jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kiran Vaddi | Huat Thart Chiang | Arthur A. Grey | Zachery R. Wylie | Lilo D. Pozzo
Source: npj Computational Materials (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape Abstract Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform. Workflow ; Characterization (materials science) ; Colloidal gold ; Inverse ; Differentiable function ; Generative model ; Phase (matter) Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Block Copolymer Self-Assembly
Authors: Jihua Chen | Panagiotis Christakopoulos | Ilia N. Ivanov | Rama K. Vasudevan | Rajeev Kumar | Bobby G. Sumpter | Rigoberto C. Advincula
Source: MRS Bulletin (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence/machine learning of polymer materials and discovery chemistry Artificial intelligence (AI) and machine learning (ML) approaches are increasingly applied to various science and engineering areas to innovate with enhanced efficiencies and productivity. This article discusses some of the progress in polymer materials and discovery chemistry facilitated by AI/ML, with a focus on (1) AI/ML for polymers and chemistry, (2) polymerization methods and flow chemistry, (3) three-dimensional printing and AI/ML, (4) self-driving laboratories and workflows, (5) large language models and algorithms, and (6) simulations. AI is playing critical roles for discovery chemistry, including polymer materials Polymer ; Polymerization ; Focus (optics) ; Synthetic polymer ; Science and engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Icíar Monterrubio | Joseba Orive | Maha Ismail | Evaristo Castillo | Javier García | I. Redondo | Jean‐Luc Dauvergne | Amaia Saracibar | Javier Carrasco | Montse Casas‐Cabanas | Marine Reynaud
Source: Chemistry - A European Journal (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Affordable Automated Modules for Lab‐Scale High‐Throughput Synthesis of Inorganic Materials The development of sustainable advanced materials is increasingly driven by the need for sustainable, faster, scalable, and more efficient research workflows. Advancements in computational screening, high-throughput experimentation, and artificial intelligence (AI) are accelerating progress in materials discovery. To fully leverage the benefits of these complementary approaches, the implementation of materials acceleration platforms (MAPs) and self-driving laboratories (SDL) has emerged as a promising strategy. Here, we present the development of a semi-automated station for the lab-scale high-throughput synthesis (HTS) of inorganic materials, as part of the Materials Acceleration and Innovation plaTform for ENergy Applications (MAITENA). The system integrates two in-house-designed liquid-handling modules capable of performing sol-gel, Pechini, solid-state, and hydro/solvothermal syntheses. Each module enables the preparation of several dozen gram-scale samples per week with high reproducibility and minimal manual intervention. The system's capabilities are demonstrated through three case studies involving Li-ion battery materials. Results highlight the module's utilization for efficient screening of compositions and synthesis conditions to vary materials' properties. This accessible and modular infrastructure offers a practical route to implementing high-throughput strategies in inorganic materials research. Workflow ; Modular design ; Computer science ; Scalability ; Throughput ; Leverage (statistics) ; Nanotechnology ; Process engineering ; Embedded system ; Systems engineering ; Materials science ; Engineering ; Operating system ; Database ; Wireless Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Emory M. Chan
Source: Nano Letters (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Automated Nanocrystal Synthesis: Lessons from 25 Years of Robots, Microfluidics, and Machine Learning This perspective highlights the evolution of techniques for automating the synthesis of colloidal nanocrystals. Over the past 25 years, microfluidic reactors and robotic workflows have been developed to enhance the reproducibility of nanocrystal synthesis, facilitate rapid screening of reaction conditions, optimize material properties, and perform multistep syntheses of high-quality nanoparticles with complex heterostructures. Modern automated systems are now valued for their ability to generate robust data sets for validating physical models, supporting chemical mechanisms, training machine learning models, and for directing autonomous experimentation. We discuss the early challenges and limitations of these technologies and present key lessons for effectively utilizing automated and ML-guided tools to accelerate nanocrystal discovery for the next 25 years. Nanocrystal ; Workflow ; Key (lock) ; Automation ; Microfluidics ; Perspective (graphical) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Pickering emulsions and particle stabilization
Authors: Chenxi He | Shinya Mine | Yuan Jing | Jialei Zhang | Junxian Qin | Ningqiang Zhang | Koichiro Taketoshi | Akihiko Anzai | Ryo Toyoshima | Hiroshi Kondoh | Ichigaku Takigawa | Ken‐ichi Shimizu | Takashi Toyao
Source: ChemRxiv (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Data-driven catalyst design for direct catalytic N2O decomposition Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, such as greenhouse gas emissions and ozone layer depletion. However, the identification of efficient catalysts for this reaction remains challenging owing to the limitations of conventional methods. In this study, we employed a machine learning (ML) approach designed to accelerate the discovery of effective direct catalytic N2O decomposition catalysts. Starting with 51 catalysts and conducting 37 cycles of a closed-loop discovery system (ML prediction + experiment), 633 catalysts were experimentally tested. Over 10 multi-elemental catalysts exhibiting superior activity were identified, surpassing the performance of the originally identified best catalyst. Among them, Rh(1)–Pd(2)/ZrO2_EP exhibited the highest catalytic performance for N2O decomposition. Through control experiments and a combination of ex situ and in situ characterizations, we identified the essential role of each component within the catalyst system. Catalysis ; Decomposition ; Chemistry ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Alexander E. Siemenn | Basita Das | Kangyu Ji | Sheng Fang | Tonio Buonassisi
Source: Science Advances (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high throughputs. We demonstrate the performance of this approach by autonomously driving a 4-DOF robotic probe for 24 hours to characterize semiconductor photoconductivity at 3025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs of more than 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing defects. With this self-supervised neural network-driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories. Computer science ; Robot ; Automation ; Throughput ; Artificial intelligence ; Reliability (semiconductor) ; Artificial neural network ; Robotics ; Wireless ; Mechanical engineering ; Engineering ; Power (physics) Electronic and Structural Properties of Oxides ; Industrial Vision Systems and Defect Detection ; Advanced Memory and Neural Computing
Authors: Yahao Dai | Henry Chan | Aikaterini Vriza | Jing‐Yuan Fan | FrederickChando Kim | Yunfei Wang | Wei Liu | Naisong Shan | Zoe Xu | Max Weires | Yukun Wu | Zhiqiang Cao | C. S. Miller | Ralu Divan | Xiaodan Gu | Chenhui Zhu | Sihong Wang | Jie Xu
Source: Nature Chemical Engineering (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Adaptive AI decision interface for autonomous electronic material discovery Adaptability ; Interface (matter) ; Electronics ; Scarcity ; Key (lock) ; Adaptation (eye) ; Capacitance Machine Learning in Materials Science ; Conducting polymers and applications ; Advanced Memory and Neural Computing
Authors: X. H. Mo | Jing Zhang | Yonghui Li | Xiaodong Zhang
Source: Journal of intelligent medicine. (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Advances in machine learning applications for thiolate‐protected metal nanoclusters with potential metallic core exploration Abstract Thiolate‐protected metal nanoclusters (TPMNCs) exhibit tunable physicochemical properties governed by quantum effects related to size, composition, assembly, and surface ligands. Atomically precise synthesis enables researchers to directly correlate nanostructure with material performance. However, slight variations in structure can lead to significant and nonlinear quantum effects, making macroscopic properties unpredictable. Therefore, nanolevel property tuning remained challenging before the advanced development of machine learning (ML). In contrast to traditional nanodesign methods, algorithm development based on data enables ML approaches to capture the nonlinear behaviors and electronic features of nanoclusters by embedding characteristics into a high‐dimensional numerical space, thereby improving the predictability and generative capability for property prediction. TPMNCs are a representative system with atomic precision and distinct optical and catalytic properties. This review explores ML applications in nanocluster research, with a focus on TPMNCs, including synthesis, structure prediction, optical property analysis, and catalytic mechanism discovery. The rapid advancement of ML is propelling progress in this field and paving the way for future directions including active learning, model transferability, autonomous experimentation, and adaptive simulation. Nanoclusters ; Core (optical fiber) ; Metal ; Materials science ; Nanotechnology ; Computer science ; Metallurgy ; Composite material Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Yuan Jing | Chenyang Zhang | Mine Shinya | Xiupeng Zhang | Chenxi He | Ningqiang Zhang | Xu Guo | Akihiko Anzai | Kohei Oka | Ryo Toyoshima | Hiroshi Kondoh | Ichigaku Takigawa | Ken‐ichi Shimizu | Takashi Toyao
Source: Research Square (preprint, 2025-04-01)
Field: Materials Science — Materials Chemistry

Extrapolative-machine-learning-guided discovery of multi-elemental heterogeneous catalysts for low-temperature NO reduction by H2 Reduction (mathematics) ; Catalysis ; Cost reduction ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Economics ; Mathematics ; Organic chemistry Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Machine Learning in Materials Science
Authors: Dilshod Nematov | Iskandar Raufov
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Autonomous Platforms for Nanomaterial Discovery and Optimization: Bridging Simulation, Synthesis, and In Situ Characterization Bridging (networking) ; In situ ; Characterization (materials science) ; Nanotechnology ; Nanomaterials ; Computer science ; Materials science ; Systems engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Sanna Jarl | Jens Sjölund | R.J.W. Frost | Anders Vedel Holst | Jonathan J. S. Scragg
Source: Materials & Design (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system Self-driving labs (SDLs) employing automation and machine learning (ML) offer great promise for accelerating materials discovery and optimisation. However, in thin film science, SDLs are mainly restricted to solution-based methods which are easier to automate, restricting access to the broader chemical space of inorganic materials. This work advances an SDL based on magnetron co-sputtering, addressing a key challenge: rapidly generating accurate composition maps of multi-element, compositionally graded thin films. Traditional ex-situ methods are slow and error-prone; instead, we present a fast, calibration-free, in-situ ML approach to predict the deposition rate using quartz-crystal microbalance (QCM) sensors. For each sputtering source, deposition rates are sequentially learned as a function of pressure and power via active learning with Gaussian processes (GPs). The final GPs are combined with a geometric flux model to interpolate deposition rates across the sample. Among several acquisition functions with random query as the baseline, the Bayesian active learning MacKay (BALM) approach yielded the best performance, requiring as few as 10 experiments per source. The model predictions for co-sputtering composition distributions were validated against external composition measurements. This framework significantly increases throughput in combinatorial sputtering studies and highlights the potential of ML-guided SDLs to surpass traditional Edisonian methods. • Self-driving PVD lab synthesising of combinatorial multi-element thin-films thin-films. • In-situ process modelling to obtain accurate composition maps. • High-throughput machine learning guided experiment design via active learning. • Deposition rate can be learnt in as few as 10 experiments using fully Bayesian models. Bayesian optimization ; Sputter deposition ; Deposition (geology) ; Sputtering ; Gaussian process ; High-power impulse magnetron sputtering ; Bayesian inference ; Thin film Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Block Copolymer Self-Assembly
Authors: Kangyu Ji | Tianran Liu | Sheng Fang | Shaun Tan | Moungi G. Bawendi | Tonio Buonassisi
Source: ArXiv.org (preprint, 2025-09-23)
Field: Materials Science — Materials Chemistry

A closed-loop AI framework for hypothesis-driven and interpretable materials design Scientific hypothesis generation is central to materials discovery, yet current approaches often emphasize either conceptual (idea-to-data) reasoning or data-driven (data-to-idea) analysis, rarely achieving an effective integration of both. Here, we present a generalizable active learning workflow that integrates top-down, theory-driven hypothesis generation, guided by a large language model. This is complemented by bottom-up, data-driven hypothesis testing through a root-cause association study. We demonstrate this approach through the design of equimolar quinary-cation two-dimensional perovskite, a chemically complex system with over 850,000 possible cation combinations. In the top-down component, the large language model drives closed-loop optimization by proposing candidates that are likely to achieve phase purity, leveraging domain knowledge and chain-of-thought reasoning. With each iteration, the model identifies an increasing number of near phase-pure compositions, sampling less than 0.004% of the design space. In parallel, the bottom-up association study identifies molecular features with statistically significant influences on phase purity. The integration of these approaches enables the convergence of conceptual and statistical hypotheses, leading to generalizable and rational design rules for phase-pure quinary-cation two-dimensional perovskites. As a proof of concept, we applied the optimized phase-pure quinary-cation two-dimensional perovskite film as a surface capping layer in perovskite solar cells, achieving good performance and stability. Our framework enables the development of interpretable and generalizable design rules that are applicable to a wide range of optimization processes within complex design spaces, providing a foundational strategy for rational, scalable, and efficient materials discovery. Workflow ; Conceptual design ; Domain (mathematical analysis) ; Range (aeronautics) ; Association (psychology) ; Phase (matter) ; Design of experiments ; Complex system Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Jing Gao | Junhan Chang | Haohui Que | Yanfei Xiong | S. C. Zhang | Xianwei Qi | Zhen Liu | Jun-Jie Wang | Qianjun Ding | Xinyu Li | Ziwei Pan | Qiming Xie | Zhuang Yan | Junchi Yan | Linfeng Zhang
Source: arXiv (Cornell University) (preprint, 2025-12-25)
Field: Materials Science — Materials Chemistry

UniLabOS: An AI-Native Operating System for Autonomous Laboratories Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separates high-level planning from low-level execution. Here we present UniLabOS, an AI-native operating system for autonomous laboratories that bridges digital decision-making and embodied experimentation through typed, stateful abstractions and transactional safeguards. UniLabOS unifies laboratory elements via an Action/Resource/Action&amp;Resource (A/R/A&amp;R) model, represents laboratory structure with a dual-topology of logical ownership and physical connectivity, and reconciles digital state with material motion using a transactional CRUTD protocol. Built on a distributed edge-cloud architecture with decentralized discovery, UniLabOS enables protocol mobility across reconfigurable topologies while supporting human-in-the-loop governance. We demonstrate the system in four real-world settings -- a liquid-handling workstation, a modular organic synthesis platform, a distributed electrolyte foundry, and a decentralized computation-intensive closed-loop system -- showing robust orchestration across heterogeneous instruments and multi-node coordination. UniLabOS establishes a scalable foundation for agent-ready, reproducible, and provenance-aware autonomous experimentation. Scalability ; Orchestration ; Modular design ; Stateful firewall ; Software ; Modularity (biology) ; Protocol (science) ; Interface (matter) ; Network topology Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Scientific Computing and Data Management
Authors: H. J. Yang | Ruoyu Yin | Chi Jiang | Yuepeng Hu | Xiaokai Zhu | Xingjian Hu | S. Ananda Kumar | Samantha K. Holmes | Xinghuan Wang | Xiaohua Zhai | Keran Rong | Yaguang Zhu | Tianyi Zhang | Zongyou Yin | Yuan Cao | Haoning Tang | Aaron D. Franklin | Jing Kong | Neil Zhenqiang Gong | Zhichu Ren | Haozhe Wang
Source: ACS Nano (article, 2025-10-02)
Field: Materials Science — Materials Chemistry

Zero-Shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials Characterization of atomic-scale materials traditionally requires human experts with months to years of specialized training. Even for trained human operators, accurate and reliable characterization remains challenging when examining newly discovered materials such as two-dimensional (2D) structures. This bottleneck drives demand for fully autonomous experimentation systems capable of comprehending research objectives without requiring large training data sets. In this work, we present ATOMIC (Autonomous Technology for Optical Microscopy & Intelligent Characterization), an end-to-end framework that integrates foundation models to enable fully autonomous, zero-shot characterization of 2D materials. Our system integrates the vision foundation model (i.e., Segment Anything Model), large language models (i.e., ChatGPT), unsupervised clustering, and topological analysis to automate microscope control, sample scanning, image segmentation, and intelligent analysis through prompt engineering, eliminating the need for additional training. When analyzing typical MoS<sub>2</sub> samples, our approach achieves 99.7% segmentation accuracy for single layer identification, which is equivalent to that of human experts. In addition, the integrated model is able to detect grain boundary slits that are challenging to identify with human eyes. Furthermore, the system retains robust accuracy despite variable conditions, including defocus, color-temperature fluctuations, and exposure variations. It is applicable to a broad spectrum of common 2D materials─including graphene, MoS<sub>2</sub>, WSe<sub>2</sub>, SnSe─regardless of whether they were fabricated via top-down or bottom-up methods. This work represents the implementation of foundation models to achieve autonomous analysis, providing a scalable and data-efficient characterization paradigm that transforms the approach to nanoscale materials research. Characterization (materials science) ; Bottleneck ; Scalability ; Segmentation ; Foundation (evidence) ; Microscopy ; Nanoscopic scale ; Automation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Yadong Jin | Yiting Zhou | Zhuo Xu | Zhongmin Jin | Huan Meng | Suping Li | Liang Yan | Hui Wang | Jia‐Jia Zheng | Xingfa Gao | Yuliang Zhao
Source: Nano Today (article, 2025-10-28)
Field: Materials Science — Biomaterials

Silico-driven drug discovery: A paradigm shift for nanomedicine science and industry Paradigm shift ; Nanomedicine ; Transformative learning ; Drug discovery ; Automation ; Multidisciplinary approach ; Drug development Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Nanotechnology research and applications