jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuan Chiang | E. R. Hsieh | Chia-Hong Chou | Janosh Riebesell
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval Materials science research requires multi-step reasoning and precise material informatics retrieval, where minor errors can propagate into significant failures in downstream experiments.Despite their general success, Large Language Models (LLMs) often struggle with hallucinations, handling domain-specific data effectively (e.g., crystal structures), and integrating experimental workflows.To address these challenges, we introduce LLaMP, a hierarchical multi-agent framework designed to emulate the materials science research workflow.The high-level supervisor agent decomposes user requests into sub-tasks and coordinates with specialized assistant agents.These assistant agents handle domain-specific tasks, such as retrieving and processing data from the Materials Project (MP) or conducting simulations as needed.This pipeline facilitates iterative refinement of material property retrieval and enables the simulation of real-world research workflows.To ensure reliability, we propose a novel metric combining uncertainty and confidence estimate to evaluate the selfconsistency of responses from LLaMP and baseline methods.Our experiments demonstrate LLaMP's superior performance in material property retrieval, crystal structure editing, and annealing molecular dynamics simulations using pre-trained interatomic potentials.Unlike prior work focused solely on material property prediction or discovery, LLaMP serves as a foundation for autonomous materials research by combining grounded informatics and enabling iterative experimental processes.Code and live demo are available at https://github.com/chiang-yuan/llamp. Language model ; Language identification ; Knowledge base ; Natural language ; Universal Networking Language ; Knowledge-based systems ; Knowledge representation and reasoning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Jinglan Zhang | Xinyi Chen | Ye Xu | Yulin Yang | Bin Ai
Source: Advanced Intelligent Discovery (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook Large language models (LLMs) are creating a new paradigm for materials science by transforming textual insights into experimental findings. Leveraging their strengths in natural language understanding, multimodal alignment, and few‐shot reasoning, LLMs already show potential in property prediction, synthesis planning, and uncertainty quantification. This perspective highlights four key roles, Oracle, Surrogate, Quant, and Arbiter, to systematize recent advancements of LLMs in knowledge extraction, property inference, risk assessment, and decision‐making. Experience suggests that true value arises from integrating these capabilities into a verifiable, traceable loop rather than merely scaling model size. However, LLMs still face challenges due to data heterogeneity, limited interpretability, hallucination control, and misalignment with scientific tasks. To address these issues, we propose three forward‐looking directions: developing domain‐adapted foundation models infused with materials science context, establishing a standardized cross‐modal data infrastructure, and incorporating expert feedback alongside robotic automated experimentation into a fully traceable research loop. Through enhanced human–AI collaboration and methodological innovation, LLMs can transform from general‐purpose language tools into scientifically aware partners, advancing materials discovery toward a more efficient, interpretable, and sustainable future. Engineering ethics ; Computer science ; Political science ; Management science ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Topic Modeling
Authors: Boris N. Slautin | Yongtao Liu | Kamyar Barakati | Yu Liu | Reece Emery | Seungbum Hong | Astita Dubey | Vladimir V. Shvartsman | Doru C. Lupascu | Sheryl L. Sanchez | Mahshid Ahmadi | Yunseok Kim | Evgheni Strelcov | Keith A. Brown | Philip D. Rack | Sergei V. Kalinin
Source: Applied Physics Reviews (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Materials discovery in combinatorial and high-throughput synthesis and processing: A new Frontier for SPM For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe the structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization. Frontier ; Throughput ; Computer science ; Nanotechnology ; Materials science ; History ; Archaeology ; Telecommunications Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Force Microscopy Techniques and Applications
Authors: Lei Wang | Yan Chen | Xueru Wang | Xiaobin Deng | Yilun Liu | Xi Chen | Jun Zhang | Yunwei Zhang | Hang Xiao
Source: Research Square (preprint, 2025-09-05)
Field: Materials Science — Materials Chemistry

MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials Inverse ; Generative grammar ; Transformer ; Solid-state ; Property (philosophy) ; Computer science ; Mathematics ; Physics ; Electrical engineering ; Engineering ; Artificial intelligence ; Engineering physics ; Philosophy ; Geometry ; Voltage Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Ming-Chiang Chang | Sebastian Ament | Maximilian Amsler | Duncan R. Sutherland | Lan Zhou | John M. Gregoire | Carla P. Gomes | R. B. van Dover | Michael O. Thompson
Source: npj Computational Materials (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

Probabilistic phase labeling and lattice refinement for autonomous materials research X-ray diffraction (XRD) is a powerful method for determining a material’s crystal structure in high-throughput experimentation, and is widely being incorporated in artificially intelligent agents for autonomous scientific discovery. However, rapid, automated, and reliable analysis of XRD data at rates that match the pace of experimental measurements at a synchrotron source remains a major challenge. To address these issues, we developed CrystalShift for rapid and efficient probabilistic XRD phase labeling employing symmetry-constrained optimization, best-first tree search, and Bayesian model comparison. The algorithm estimates probabilities for phase combinations without requiring additional phase space information or training. We demonstrate that CrystalShift provides robust probability estimates, outperforming existing methods on synthetic and experimental datasets, and can be readily integrated into high-throughput experimental workflows. In addition to efficient phase labeling, CrystalShift offers quantitative insights into materials’ structural parameters, which facilitate both expert evaluation and AI-based modeling of the phase space, ultimately accelerating materials identification and discovery. Lattice (music) ; Probabilistic logic ; Materials science ; Computer science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Yong Xie | Kexin He | Andrés Castellanos-Gómez
Source: Small Structures (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a single‐pixel camera or a scanning photocurrent microscope, it is demonstrated how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization. Building on this capability, it is further illustrated how LLM‐assisted tools can be used to develop autonomous agents capable of independently operating laboratory instruments. This approach underscores the transformative role of LLM‐based tools in democratizing laboratory automation and accelerating scientific progress. Instrumentation (computer programming) ; Scripting language ; Automation ; Transformative learning ; Computer science ; Personalization ; Control (management) ; Visual programming language ; Systems engineering ; Software engineering ; Engineering ; Artificial intelligence ; Programming language ; World Wide Web ; Mechanical engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Ferroelectric and Negative Capacitance Devices
Authors: Costanza Fiorentino | Paola D’Antonio | Francesco Toscano | Nicola Capece | Luís Alcino Conceição | Emanuele Scalcione | Felice Modugno | Maura Sannino | Roberto Colonna | Emilia Lacetra | Giovanni Di Mambro
Source: Sustainability (article, 2025-02-18)
Field: Chemistry — Analytical Chemistry

Smart Sensors and Artificial Intelligence Driven Alert System for Optimizing Red Peppers Drying in Southern Italy The Senise red pepper, known as peperone crusco, is a protected geographical indication (PGI) product from Basilicata, Italy, traditionally consumed dried. Producers use semi-open greenhouses to meet PGI standards, but significant losses are caused by rot from microorganisms thriving in high moisture, temperature, and humidity, which also encourage pest infestations. To minimize losses, a low-cost alert system was developed. The study, conducted in summer 2022 and 2023, used external parameters from the ALSIA Senise weather station and internal sensors monitoring the air temperature and humidity inside the greenhouse. Since rot is complex and difficult to model, an artificial intelligence (AI)-based approach was adopted. A feed forward neural network (FFNN) estimated greenhouse climate conditions as if it were empty, comparing them with actual values when peppers were present. This revealed the most critical period was the first 3–4 days after introduction and identified a critical air relative humidity threshold. The system could also predict microclimatic parameters inside the greenhouse with red peppers, issuing warnings one hour before risk conditions arose. In 2023, it was tested by comparing predicted values with previously identified thresholds. When critical levels were exceeded, greenhouse operators were alerted to adjust conditions. In 2023, pepper rot decreased. Environmental science ; Agricultural engineering ; Computer science ; Artificial intelligence ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Andre K. Y. Low | Jayce Jian Wei Cheng | Kedar Hippalgaonkar | Leonard W. T. Ng
Source: ACS Omega (review, 2025-06-03)
Field: Materials Science — Materials Chemistry

Self-Driving Laboratories: Translating Materials Science from Laboratory to Factory The field of materials science stands at a critical inflection point. While laboratory innovations continue to emerge at an unprecedented pace, the traditional timeline from discovery to market in 10-20 years has become an unacceptable bottleneck in addressing urgent technological challenges. We argue that self-driving laboratories (SDLs) represent not merely another step in automation, but a fundamental reimagining of the materials development pipeline. By integrating manufacturing constraints and scalability considerations from the earliest stages of discovery, SDLs can collapse the laboratory-to-factory timeline while improving reproducibility and success rates. This requires abandoning the traditional sequential approach of materials screening, device optimization and manufacturing scale-up; in favor of concurrent cross-scale development. Here, we critically examine current SDL implementations, challenge prevailing assumptions about automation in materials science, and propose a roadmap for truly integrated materials development platforms that could revolutionize how we translate laboratory discoveries into commercial products. Factory (object-oriented programming) ; Engineering ; Environmental science ; Computer science Machine Learning in Materials Science
Authors: Albertus D. Handoko | Riko I Made
Source: World Scientific Annual Review of Functional Materials (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Generative Models for Materials Discovery: A Review High-throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly towards the artificial intelligence (AI) driven approach, realizing the ‘inverse design’ capabilities that allow the discovery of new materials given the desired properties. This review aims to discuss different principles of AI-driven generative models that are applicable for materials discovery, including different materials representations available for this purpose. We will also highlight specific applications of generative models in designing new catalysts, semiconductors, polymers, or crystals while addressing challenges such as data scarcity, computational cost, interpretability, synthesizability, and dataset biases. Emerging approaches to overcome limitations and integrate AI with experimental workflows will be discussed, including multimodal models, physics-informed architectures, and closed-loop discovery systems. This review aims to provide insights for researchers aiming to harness AIs transformative potential in accelerating materials discovery for sustainability, healthcare, and energy innovation. Generative grammar ; Download ; Computer science ; Generative model ; Data science ; Scientific discovery ; Library science ; Artificial intelligence ; World Wide Web ; Cognitive science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Cristopher Tinajero | Marcileia Zanatta | Julián E. Sánchez‐Velandia | Eduardo Garcı́a-Verdugo | Víctor Sans
Source: Nature Communications (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Reac-Discovery: an artificial intelligence–driven platform for continuous-flow catalytic reactor discovery and optimization Digital technologies, including artificial intelligence and additive manufacturing, have revolutionized chemistry and chemical engineering. In reactor engineering, performance improvements have been enabled by novel geometries, yet design approaches have traditionally relied on human input. This study introduces Reac-Discovery, a digital platform that integrates catalytic reactor design, fabrication, and optimization based on periodic open-cell structures (POCs). It combines the parametric design and analysis of advanced structures from mathematic models (Reac-Gen), high-resolution 3D printing and functionalization of catalytic reactors (Reac-Fab) with an algorithm validating the printability of reactor designs and a self-driving laboratory (Reac-Eval), capable of parallel multi-reactor evaluations featuring real-time nuclear magnetic resonance (NMR) monitoring and machine learning (ML) optimization of process parameters and topological descriptors. Two multiphase catalytic reactions-the hydrogenation of acetophenone and the CO₂ cycloaddition-were selected as case studies, where Reac-Discovery achieved the highest reported space-time yield (STY) for a triphasic CO₂ cycloaddition using immobilized catalysts. Process (computing) ; Yield (engineering) ; Catalysis ; Reactor design ; Parametric statistics ; Acetophenone ; Chemical reaction engineering ; Chemical reactor ; Minification Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalytic Processes in Materials Science
Authors: Jin Qian | Asmita Jana | Siddarth Menon | Andrew Bogdan | Rebecca Hamlyn | Johannes Mahl | Ethan J. Crumlin
Source: Nature Computational Science (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag-H<sub>2</sub>O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner. Surface (topology) ; Materials science ; Mathematics ; Geometry Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Catalysis and Oxidation Reactions
Authors: Qihang Bian | Xiangfu Wang
Source: Nanomaterials (review, 2025-09-30)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Design of Fluorescent Materials: Principles, Methodologies, and Future Directions Dual-mode fluorescent materials are vital in bioimaging, sensing, displays, and lighting, owing to their efficient emission of visible or near-infrared light. Traditional optimization methods, including empirical experiments and quantum chemical computations, suffer from high costs, high labor intensities, and difficulties capturing complex relationships among molecular structures, synthesis parameters, and key photophysical properties. In this review, fundamental principles, key methodologies, and representative applications of machine learning (ML) in predicting fluorescent material performance are systematically summarized. The core ML techniques covered include supervised regression, neural networks, and physics-informed hybrid frameworks. The representative fluorescent materials analyzed encompass aggregation-induced emission (AIE) luminogens, thermally activated delayed fluorescence (TADF) emitters, quantum dots, carbon dots, perovskites, and inorganic phosphors. This review details the modeling approaches and typical workflows-such as data preprocessing, descriptor selection, and model validation-and highlights algorithmic optimization strategies such as data augmentation, physical constraints embedding, and transfer learning. Finally, prevailing challenges, including limited high-quality data availability, weak model interpretability, and insufficient model transferability, are discussed. Fluorescence ; Key (lock) ; Core (optical fiber) ; Artificial neural network ; Quantum ; Data modeling ; Experimental data ; Aggregation-induced emission Luminescence and Fluorescent Materials ; Conducting polymers and applications ; Machine Learning in Materials Science
Authors: Gyan Prakash | Devendra Pandey | Shivbilas Maurya | Virendra N. Pandey
Source: European Journal of Nutrition & Food Safety (article, 2025-08-18)
Field: Materials Science — Biomaterials

Smart and Sustainable: Review of AI-Driven Approaches in Biodegradable Food Packaging Systems Biodegradable materials for food preservation are becoming more popular as a result of the growing need for environmentally friendly and sustainable packaging solutions. However, because biopolymers have a wide range of characteristics and behaviors, improving the sustainability, safety, and performance of such materials is still a difficult task. One promising way to speed up research and innovation in this field is through artificial intelligence (AI). The use of AI in the creation, formulation, and improvement of biodegradable packaging systems is examined in this review. Predicting material properties, designing polymer blends in reverse, integrating sensors into smart packaging, and automated quality control are some of the main uses. Case studies demonstrating effective applications of machine learning and deep learning methodologies in actual biodegradable packaging situations are also covered in the review. Limitations are also critically analysed, including regulatory barriers, model interpretability, and data scarcity. The study concludes by outlining potential future developments, highlighting how AI-driven technologies, digital twins, and autonomous labs could revolutionise the sustainable packaging sector. This review offers a thorough grasp of how AI can facilitate the shift to intelligent, secure, and environmentally friendly food packaging systems by highlighting recent developments and potential future paths. Food packaging ; Active packaging ; Business ; Risk analysis (engineering) ; Biochemical engineering ; Engineering ; Mechanical engineering Nanocomposite Films for Food Packaging