jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cher Tian Ser | Han Hao | Sergio Pablo‐García | Kjell Jorner | Shangyu Li | Robert Pollice | Alán Aspuru‐Guzik
Source: ChemRxiv (article, 2025-08-19)
Field: Chemistry — Organic Chemistry

Bulky phosphine ligands promote palladium-catalyzed protodeboronation The Suzuki-Miyaura cross-coupling reaction is plagued by protodeboronation, an undesirable side reaction with water that consumes the boronic acid derivatives required for the cross-coupling reaction. Meticulous mechanistic studies have previously established protodeboronation to be highly sensitive to the nature of the boronic reagent and reaction conditions. Particularly, the presence of bases, which are essential for the Suzuki-Miyaura coupling, is known to catalyze protodeboronation. However, protodeboronation catalyzed by palladium-phosphine complexes, the benchmark catalyst system for Suzuki-Miyaura cross-coupling, has been understudied compared to its base-catalyzed counterpart. We demonstrate, using automated high-throughput experimentation, comprehensive computational mechanistic analyses and kinetic modeling, that protodeboronation is accelerated by palladium(II) complexes bound to bulky phosphine ligands. While sterically hindered ligands are typically used to facilitate difficult cross-couplings, these ligands can instead paradoxically impede cross-coupling product formation, requiring careful and judicious consideration when choosing ligands for Suzuki-Miyaura cross-couplings. Phosphine ; Palladium ; Catalysis ; Chemistry ; Combinatorial chemistry ; Organic chemistry Organoboron and organosilicon chemistry ; Chemical Synthesis and Analysis ; Catalytic Cross-Coupling Reactions
Authors: Murad Khan
Source: Research & Development in Material Science (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Designing the Materials of Tomorrow Insights from Recent Developments Central to this evolution is the concept of materials-by-design, which emphasizes tailoring properties at the atomic and molecular levels to achieve desired macroscopic behavior.This paradigm shift leverages advancements in computational power, enabling high-throughput screening of potential materials and optimization of properties before synthesis.Computational methods like Density Functional Theory (DFT) and molecular dynamics simulations have become indispensable tools in predicting the electronic, thermal, and mechanical properties of materials [3,4].Simultaneously, Artificial Intelligence (AI) and Machine Learning (ML) algorithms are increasingly being used to accelerate material 2534 Work (physics) ; Context (archaeology) ; Key (lock) ; Focus (optics) ; Process (computing) Material Selection and Properties ; Machine Learning in Materials Science ; Architecture and Computational Design
Authors: Shan He | Tong Luo | Xiaoe Chen | David James Young | Matt Jellicoe
Source: Preprints.org (preprint, 2025-02-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Developments in Automated Reactors for Plasmonic Nanoparticles Automated reactors are revolutionizing nanomaterial synthesis, streamlining processes through advanced machine-driven execution and data collection. By delegating repetitive tasks—such as experimental execution and data acquisition—to automation, researchers can focus more on critical analysis and creative problem-solving. Unlike traditional approaches that primarily manipulate starting materials, autonomous systems now enable precise control over morphology at various growth stages. This capability is crucial for applications such as efficient chemical space exploration and adaptive manufacturing. Despite these advancements, transitioning from single-step automation to complex, multistep syntheses remains a major challenge due to the intricate structural diversity of nanomaterials and the interdependent chemical and physical processes involved. Robotic batch and continuous-flow platforms are progressively evolving into more versatile systems, expanding access to a broader range of chemistries required for autonomous multistep fabrication. Advancements in process analytical technologies have significantly improved real-time monitoring of interconnected reactions, accelerating data collection while ensuring stringent safety standards and high product quality. The integration of these monitoring tools with advanced control software establishes adaptive feedback loops, enabling flexible multistep screening and self-optimizing synthesis strategies. This mini review explores recent developments in automated reactor technologies for plasmonic nanomaterial synthesis, focusing on both batch and continuous-flow platforms and giving an outlook on what’s next for the field. Nanoparticle ; Plasmon ; Nanotechnology ; Plasmonic nanoparticles ; Materials science ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Yutong Liu | Mehrad Ansari | Robert Black | Jason Hattrick‐Simpers
Source: Communications Materials (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Kernel learning assisted synthesis condition exploration for ternary spinel Abstract Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, the lack of established synthesis routes for solid-state materials remains a significant challenge in inorganic chemistry. An interpretable machine learning model is therefore essential, as it provides insights into the key factors governing phase formation. Here, we focus on the formation of single-phase Fe 2 (ZnCo)O 4 , synthesized via a high-throughput co-precipitation method. We combined a kernel classification model with an application of global SHapley Additive exPlanations (SHAP) analysis to pinpoint the experimental features most critical to single phase synthesizability by interpreting the contributions of each feature. Global SHAP analysis reveals that precursor and precipitating agent contributions to single-phase spinel formation align closely with established crystal growth theories. These results not only underscore the importance of interpretable machine learning in refining synthesis protocols but also establish a framework for data-informed experimental design in inorganic synthesis. Ternary operation ; Kernel (algebra) ; Spinel ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Materials science ; Combinatorics ; Metallurgy X-ray Diffraction in Crystallography ; Geochemistry and Geochronology of Asian Mineral Deposits ; Pigment Synthesis and Properties
Authors: Rebekah Duke | Chih-Hsuan Yang | Baskar Ganapathysubramanian | Chad Risko
Source: ChemRxiv (preprint, 2025-01-30)
Field: Chemistry — Organic Chemistry

Evaluating molecular similarity measures: Do similarity measures reflect electronic structure properties? The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance of quantifying molecular similarity. Molecular similarity, commonly assessed as the distance between molecular fingerprints, is integral to applications such as database curation, diversity analysis, and property prediction. AI tools frequently rely on these similarity measures to cluster molecules under the assumption that structurally similar molecules exhibit similar properties. However, this assumption is not universally valid, particularly for continuous properties like electronic structure properties. Despite the prevalence of fingerprint-based similarity measures, their evaluation has largely depended on biological activity datasets and qualitative metrics, limiting their relevance for non-biological domains. To address this gap, we propose a framework to evaluate the correlation between molecular similarity measures and molecular properties. Our approach builds on the concept of neighborhood behavior and incorporates kernel density estimation (KDE) analysis to quantify how well similarity measures capture property relationships. Using a dataset of over 350 million molecule pairs with electronic structure, redox, and optical properties, we systematically evaluate the correlation between several molecular fingerprint generators, distance functions, and these properties. Both the curated dataset and the evaluation framework are publicly available. Similarity (geometry) ; Computer science ; Information retrieval ; Artificial intelligence Chemical Thermodynamics and Molecular Structure ; History and advancements in chemistry
Authors: Stef A. H. Jansen | Lasse Sander Dreyer | Jule van Basten | Yihan Yao | Daniel E. Otzen | Tom F. A. de Greef | Tuomas P. J. Knowles | E. W. Meijer | Nadia A. Erkamp
Source: ACS Nano (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

PhaseXplorer Creates High-Dimensional Phase Diagrams with Closed-Loop Active Learning Phase separation fundamentally governs material properties and cellular function on multiple organizational scales. Conventional approaches to studying this nonlinear phenomenon necessitate resource-intensive experiments. As such, investigations were limited to low-dimensional space. We present PhaseXplorer, a platform that combines microfluidics, microscopy, and machine learning to efficiently study phase separation systems. PhaseXplorer autonomously designs, generates, and analyzes samples in a closed-loop active learning workflow until an accurate phase diagram is obtained. Using an acquisition function that balances exploration and exploitation, all the phase boundaries are located with minimal sampling. A convolutional neural network executes real-time image recognition to identify microfluidic droplets and phase separation within them in less than 1 ms per droplet. PhaseXplorer standardizes analysis across experiments and does not require calibration nor extensive postexperiment analysis. We demonstrate PhaseXplorer's capabilities using a poly rA model system by creating a four-dimensional phase diagram 100 times faster than traditional methods while simultaneously consuming 10,000 times less material. Workflow ; Phase diagram ; Convolutional neural network ; Phase (matter) ; Function (biology) ; Calibration ; Artificial neural network Block Copolymer Self-Assembly ; Advanced Electron Microscopy Techniques and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Musa Shamilevich Adygamov | Anton Olegovich Golub | Э Р Сайфуллин | Timur Gimadiev | Nikita Yu. Serov
Source: Russian Digital Libraries Journal (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Intelligent Chemist Robot: Towards an Autonomous Laboratory This paper describes a hardware and software platform that enables automated chemical syntheses, including the preparation, heating, and mixing of reaction mixtures, as well as post-synthesis dilution sampling and sending for high-performance liquid chromatography (HPLC) analysis, followed by automated processing of the results. A custom Python library, ChemBot, was developed to control individual robotic devices, and a client web server was created to manage the entire system. A web interface was created to view the system status and the progress of syntheses. The performance of the entire platform for performing experiments was tested by performing aldol condensation syntheses, where the ratio of reagents, the catalyst and its amount, the temperature and time of synthesis were varied. Writing custom code to monitor and control the entire system is an important step toward integrating the robotic system with artificial intelligence (AI), which will ultimately enable the transition to an autonomous laboratory, where target molecule prediction and synthesis, experimental execution and analysis, and, if necessary, refinement or modification of the model will be performed automatically, without the need for human intervention. Software ; Python (programming language) ; Interface (matter) ; Control software ; Automation ; Source code ; Robotic arm ; User interface Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Various Chemistry Research Topics
Authors: Hong Pang | Cédric Bourgès | Naohito Tsujii | Jha Rajveer | Naoyuki Kawamoto | Fumihiko Ichihara | Takahiro Baba | Tetsuya Baba | Naoki Sato | Yuichi Yamasaki | Takao Mori
Source: Institutional Repositories DataBase (IRDB) (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Zinc Stabilized Cation Ordered Chalcopyrite Thin Film for Enhanced Thermoelectric Power Generation Near Room Temperature Sulfides represent a significant family in thermoelectric materials for their low cost and promising performance. Sulfide thin films have been a very challenging topic due to the difficulty in finely controlling the composition. The fabrication of CuFeS2 thin film attracts researchers as CuFeS2 has been demonstrated to have a big potential in thermoelectricity and photovoltaic. Herein we reveal the newest finding of the Zn dopant to stabilize the chalcopyrite phase and suppress the reverse transformation back to wurtzite at high temperature in magnetron sputtered films. By increasing the Zn doping amount, the transformation temperature could be lowered and the majority carrier concentration could be optimized. Based on this principle, we obtained the chalcopyrite thin film by modulating the doping amount during co-deposition, and boosted the thin film’s thermoelectric properties to a power factor of 0.168 mW/m·K2 at room temperature and 0.285 mW/m·K2 at 523 K, which is multifold than the wurtzite CuFeS2 thin film. The dimensionless figure of merit was comparable to the bulk counterpart. It is highlighted that the phase transformation from wurtzite CuFeS2 to the chalcopyrite CuFeS2 driven by Zn substitution is demonstrated for the first time by synchrotron-based in situ X-ray diffraction and X-ray absorption fine structure measurements, giving deep insights into the isomeric structure of CuFeS2 and doping effects on the local structure. This work also opens up the possibility of applying the sputtered sulfide thin films for flexible thermoelectric and magnetic devices. The in-depth understanding of phase transformation between CuFeS2 polymorphs might be significant for the ternary I-III-S(Se/Te)2 compound film fabrication and the doping modulation in thermoelectrics, solar cells, and other energy conversion. Thin film ; Chalcopyrite ; Zinc ; Thermoelectric effect ; Seebeck coefficient ; Electricity generation ; Thermoelectric generator Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Semiconductor materials and interfaces
Authors: Nis Fisker‐Bødker | Enzo Raffaele Moretti | Jin Hyun Chang | Tejs Vegge
Source: ChemRxiv (preprint, 2025-07-09)
Field: Materials Science — Materials Chemistry

FastCat - Autonomous Discovery ofMultielement LDH Alloy Catalysts for AlkalineOER The search for efficient, durable, and scalable catalysts for the Oxygen Evolution Reaction (OER) is hampered by the slow innovation cycles for advanced materials. Here, we present FastCat - an AI-orchestrated self-driving closed-loop materials discovery system for the autonomous discovery of platinum group metal (PGM) free multimetal hydroxides for alkaline water electrolysis. With FastCat, we have synthesized, characterized, and tested more than 500 Ni-based multielement layered double hydroxide (LDH) OER catalysts. In one of the largest AI-orchestrated catalyst discovery campaigns to date, our metaheuristic Bayesian Optimization identified known high-performance compositions and several novel multielement Ni-Fe-Cr-Co alloys with unprecedented overpotentials as low as 231 mV at 20 mA/cm^2 and compositional ratios approaching 1/3 , 1/3, 1/6, 1/6 at higher current densities. FastCat can synthesize and electrochemically test up to 75 material compositions daily without human interaction. It can optimize the composition by analyzing trends in the test results, validating their reproducibility, and testing durability. This study includes measurements of >1000 samples, making it one of the largest autonomous experimental electrocatalysis datasets to date, and the findings showcase how autonomous materials discovery can make complex, multielement compositions accessible for research and drastically accelerate the development of novel materials. Alloy ; Catalysis ; Chemistry ; Biochemistry ; Organic chemistry Catalytic Processes in Materials Science
Authors: Arshia Batool | Tanzeela Urooj
Source: Journal of Engineering Science and Technological Trends (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

AI/ML-Driven Design and Optimisation of Quantum Dots: A Perspective Toward Intelligent Materials Discovery Quantum dots (QDs), nanoscale semiconductors with size-dependent and tunable optoelectronic properties, are central to next-generation technologies spanning displays, photovoltaics, bioimaging, and quantum information systems. However, their synthesis and optimisation remain challenging due to the intricate interplay of reaction parameters and nonlinear physicochemical interactions. The integration of artificial intelligence (AI) and machine learning (ML) is redefining this landscape, enabling predictive design, autonomous synthesis control, and accelerated discovery across the QD domain. This Perspective highlights the conceptual advances and methodological innovations driving AI/ML-assisted QD research, emphasising achievements in data-driven modelling, synthesis optimisation, and materials informatics. Persistent challenges, including data scarcity, model transparency, and limited generalizability, are critically examined, alongside emerging strategies toward physics-informed and autonomous discovery frameworks. We propose that the convergence of intelligent algorithms and human expertise will catalyse a paradigm shift from empirical experimentation toward rational, self-evolving materials design in quantum dot science. Perspective (graphical) ; Quantum dot ; Quantum ; Convergence (economics) ; Quantum computer ; Scale (ratio) ; Dependency (UML) Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Nanowire Synthesis and Applications
Authors: Benben Jiang | Xufeng Hong | Xizhe Wang | Stephen J. Harris | Hongbo Zhao | Jiashen Meng | Qing‐Shan Jia | Qianchuan Zhao | Kang Xu | Quanquan Pang
Source: Research Square (preprint, 2025-08-11)
Field: Materials Science — Materials Chemistry

Deep active learning and knowledge transfer for rapid discovery of lithium metal battery electrolytes Electrolyte ; Computer science ; Chemical space ; Lithium (medication) ; Transfer of learning ; Artificial intelligence ; Chemistry ; Drug discovery ; Electrode Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: M. Prema Rani
Source: International Journal on Science and Technology (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Role of Machine Learning in Inorganic and Coordination Chemistry: Applications and Challenges Machine learning (ML) is a branch of artificial intelligence. ML is revolutionizing various areas of chemistry by enhancing predictions of chemical properties, uncovering hidden patterns in complex data, and accelerating the discovery of new compounds. Machine learning is also accelerating research and development in the complex and limited data fields of coordination and inorganic chemistry. This paper examines the advancements in inorganic and coordination chemistry enabled by the application of ML. These advancements include the prediction of chemical properties, the design of effective catalysts, spectroscopy, and the analysis of compound structures. This paper discusses case studies where ML techniques are effectively applied to research and development in inorganic and coordination chemistry. In the end of this paper, the key challenges in the path of using ML techniques in fostering innovation in inorganic and coordination chemistry are highlighted. Coordination complex ; Computer science ; Chemistry ; Nanotechnology ; Materials science Machine Learning in Materials Science
Authors: Yang Pu | Yujiang Du | Qizhen Song | Ning Jia | Yingchu Dong | Mingye Yang | Luyao Ma | Ruihao Chen | Hongqiang WANG | Qi Chen | Yang Bai | Zhe Liu
Source: Cell Reports Physical Science (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Hierarchical optimization of perovskite solar cell fabrication via step-by-step machine learning <h2>Summary</h2> Scalable manufacturing of perovskite solar cells (PSCs) remains challenging due to the need for efficient co-optimization of multiple interdependent process parameters across stepwise fabrication stages. Here, we propose a machine-learning-driven multi-target Bayesian optimization (MTBO) framework that integrates adaptive constraints with hierarchical objective optimization to guide full process optimization. Applied to electrospray deposition, MTBO simultaneously optimized six processing parameters and five target variables related to perovskite absorbers. By incorporating the characterization results of precursor solubility, perovskite crystallinity, and film quality as adaptive constraints, MTBO ensures that optimization stays within feasible manufacturing conditions while hierarchically prioritizing optimization targets of carrier lifetime and film homogeneity aligned with fabrication workflow. Under MTBO-optimized conditions, a champion PSC based on a 1.63 eV FA<sub>0.82</sub>Cs<sub>0.18</sub>Pb(I<sub>0.86</sub>Br<sub>0.11</sub>Cl<sub>0.03</sub>)<sub>3</sub> perovskite achieved a power conversion efficiency (PCE) of 21.95%. These results demonstrate that MTBO enables autonomous and intelligent optimization across a multi-stage, multi-target fabrication workflow, paving the way toward self-driving laboratories for scalable PSC manufacturing. Fabrication ; Bayesian optimization ; Perovskite (structure) ; Perovskite solar cell ; Process optimization ; Scalability ; Curse of dimensionality ; Process (computing) Machine Learning in Materials Science ; Perovskite Materials and Applications ; TiO2 Photocatalysis and Solar Cells
Authors: Soumendu Bagchi | Ankita Biswas | Prasanna V. Balachandran | Ayana Ghosh | Panchapakesan Ganesh
Source: ArXiv.org (preprint, 2025-04-07)
Field: Materials Science — Materials Chemistry

Towards "on-demand" van der Waals epitaxy with an adaptive resource-driven online ensemble sampling simulation framework Traditional approaches to achieve targeted epitaxial growth involves exploring a vast parameter space of thermodynamical and kinetic drivers (e.g., temperature, pressure, chemical potential etc). This tedious and time-consuming approach becomes particularly cumbersome to accelerate synthesis and characterization of novel materials with complex dependencies on local chemical environment, temperature and lattice-strains, specifically nanoscale heterostructures of layered 2D materials. We combine the strength of next generation supercomputers at the extreme scale, machine learning and classical molecular dynamics simulations within an adaptive real time closed-loop virtual environment steered by Bayesian optimization to enable asynchronous ensemble sampling of the synthesis space, and apply it to the recrystallization phenomena of amorphous transition-metal dichalcogenide (TMDC) bilayer to form stack moiré heterostructures under various growth parameters. We show that such asynchronous ensemble sampling frameworks for materials simulations can be promising towards achieving on-demand epitaxy of van der Waals stacked moiré devices, paving the way towards a robust autonomous materials synthesis pipeline to enable unprecedented discovery of new functionalities. van der Waals force ; Asynchronous communication ; Chemical space ; Molecular dynamics ; Amorphous solid ; Characterization (materials science) ; Epitaxy ; Rectification Machine Learning in Materials Science ; 2D Materials and Applications ; Catalysis and Oxidation Reactions