jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Markus Schilling | Harald Bresch | Bernd Bayerlein | Bastian Ruehle
Source: ChemRxiv (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

WeChemSynOntology: Semantic Modeling of Wet Chemical Syntheses in a Self-Driving Lab for Nano-and Advanced Materials Representing experimental procedures in an unambiguous way that can be understood and reproduced by other scientists is at the heart of scientific progress. For centuries, these descriptions were made by humans and for humans, often assuming implicit or tacit knowledge. However, when Materials Acceleration Platforms (MAPs) and Self-Driving Labs (SDLs) are used for the autonomous discovery and optimization of materials, sharing knowledge and workflows that were designed and executed by machines becomes increasingly important. These machines require an explicit, precise and accurate description and modeling of all process parameters and steps that need to be executed. To address these needs, especially in the domain of materials science and nano and advanced materials synthesis, we developed the Wet Chemical Synthesis Ontology, which is based on the Project Material Digital and Basic Formal Ontology. The ontology contains recurring concepts from millions of wet chemical synthesis procedures in the scientific literature. We discuss the design considerations, concepts, and architecture of our ontology in detail, and demonstrate how it can be applied to the construction and querying of semantically annotated knowledge graphs from wet chemical nano and advanced materials synthesis workflows that were previously described and performed on an SDL. Using such formal representations and semantic annotations for describing synthesis procedures and workflows facilitates the reproducibility, sharing, and execution of synthesis procedures across different labs around the world that use different orchestrators for their robotic hardware. Workflow ; Ontology ; Process (computing) ; Domain (mathematical analysis) ; Scientific discovery ; Semantic Web ; Architecture ; Knowledge representation and reasoning Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Jonathan Staaf Scragg
Source: arXiv (Cornell University) (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Exploring self-driving labs for optoelectronic materials Self-driving laboratories (SDLs), by combining automation with machine learning-guided experiment selection, have the potential to transform experimental materials science. To date, most SDLs have been optimisation-driven, designed to rapidly converge on performance metrics, by embedding multiple mechanistic layers within platform-specific surrogate models. Such approaches excel at process tuning yet offer limited insight into the underlying physics governing synthesis-property relationships. Here we articulate a complementary paradigm: the exploration-driven, or scientific, SDL, whose primary purpose is the generation of data for data-driven science. We exemplify this concept for the case of inorganic optoelectronic materials, arguing that defect physics, which forms the central mechanistic link between synthesis conditions and functional properties, provides the foundation for designing a suitable SDL. Because defect populations and their spatial organisation cannot generally be resolved directly - nor fully predicted from first principles - the task of the SDL is to generate datasets in which thermodynamic and kinetic synthesis variables are systematically perturbed and defect-sensitive observables measured in parallel. From this basis, we propose a set of design principles for scientific SDLs that will enable them to operate close to the physics of optoelectronic materials, thereby generating transferrable and reusable datasets offering radical insight. We use Cu2ZnSn(S,Se)4 as a case study, both to show the scale of the task of defect-aware materials exploration as well to highlight as the deficiencies in the current paradigm. We propose that properly designed SDLs can generate the structured datasets necessary to enable mechanistic inference and advance synthesis-aware materials design. Inference ; Task (project management) ; Automation ; Process (computing) ; Set (abstract data type) Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Electronic and Structural Properties of Oxides
Authors: Jonathan Staaf Scragg
Source: arXiv (Cornell University) (preprint, 2026-03-23)
Field: Materials Science — Materials Chemistry

Exploring self-driving labs for optoelectronic materials Self-driving laboratories (SDLs), by combining automation with machine learning-guided experiment selection, have the potential to transform experimental materials science. To date, most SDLs have been optimisation-driven, designed to rapidly converge on performance metrics, by embedding multiple mechanistic layers within platform-specific surrogate models. Such approaches excel at process tuning yet offer limited insight into the underlying physics governing synthesis-property relationships. Here we articulate a complementary paradigm: the exploration-driven, or scientific, SDL, whose primary purpose is the generation of data for data-driven science. We exemplify this concept for the case of inorganic optoelectronic materials, arguing that defect physics, which forms the central mechanistic link between synthesis conditions and functional properties, provides the foundation for designing a suitable SDL. Because defect populations and their spatial organisation cannot generally be resolved directly - nor fully predicted from first principles - the task of the SDL is to generate datasets in which thermodynamic and kinetic synthesis variables are systematically perturbed and defect-sensitive observables measured in parallel. From this basis, we propose a set of design principles for scientific SDLs that will enable them to operate close to the physics of optoelectronic materials, thereby generating transferrable and reusable datasets offering radical insight. We use Cu2ZnSn(S,Se)4 as a case study, both to show the scale of the task of defect-aware materials exploration as well to highlight as the deficiencies in the current paradigm. We propose that properly designed SDLs can generate the structured datasets necessary to enable mechanistic inference and advance synthesis-aware materials design. Inference ; Task (project management) ; Automation ; Process (computing) ; Set (abstract data type) Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Electronic and Structural Properties of Oxides
Authors: Amanda K. Brown | Abhishek Soni | Daniel Lin | Curtis P. Berlinguette
Source: ACS Central Science (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Accelerated Emergence of Self-Driving Laboratories for Accelerating Materials Discovery The rise of self-driving laboratories (SDLs) marks a transformative era in materials science. By leveraging automation and machine learning, SDLs provide unprecedented opportunities to rapidly explore complex chemical landscapes and accelerate discoveries. In this Outlook, we explore the growth of the global SDL ecosystem in materials science, describe recent advances, and discuss how SDLs are poised to drive more discoveries and accelerate commercialization. Automation ; Transformative learning ; Key (lock) ; Scientific discovery Machine Learning in Materials Science ; Micro and Nano Robotics ; Modular Robots and Swarm Intelligence
Authors: Rigoberto Advincula | Jihua Chen
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Chemical Reaction Engineering and Catalysis: AI/ML Workflows and Self-Driving Laboratories Chemical reaction engineering is key to industrial might and sustainable chemistry. This will be enabled using smart, efficient catalysts or catalysis ecosystems. This is possible with advanced artificial intelligence and machine learning (AI/ML) workflows that need to be employed as agentic AI projects. The fundamentals of catalysis need to be emphasized. A strong focus on catalyst design, mechanistic studies, reaction engineering, and scale-up must use ML-driven workflows, along with high-throughput experimentation (HTE) and an autonomous, self-driving laboratory (SDL). Laboratory experience and data-driven approaches are valuable when working together to accelerate this development. Parametrize and create a virtuous circle for data-driven discovery across heterogeneous, homogeneous, and biocatalysts to enable utility in many chemical process industries as agentic AI tasks. This article builds the case for discovery science in catalysis and continuous improvement in chemical reaction engineering with this new ecosystem. Workflow ; Chemical reaction engineering ; Process (computing) ; Key (lock) ; Focus (optics) ; Chemical process Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Jonathan J. S. Scragg
Source: Faraday Discussions (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Exploring self-driving labs for optoelectronic materials Self-driving laboratories (SDLs), by combining automation with machine learning-guided experiment selection, have the potential to transform experimental mateirals science. To date, most SDLs have been optimisation-driven, designed to rapidly converge... Automation Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Modular Robots and Swarm Intelligence
Authors: Anastasiia M. Kutskaia | Konstantin Rodygin
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Infrared spectroscopy-assisted prediction of impurities in chemicals using machine learning: towards smart self-driving laboratories The changeable nature of chemicals requires constant monitoring. In a self-driving laboratory (SDL), manual revision of chemicals should be replaced by automated compliance checks. Infrared spectroscopy is an SDL compatible tool that can be integrated into a robotic arm to record a spectrum for each compound scheduled to be used in a synthetic transformation. In the case of any changes happening with a chemical, the IR-spectrum would include both signals of the main compound and an impurity, resulting in failure in decoding and further synthesis. Here, a series of complicated IR-spectra of mixtures of compounds was generated to train a neural network. The mixtures were identified based on chemical intuition. An <i>F</i><sub>1</sub>-score of 0.94 for purity determination and 0.97 for functional group determination was achieved for the generated spectra. The trained model was tested using IR-spectra of real mixtures of compounds. An <i>F</i><sub>1</sub>-score of 0.99 for functional group determination was achieved for the real spectra and 0.95 for the purity determination task was observed. In fact, the model was able to determine whether a compound was pure enough for further use or contaminated with the identified substance and should be purified. Infrared spectroscopy ; Impurity ; Infrared ; Task (project management) ; Analytical Chemistry (journal) ; Artificial neural network Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications
Authors: Hitoshi Mikami | Azusa Kamiyama | Kohei Kusada | Megumi Mukoyoshi | Hiromasa Kaneko | Masaaki Haneda | Hiroshi Maeno | Tomokazu Yamamoto | Yasukazu Murakami | Hiroshi Kitagawa
Source: Nanoscale Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Post-ageing guided closed-loop discovery of multi-element alloy catalysts for automotive exhaust purification Multi-element alloy catalysts exhibit tunable electronic structures and remarkable thermal stability, making them promising materials for automotive exhaust purification. However, most data-driven explorations have emphasised fresh activity, overlooking the post-ageing durability that governs real-world performance. Here, we have developed a closed-loop high-throughput discovery framework that employs post-ageing activity as the principal design index and integrates inverse analytical prediction to accelerate the development of durable high-entropy alloy catalysts. A total of 1493 catalysts were synthesised and automatically evaluated, yielding over one hundred compositions surpassing Pd benchmarks in low-temperature activity, total conversion, and durability. Mechanistic analyses revealed that the enhanced performance originates from cooperative sites formed between different elements-indicating synergistic adsorption behaviour beyond that of individual metals-and from synthesis conditions involving low temperatures and high alkalinity, which suppress the formation of mixed oxides with alumina and thereby optimise metal-support interactions. Furthermore, multi-component evaluations including low-reactivity hydrocarbons (i-C<sub>5</sub>H<sub>12</sub>) clarified the coupled redox behaviour between NO reduction and hydrocarbon oxidation, realistically reproducing actual TWC operation. Statistical validation demonstrated over twentyfold higher discovery efficiency than random exploration (<i>p</i> < 0.001). This study establishes a durability-aware, data-driven paradigm linking alloy design, process informatics, and machine learning toward practical, platinum-group metal-efficient automotive catalysts. Automotive industry ; Alloy ; Catalysis ; Nickel alloy ; Automotive engine Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Rigoberto Advincula | Jihua Chen
Source:  (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Polymer Membranes and Nanostructured Materials: AI/ML in Advanced Manufacturing, Characterization, and Self-Driving Labs Membrane and separations technology is a driving force for both fundamental and applied research that impacts the economy. Polymer materials are a vast design space that can be further exploited towards high-performance membrane and filtration applications. Machine learning (ML) can be used to improve membrane materials and design using datasets and data analytics with agentic AI tasks from large language models (LLM). The applications of AI/ML workflows include membrane polymer structure-composition-processing-property (SCPP) relationships, membrane testing, and performance validation. Characterization methods to quantify transport, permeance, selectivity, and environmental stability of membranes need to go beyond statistical methods. Generative AI workflows are important for science discovery and rapid feedback. Exploring bio-inspired designs – de novo membrane design, and future directions will depend on how fast we can implement these workflows. By designing and updating research end-to-end, it is possible to leverage the new tools and datasets becoming available to the membrane community. Workflow ; Membrane ; Leverage (statistics) ; Synthetic membrane ; Characterization (materials science) ; Analytics ; Generative grammar Machine Learning in Materials Science ; Membrane Separation Technologies ; Block Copolymer Self-Assembly
Authors: Rigoberto Advincula | Jihua Chen
Source: ArXiv.org (article, 2026-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI/ML-Driven Surface Plasmon Resonance (SPR) and Spectroscopy: Materials Interfaces and Autonomous Experiments This review explores the evolution of Surface Plasmon Resonance (SPR) spectroscopy and sensing, transitioning from fundamental studies of adsorption-desorption kinetics to the sophisticated sensing with Electropolymerized Molecularly Imprinted Polymers (E-MIPs). A significant portion of our previous research focuses on the optical properties, electrochromism of polymer dielectrics, and structure-order correlation in polymer brushes and hierarchical ultrathin films. We then address the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in data interpretation, culminating in the conceptualization of Self-Driving Labs (SDLs). The importance of generating high-quality training data through high-throughput experimentation (THE) with the SPR is a possibility. These autonomous systems represent the future of materials science, enabling the rapid, closed-loop discovery and optimization of next-generation SPR sensors and analytical methods. This overview highlights the trajectory for integrating conventional experimental design with AI-driven sensing and analytical chemistry across materials and biomedical applications. Surface plasmon resonance ; Polymer ; Plasmon ; Electrochromism ; Transformative learning ; Conceptualization ; Surface plasmon ; Spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Polymer Surface Interaction Studies ; Plasmonic and Surface Plasmon Research
Authors: Rigoberto Advincula | Jihua Chen
Source: Open MIND (preprint, 2026-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI/ML-Driven Surface Plasmon Resonance (SPR) and Spectroscopy: Materials Interfaces and Autonomous Experiments This review explores the evolution of Surface Plasmon Resonance (SPR) spectroscopy and sensing, transitioning from fundamental studies of adsorption-desorption kinetics to the sophisticated sensing with Electropolymerized Molecularly Imprinted Polymers (E-MIPs). A significant portion of our previous research focuses on the optical properties, electrochromism of polymer dielectrics, and structure-order correlation in polymer brushes and hierarchical ultrathin films. We then address the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in data interpretation, culminating in the conceptualization of Self-Driving Labs (SDLs). The importance of generating high-quality training data through high-throughput experimentation (THE) with the SPR is a possibility. These autonomous systems represent the future of materials science, enabling the rapid, closed-loop discovery and optimization of next-generation SPR sensors and analytical methods. This overview highlights the trajectory for integrating conventional experimental design with AI-driven sensing and analytical chemistry across materials and biomedical applications. Surface plasmon resonance ; Polymer ; Plasmon ; Electrochromism ; Transformative learning ; Conceptualization ; Surface plasmon ; Spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Polymer Surface Interaction Studies ; Plasmonic and Surface Plasmon Research
Authors: Ankush Kumar Mishra | Jacob Paul Mauthe | Nicholas S. Luke | Aram Amassian | Baskar Ganapathysubramanian
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

InSpecLearn4SDL: interpretable spectral features predict conductivity in self-driving doped conjugated polymer labs To accelerate materials discovery using self-driving labs (SDLs), we present a machine learning pipeline that predicts the electrical conductivity of doped conjugated polymers using rapid, non-destructive optical spectroscopy. Conjugated system ; Conductivity ; Polymer ; Doping ; Pipeline (software) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Nanopore and Nanochannel Transport Studies
Authors: Ming-Chiang Chang | Maximilian Amsler | Duncan R. Sutherland | Sebastian Ament | Katie R. Gann | Lan Zhou | Louisa Smieska | Arthur R. Woll | John M. Gregoire | Carla P. Gomes | R. Bruce van Dover | Michael O. Thompson
Source: ArXiv.org (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning Autonomous experimentation holds the potential to accelerate materials development by combining artificial intelligence (AI) with modular robotic platforms to explore extensive combinatorial chemical and processing spaces. Such self-driving laboratories can not only increase the throughput of repetitive experiments, but also incorporate human domain expertise to drive the search towards user-defined objectives, including improved materials performance metrics. We present an autonomous materials synthesis extension to SARA, the Scientific Autonomous Reasoning Agent, utilizing phase information provided by an automated probabilistic phase labeling algorithm to expedite the search for targeted phase regions. By incorporating human input into an expanded SARA-H (SARA with human-in-the-loop) framework, we enhance the efficiency of the underlying reasoning process. Using synthetic benchmarks, we demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency. We conduct experimental active learning campaigns using robotic processing of thin-film samples of several oxide material systems, including Bi$_2$O$_3$, SnO$_x$, and Bi-Ti-O, using lateral-gradient laser spike annealing to synthesize and kinetically trap metastable phases. We showcase the utility of human-in-the-loop autonomous experimentation for the Bi-Ti-O system, where we identify extensive processing domains that stabilize $δ$-Bi$_2$O$_3$ and Bi$_2$Ti$_2$O$_7$, explore dwell-dependent ternary oxide phase behavior, and provide evidence confirming predictions that cationic substitutional doping of TiO$_2$ with Bi inhibits the unfavorable transformation of the metastable anatase to the ground-state rutile phase. The autonomous methods we have developed enable the discovery of new materials and new understanding of materials synthesis and properties. Modular design ; Probabilistic logic ; Flexibility (engineering) ; Domain (mathematical analysis) ; Autonomous agent ; Robotics ; Phase (matter) ; Automation Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides