jablonkagroup/rise_ai_scientists huggingface dataset:
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 (Cornell University) (preprint, 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
Authors: Zuyang Ye | Young-Jae Ryu | Shuan Cheng | Kshithij Mysore Nandishwara | Kai Xiang Chong | Aditya Prasad | Hardik Jain | Artin Tajdini | Kevin Jamieson | Shijing Sun
Source: ChemRxiv (article, 2026-02-23)
Field: Chemistry — Inorganic Chemistry

A reaction-aware autonomous platform co-optimizing formulation and execution for the synthesis of zeolitic imidazolate framework Autonomous experimentation is transforming materials discovery, yet most platforms rely on endof-run measurements, ignoring reaction-progress information and imposing rigid execution protocols. Here, we present Axo, a reaction-aware batch autonomous platform that integrates timeresolved in situ optical monitoring into liquid-handling synthesis. Each run is recorded as a reaction trajectory and converted into two kinetic descriptors: the extent of reaction that adapts measurement interval and batch size to match the reaction timescale, and the nucleation rate constant (𝑘𝑛) that guides the Bayesian optimization of formulation variables. Applied to the solution-phase crystallization of zeolitic imidazolate framework-67 (ZIF-67, Axo identified a rapid-nucleation region with 𝑘𝑛 = 2.33 min-1, exceeding the best condition found by static maximin sampling by over five-fold. This work demonstrates how programmable in situ trajectories can be converted into actionable feedback that enables reaction-aware closed-loop optimization to cooptimize what to test and how to measure in solution-phase materials synthesis. Zeolitic imidazolate framework ; Measure (data warehouse) ; Trajectory ; Work (physics) ; Bayesian optimization ; Sampling (signal processing) ; Interval (graph theory) ; Constant (computer programming) Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Ashok Alishala | B. Ramesh
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Integrating Artificial Intelligence into Chemical Synthesis: Advances, Applications, and Challenges Abstract Artificial Intelligence (AI) is redefining the landscape of chemical synthesis by introducing data-driven models for retrosynthetic planning, reaction prediction, optimization, and automated experimentation. With recent advancements in machine learning (ML), graph neural networks (GNNs), large language models (LLMs), and self-driving laboratories (SDLs), chemical discovery is becoming faster, greener, and more reproducible. This paper reviews current progress (2023–2026) in AI-assisted synthesis, highlighting recent breakthroughs, case studies, and challenges of integrating computational intelligence with experimental chemistry. Artificial neural network ; Applications of artificial intelligence ; Graph ; Retrosynthetic analysis ; Expert system Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Rigoberto Advincula | Jihua Chen
Source:  (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Polyelectrolytes and Water-Soluble Polymers: AI/ML-Driven Materials and Applications Water-soluble polymers are a class of macromolecules that includes natural, synthetic, and polyelectrolyte polymers. Their study is fascinating, and increasingly, new applications are being discovered from membranes to drug delivery. How do we apply artificial intelligence and machine learning (AI/ML) to this class of polymers? An AI/ML-polymer redesign and the application of large language models (LLMs) for agentic AI tasks can transform these macromolecules into libraries and workflows that can answer some of the most interesting questions in microstructure, phase stability, ion transport, and self-assembly, furthering discovery science and new technologies. This article examines the background of natural polymers, water-soluble polymers, polyelectrolytes, polyelectrolyte complexes (PEC), and polyelectrolyte multilayers (PEM). Active ML and creative materials design by generative AI will create opportunities for new structure-composition-processing-property (SCPP) correlations. Methods for scaling synthesis using continuous-flow chemistry (CFC) in autonomous, self-driving labs (SDL) will be examined for parametrization and optimization in reaction engineering. Finally, 3D printing, an advanced manufacturing method, will be reviewed, with a focus on these materials. Perspectives on the future impact of this approach will generate high interest in accelerating research and scientific discovery. Polyelectrolyte ; Workflow ; Generative grammar ; Class (philosophy) ; Focus (optics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer Surface Interaction Studies
Authors: Konstantinos D. Vogiatzis | Clémence Corminboeuf | Ainara Nova | Kjell Jorner | Johannes Kästner | Markus Meuwly | Philippe Schwaller | Victor Böttcher | Maria Drosou | Edvin Fako | Hannes Hoppe | Žarko Ivković | Nestor M. Iwanojko | Dimitrios A. Pantazis | Stefan P. Schmid | Kalman Szenes | Auguste Tetenoire | Markus Reiher
Source: Journal of the American Chemical Society (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence Artificial intelligence (AI) and machine learning (ML) are rapidly reshaping the landscape of computational chemistry, offering new opportunities for accelerating catalyst discovery and deepening our understanding of chemical reactivity. This perspective highlights emerging methodologies ranging from machine learning potentials and reinforcement learning to generative AI and large language models that are poised to transform computational catalysis. We discuss challenges in developing robust molecular representations for transition-metal complexes, bridging mechanistic understanding with AI-driven predictions, and constructing reliable data sets that capture both successful and failed reactivity outcomes. By drawing on the authors' practical experience across computational, experimental, and AI-driven domains, we emphasize the importance of integrating chemical intuition and methodological expertise with data-driven approaches while remaining open to serendipitous discoveries enabled by automation and self-driving laboratories. Ultimately, the future of computational catalysis lies in balancing human intuition with algorithmic power, leveraging AI not as a replacement but as an accelerator of chemical insight, mechanistic understanding, and catalyst design. Intuition ; Boosting (machine learning) ; Computational model ; Bridging (networking) ; Automation ; Generative grammar Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Scientific Computing and Data Management
Authors: Hyun Suk Park | Mahdi Mazaheri | Changhyeok Choi | Han Hao | Davide Avagliano | Eric S. Isbrandt | Buddhika S. B. Karunathilaka | Chihaya Adachi | Alán Aspuru-Guzik
Source: Nature Communications (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Discovery of tunable and soluble organic emitters for solid-state lasers with a self-driving laboratory We have recently demonstrated the ability of using self-driving laboratories for AI-driven searches of organic emitters for solid-state lasing devices. Our past workflow featured solubility challenges for such large molecular moieties. In this next-generation study, we return to the drawing board to explore a family of compounds that are much solution processable and composed of a set of electronic cores that provide a broader color response. Out of 252 potential candidates, and with guidance from DFT calculations, we selectively perform a comprehensive study exploring 51 fluorene-based A-B-A-type organic laser oligomers, armed with our self-driving lab. The candidates range from simple hydrocarbon molecules to complex heteroatom-mixed molecules. As a result of this study, we highlight diketopyrrolopyrrole and benzodiazole derivatives for their largely red-shifted emissions. Furthermore, we investigate the effect of color change arising from heteroatom permutation, fluorine addition, thiophene coupling, and a combination of fluorine addition and thiophene coupling. Amplified spontaneous emission (ASE) measurements in the solid state further corroborate the lasing potential of selected candidates, reinforcing their suitability for future device applications. The computational study with density functional theory confirms the experimental results. Lasing threshold ; Heteroatom ; Thiophene ; Laser ; Density functional theory ; Fluorine ; Molecule Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics ; Strong Light-Matter Interactions
Authors: Mohammad Mazedul Islam | T. M. A. Iqbal Bin Belal | Md. Arif Hossain Chowdhury Anik | Ahmed Sharif
Source: Archives of Computational Methods in Engineering (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Predicting Materials Using Variational Autoencoders: A Systematic Literature Review Property (philosophy) ; Timeline ; Key (lock) ; Materials informatics ; Representation (politics) ; Systematic review ; Domain (mathematical analysis) ; Tree (set theory) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Joanna Przybysz | Ken J. Jenewein | Serhiy Cherevko
Source: ACS electrochemistry. (article, 2026-03-13)
Field: Chemistry — Electrochemistry

High-Throughput Electrochemistry Using Scanning Electrochemical Cells The surge in interest surrounding accelerated materials discovery, coupled with growing efforts to implement the self-driving lab concept in electrochemistry research, highlights the importance of providing a comprehensive overview of the methods that can be integrated into such workflows. These span synthesis, characterization, and beyond. Since electrochemical characterization often constitutes the bottleneck in electrocatalyst, battery, and corrosion discovery workflows, we highlight highly versatile and high-throughput screening tools─namely, scanning droplet cells and scanning flow cells, which show great promise for use in autonomous research due to the ease of automation and coupling with other characterization techniques. However, designing an effective workflow around such an electrochemical screening step requires an informed choice of the material library preparation method. Thus, we present an overview of available high-throughput electrode preparation tools, including those that have not yet been demonstrated for electrocatalysts but show promise. Additionally, we distinguish between methods that allow for a seamless progression of workflow steps (direct methods) and those that require additional effort for electrode preparation (indirect methods). An overview of electrochemical screening approaches, as well as strategies to accelerate physicochemical characterization, is also provided, completing the picture of the high-throughput toolbox for workflow design. Finally, we showcase examples of electrochemical scanning methods incorporated into highly automated and autonomous electrochemical platforms, highlighting their promising functionality in the advent of fully automated and AI-guided research. Workflow ; Characterization (materials science) ; Bottleneck ; Scanning electrochemical microscopy ; Automation ; Electrochemistry ; Electrochemical cell Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: James A. Warren | Francesca Tavazza | Austin McDannald | A. Gilad Kusne | Howie Joress | David P. Hoogerheide | Brian DeCost | Kamal Choudhary | Debra J. Audus
Source: Annual Review of Materials Research (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Overcoming Roadblocks for Implementing AI/ML Methods for Materials Advancement The development of novel materials with tailored properties is a complex, multiobjective optimization problem that has long been a challenge in materials research. The integration of artificial intelligence (AI) and machine learning (ML) techniques has shown great promise in accelerating materials discovery, design, and development by uncovering hidden correlations between processing, structure, and properties. Autonomous experimentation platforms, also known as self-driving laboratories (SDLs), have emerged as a powerful tool in this endeavor, enabling the rapid and efficient acquisition of critical data through a closed-loop feedback process. In this review, we explore the applications of AI/ML techniques to materials research and development through the lens of SDLs and examine the challenges and opportunities associated with the development and deployment of SDLs. We provide a detailed analysis of the components of an SDL, including AI-driven decision-making, experimental data generation, and knowledge representation, and discuss the current barriers to industrial adoption. Software deployment ; Development (topology) ; Data acquisition ; Incremental learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Rigoberto C. Advincula | Jihua Chen
Source: Open MIND (preprint, 2026-02-16)
Field: Materials Science — Surfaces, Coatings and Films

Polymer Brushes and Grafted Polymers: AI/ML-Driven Synthesis, Simulation, and Characterization towards autonomous SDL Polymer brushes and grafted polymers have attracted significant interest at the intersection of polymers, interfacial chemistry, colloidal science, and nanostructuring. The confinement of high-density grafted polymers and differences in swelling regimes govern the synthetic challenges and the interesting physics underlying their macromolecular dynamics. In this article, we focus on another intersection, artificial intelligence and machine learning (AI/ML), and how workflows will enhance the microstructure and composition of these systems. It will also accelerate potential applications through high-throughput experimentation (HTE) and data-driven intelligence, enabling scientific discovery and optimization. Applications in microfluidics, sensors, bioimplants, drug delivery, and related areas may yet offer more opportunities for ML-driven optimization. There is also interest in applying these studies with self-driving laboratories (SDLs) that can leverage autonomous systems for synthesis screening, characterization, and application evaluation. Characterization (materials science) ; Polymer ; Leverage (statistics) ; Workflow ; Intersection (aeronautics) ; Focus (optics) Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications ; Advanced Polymer Synthesis and Characterization
Authors: Ifeanyi. C Emeto | Enefiok Etuk | P.B Gbaranwi | I.H. Ezeh | C.A Okoloegbo | A.A. Galadima | Emmanuel. C. Ochuba
Source: International journal of research and scientific innovation (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Ai-Driven High Throughput Screening (HTC) Approaches to Overcoming the Challenges of Electrocatalysis for Hydrogen Evolution Reaction (HER): A Review The urgent need for sustainable hydrogen production has intensified research into efficient electrocatalysts for the hydrogen evolution reaction (HER), yet challenges like the high cost of platinum-group metals (PGMs) and catalyst degradation persist. This review explores how AI-driven high-throughput screening (HTS) accelerates the discovery of low-cost, durable HER electrocatalysts by bridging computational predictions and experimental validation. We analyze recent advancements where machine learning (ML) models—trained on density functional theory (DFT) datasets and experimental metrics—predict key descriptors (e.g., ΔG_H, d-band center) to identify non-precious alternatives like Ni₃Mo (ΔG_H ≈ 0.08 eV) and CoMoS₄ (overpotential = 32 mV). Autonomous laboratories equipped with robotic synthesis platforms further expedite material testing, exemplified by the discovery of La₀.₅Sr₀.₅CoO₃ via a self-driving lab that screened 1,200 perovskites. Despite progress, limitations such as data scarcity and the "black box" nature of ML hinder broader adoption. We highlight strategies to enhance interpretability, including explainable AI (XAI) techniques like SHAP values, which reveal atomic-level insights (e.g., pyrrolic-N dopants in Fe-N₄ SACs). Multi-objective optimization (MOO) frameworks balance activity, stability, and cost, while active learning loops refine predictions iteratively. Challenges like overfitting (RMSE &gt; 0.2 eV for small datasets) and synthesis bottlenecks for complex morphologies are critically evaluated. The review concludes with recommendations: open-access databases for standardized HER data, physics-informed ML to integrate mechanistic equations, and operando characterization to capture dynamic catalyst behavior. By addressing these gaps, AI-HTS can unlock scalable, economically viable HER catalysts, advancing the global transition to green hydrogen. Overfitting ; Scarcity ; Electrocatalyst ; Throughput ; Bridging (networking) ; Hydrogen production Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Paolo Vincenzo Freiesleben De Blasio | Jonas Elsborg | Elif Coşkun | Andreas Burger | Pradipkumar Manubhai Leuaa | Thu Trang Pham | Anh Phan | Jin Hyun Chang | Christodoulos Chatzichristodoulou
Source: ChemRxiv (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Optimizing Ni-Mo Electrocatalysts With Machine Learning-Guided Autonomous Platform for Next-Generation Alkaline Water Electrolyzers Active and stable electrocatalysts for water electrolysis are essential to reduce the cost of green hydrogen. Scarcity and cost barriers drive extensive efforts to develop PGM-free catalysts, but matching the activity and stability of PGM-based catalysts remains a great challenge due to the slow and time consuming manual experimentation. Here, we present a closed-loop optimization workflow combining an automated electrocatalyst synthesis and testing system with Bayesian optimization for rapid discovery of nickel-molybdenum electrocatalysts. We find that exploration-focused acquisition strategies are critical under limited experimental budgets, and our results indicate that this is due to compositional parameters that exhibit delayed and conditional influence on performance, causing exploitative strategies to prematurely converge to suboptimal regions of the design space. By varying the exploration-exploitation tradeoff in our experimental campaigns, we identified several distinct regions in the Ni-Mo phase space with exceptionally high performance within limited experimental budgets. Bayesian optimization ; Workflow ; Stability (learning theory) ; Electrolysis of water ; Electrolysis ; Matching (statistics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Pengfei Du | Abdellah Ait | El Fakir | Shinya Mine | Bin Yang | Hongli Pan | Chenxi He | Nazmul Hasan | Y Ando | Jörg W. A. Fischer | Akihiko Anzai | Giles Allison | Ryo Toyoshima | Hiroshi Kondoh | Wei Zhou | Christophe Coperet | Ichigaku Takigawa | Ken-ichi Shimizu | Takashi Toyao
Source: ChemRxiv (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Machine learning-assisted development of high-performance ethanol synthesis catalysts via CO2 hydrogenation The discovery and development of high-performance catalysts, which is crucial across all catalysis areas, requires advanced technologies and innovative approaches. Recently, machine learning (ML) has shown promise in accelerating this process, but its capability and examples of discovery of truly novel catalysts have remained limited. In this study, we describe an ML approach that goes beyond the traditional element pool, incorporating elements that have not been previously studied, to develop highly efficient catalysts for ethanol synthesis via CO2 hydrogenation. Starting with an initial dataset of 58 catalysts (274 data points obtained at reaction temperatures ranging from 240-400 °C), we conducted 24 iterations of a closed-loop discovery system (ML predictions + experimental validation), testing a total of 555 catalysts (2477 data points), and building a large experimental dataset. More than 50 catalysts with superior activity were discovered through this data-driven approach. The multi-elemental Pd(0.8)-Au(0.3)/K(2.5)-Sr(1)-Fe(20)-Zn(4)-Cd(2)-Yb(1)-Re(1) /CeO2(25%)-ZrO2 catalyst, where the numbers in parentheses represent weight percent (wt%), was identified as the most effective catalyst for ethanol synthesis (ethanol space-time yield: 8.2 mmol gcat-1 h-1 with a CO2 conversion of 57.6% and an ethanol selectivity of 23.2% under reaction conditions of 360 °C, 4 MPa, 12 L gcat-1 h-1, H2/CO2 = 3/1). Comprehensive characterizations, including in situ/operando techniques such as X-ray absorption spectroscopy (XAS), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), enable us to highlight the critical roles of each constituting element in improving ethanol synthesis efficiency. Catalysis ; Ethanol ; Process (computing) ; Yield (engineering) ; Ethanol fuel ; Raw material Machine Learning in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions