrate the first fully automated experimental realization of Bayesian co-navigation - a framework in which an autonomous agent simultaneously runs a physical experiment and a computationally expensive physical model. Using an automated AFM platform coupled to a kinetic Monte Carlo (kMC) model of thin-film growth, the system infers a set of effective bond energies for the (CrTaWV)x-Mo(1-x) pseudo-binary combinatorial library, progressively adjusting the kMC parameters to decrease the epistemic disparity between simulation and experiment. This real-time theoretical refinement enables the kMC model to capture the behavior of the specific materials system and reveals the mechanistic role of hetero-bonding in governing surface diffusion. Together, these results establish co-navigation as a general strategy for tightly integrating physical models with autonomous experimental platforms to produce interpretable and continually self-correcting theoretical modelling of complex materials systems. Workflow ; Realization (probability) ; Set (abstract data type) ; Physical system ; Bayesian probability ; Kinetic Monte Carlo ; Bayesian optimization ; Monte Carlo method ; Bayesian inference Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Slautin, Boris N. | Barakati, Kamyar | Liu, Yu | Emery, Reece | Rack, Philip | Kalinin, Sergei V.
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Bayesian Co-Navigation of a Computational Physical Model and AFM Experiment to Autonomously Survey a Combinatorial Materials Library Building autonomous experiment workflows requires transcending beyond the data-driven surrogate models to incorporate and dynamically refine physical theory during exploration. Here we demonstrate the first fully automated experimental realization of Bayesian co-navigation - a framework in which an autonomous agent simultaneously runs a physical experiment and a computationally expensive physical model. Using an automated AFM platform coupled to a kinetic Monte Carlo (kMC) model of thin-film growth, the system infers a set of effective bond energies for the (CrTaWV)x-Mo(1-x) pseudo-binary combinatorial library, progressively adjusting the kMC parameters to decrease the epistemic disparity between simulation and experiment. This real-time theoretical refinement enables the kMC model to capture the behavior of the specific materials system and reveals the mechanistic role of hetero-bonding in governing surface diffusion. Together, these results establish co-navigation as a general strategy for tightly integrating physical models with autonomous experimental platforms to produce interpretable and continually self-correcting theoretical modelling of complex materials systems. Workflow ; Realization (probability) ; Set (abstract data type) ; Physical system ; Bayesian probability ; Kinetic Monte Carlo ; Bayesian optimization ; Monte Carlo method ; Bayesian inference Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Aikaterini Vriza | Uma Kornu | Aditya Koneru | Henry Chan | Subramanian K. R. S. Sankaranarayanan
Source: Digital Discovery (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Multi-agentic AI framework for end-to-end atomistic simulations Autonomous multi-agent AI system coordinates specialized agents to perform complex materials property calculations from natural language prompts. Property (philosophy) ; Natural (archaeology) ; Complex system ; Work (physics) ; Expert system Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Scientific Computing and Data Management
Authors: Jinming Hu | Hassan Nawaz | Yuting Rui | Lijie Chi | Arif Ullah | Pavlo O. Dral
Source: ChemRxiv (preprint, 2025-07-23)
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, monitoring their computational status, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. We achieve these goals by exploiting large language models that leverage the versatility of our MLatom ecosystem, supporting AI-enhanced computational chemistry tasks ranging from ground-state to excited-state calculations, including geometry optimizations, thermochemistry, and spectral calculations. The multi-agent implementation enables autonomous executions of the complex computational workflows, such as the computation of the reaction enthalpies. Aitomia is the first intelligent assistant publicly accessible online on a cloud computing platform for atomistic simulations of broad scope (Aitomistic Hub at https://aitomistic.xyz). It may also be deployed locally as described at http://mlatom.com/aitomia. Aitomia is expected to lower the barrier to performing atomistic simulations, thereby democratizing simulations and accelerating research and development in relevant fields. Quantum chemical ; Quantum ; Computer science ; Statistical physics ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Scientific Computing and Data Management