jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jeffrey A. Bennett | Milad Abolhasani
Source: Open MIND (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Dataset and code for the publication "An Autonomous Lab for Data-Driven Homogeneous Catalysis" Compiled dataset for Flex-Cat, including processed datafiles and figure reproduction code, example machine logfiles, digital twin model, and closed-loop bayesian optimization code. Code (set theory) ; Homogeneous ; Bayesian probability ; Source code Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Julia Scully | Reymark D. Maalihan | Eugene B. Caldona
Source: Journal of Fluorine Chemistry (article, 2026-03-16)
Field: Materials Science — Polymers and Plastics

Fluoropolymers in additive manufacturing: From chemical foundations to emerging strategies Chemical products Polymer Nanocomposite Synthesis and Irradiation ; Additive Manufacturing and 3D Printing Technologies ; biodegradable polymer synthesis and properties
Authors: Jeffrey A. Bennett | Milad Abolhasani
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Dataset and code for the publication "An Autonomous Lab for Data-Driven Homogeneous Catalysis" Compiled dataset for Flex-Cat, including processed datafiles and figure reproduction code, example machine logfiles, digital twin model, and closed-loop bayesian optimization code. Code (set theory) ; Homogeneous ; Bayesian probability ; Source code Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Rui Fu | Sujit Sinha | Christopher Barrow | John Maddox | Jesse B. Hoagg | Alexandre Martin
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Approach For Real-Time Estimation of Material Aerospace ; Bottleneck ; Transient (computer programming) ; Thermal conductivity ; Process (computing) ; Property (philosophy) ; Range (aeronautics) ; Reduction (mathematics) ; Work (physics) ; Process control Thermal properties of materials ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Osman Goni Ridwan | Sylvain Pitié | Monika Raj | Dong Dai | Gilles Frapper | Hongfei Xue | Qiang Zhu
Source: npj Computational Materials (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

AI-assisted rapid crystal structure generation towards a target local environment Crystal structure ; Crystal (programming language) ; X-ray crystallography ; Work (physics) ; Phase (matter) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Enzyme Structure and Function
Authors: Xu Ha | X. L. Wang | Saira Qurban | Guo‐Qiang Xu
Source: Chemistry - An Asian Journal (article, 2026-04-01)
Field: Chemistry — Organic Chemistry

Radical Smiles Rearrangements: Aryl Migration for Molecular Editing Smiles and Truce-Smiles rearrangements have evolved from classical nucleophilic aromatic substitutions into a versatile platform for radical aryl migration and molecular editing. This Review surveys how visible-light photoredox catalysis and related photoinduced activation modes-including electron-donor-acceptor (EDA) complexes, energy-transfer and ligand-to-metal charge-transfer (LMCT) processes, metal-hydride hydrogen atom transfer (MHAT), and radical-cation pathways-have transformed sulfonyl and sulfinyl groups into bifunctional aryl-migration handles that operate under remarkably mild conditions. After outlining the mechanistic features that distinguish ionic, radical, and radical-cation Smiles pathways, we discuss traditional radical Smiles rearrangements that established the feasibility of aryl transfer via open-shell Meisenheimer-like intermediates. We then focus on photocatalytic and catalyst-free radical Smiles and Truce-Smiles reactions, including alkene difunctionalizations, sulfur dioxide (SO<sub>2</sub>) insertion/relay cascades, decarboxylative and decarbonylative arylations, remote sp<sup>3</sup> carbon-hydrogen (C(sp<sup>3</sup>)-H) functionalization, and emerging applications in polymer synthesis and biocatalysis. Particular emphasis is placed on reagent design (sulfonamides, sulfones, sulfinamides, and sulfoxides), control over SO<sub>2</sub> extrusion versus retention, and recent advances in asymmetric sulfinyl-Smiles chemistry. We conclude by highlighting current limitations and future opportunities, especially in expanding substrate generality, stereocontrol, and late-stage functionalization, and in integrating Smiles rearrangements with electrochemical, flow, and data-driven catalysis for applications in medicinal chemistry and sustainable materials. Smiles rearrangement ; Photoredox catalysis ; Aryl ; Bifunctional ; Aryl radical ; Nucleophile ; Radical ; Alkene Radical Photochemical Reactions ; Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods
Authors: Hiyori Takebe | Seijiro Matsubara
Source: Chemistry Letters (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Organic Reaction Development Guided by Large Language Models: The Next Stage of AI-Driven Chemistry Abstract Digitalization-driven transformative organic synthesis has become an established research style in organic synthesis over the past decade, including reaction optimization, molecular design, and materials informatics (MI). Despite methodological advances, fundamental challenges remain in molecular representation, data curation, and hypothesis generation. This perspective review reexamines these issues from the standpoint of experimental organic synthesis and discusses the emerging role of large language models (LLMs). By integrating contextual reasoning, experimental records, and molecular networks, LLMs are positioned not as predictive engines but as cognitive layers that shorten the cycle time of hypothesis–experiment–revision loops. Transformative learning ; Organic synthesis ; Informatics ; Perspective (graphical) ; Style (visual arts) ; Organic molecules Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Ning Han | Xiaobo Li
Source: Clean Energy (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Robot-Driven Synthesis Platform Enables Next Generation of Catalysts with Industrial Compatibility Abstract Single-atom catalysts have long been recognized as a revolutionary class of catalytic materials, owing to their maximal atom utilization efficiency and unique electronic structures that enable superior catalytic performance across diverse reactions.1,2 However, the translation of catalysts, for example single-atom catalysts, from laboratory-scale fundamental research to industrial-scale practical applications has been severely hindered by inherent limitations in traditional synthesis strategies—weak metal–support interactions lead to poor stability, low reproducibility, and scalability challenges. Against this backdrop, Prof. Jin Zhang and Prof. Ruqian Zou from Peking University, in collaboration with Prof. Ju Li from the Massachusetts Institute of Technology and Jiong Lu from the National University of Singapore, have developed a clicking auxiliary-enabled strategy for constructing clicking-single-atom catalysts and integrated it with a robot-driven high-throughput platform.3 This innovative approach represents a pivotal step forward in bridging the gap between single-atom catalyst research and industrial deployment. This achievement not only paves the way for the industrialization of single-atom catalysts, but also marks the advent of a new era of materials research driven by the synergy of artificial intelligence and robotics. Moving forward, catalytic materials development should take “full-process intelligence” as the core goal to drive leaps and bounds in energy catalysis, environmental catalysis and other fields. LEAPS ; Bridging (networking) ; Scalability ; Compatibility (geochemistry) ; Catalysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Ammonia Synthesis and Nitrogen Reduction
Authors: Joe Bailey | Søren C. Spina | Andrew C-C Hu | Nathan Phan | Rachel B. Getman | Blaise R. Kimmel
Source: ACS Engineering Au (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Chemical Engineering: Protein Design from First Principles to Structural Prediction Structural approach ; Expert system ; Key (lock) ; Structural protein ; Artificial neural network Machine Learning in Materials Science ; Process Optimization and Integration ; Protein Structure and Dynamics
Authors: Yunlu Zhang
Source: Transactions on Environment Energy and Earth Sciences (article, 2026-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of Graphene Materials in Energy Storage and Conversion Graphene, a two-dimensional carbon nanomaterial, has emerged as a transformative component in energy storage and conversion due to its exceptional electrical conductivity (up to 10^8 S/m), high specific surface area (2.63 × 10^3 m²/g), and mechanical strength (Young's modulus ~1.0 TPa). This review examines the pivotal role of graphene-based materials in advancing lithium-ion batteries, supercapacitors, fuel cells, and solar cells. In batteries, graphene composites enhance energy density and cycling stability, addressing issues like volume expansion in silicon anodes. For supercapacitors, graphene enables high capacitance and rapid charge-discharge cycles. In fuel cells, it improves electrocatalytic efficiency and membrane durability, while in solar cells, it serves as a transparent electrode to boost charge transport. Synthesis methods, including chemical vapor deposition and Hummers’ method, are discussed alongside challenges such as scalability, defect control, and cost-effective production. The future outlook is hybrid materials and AI-assisted optimization to provide sustainable energy solutions, which places graphene as one of the foundations of the future generation of energy technologies. Graphene ; Supercapacitor ; Energy storage ; Chemical vapor deposition ; Energy transformation ; Carbon fibers ; Photovoltaic system ; Capacitance ; Energy conversion efficiency Supercapacitor Materials and Fabrication ; Graphene research and applications ; Chemical and Physical Properties of Materials
Authors: Kang Ao | Long Kuang | Jiajiao Zhe | Yaru Li | Wei Gan | Lanxian Li | Zongsheng Tao | Cuixia Yan
Source: NANO (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Multifaceted Frontiers of Two-Dimensional Materials: From Controlled Synthesis to Multifunctional Applications Two-dimensional (2D) materials have emerged as a transformative platform in interdisciplinary research, characterized by atomically thin layers, exceptional electronic structures and outstanding physicochemical properties. This review provides a systematic overview and synthesis of recent advances in 2D materials, establishing a comprehensive framework from fundamental characterization to functional device implementation. We first delineate the controlled growth strategies centered on chemical vapor deposition (CVD) and bottom-up on-surface synthesis, highlighting the pivotal role of scanning probe microscopy (SPM) and transmission electron microscopy (TEM) in achieving atomic-resolution structural analysis and real-time observation of defect evolution. Subsequently, we examine state-of-the-art applications across six critical domains: (1) 2D materials have been controllably synthesized and atomically characterized; (2) electronics and optoelectronics, encompassing field-effect transistors, ferroelectric tunnel junctions and flexible electronic skins; (3) energy storage and conversion, providing mechanistic insights into electrocatalysis and photocatalysis (HER/OER/ORR), lithium/sodium-ion batteries and thermoelectric materials; (4) thermal management, focusing on design principles for high-thermal-conductivity composites and interfacial thermal resistance engineering; (5) functional membranes, emphasizing the barrier and molecular-sieving effects of 2D carbon-based membranes in seawater desalination, ion-selective transport and corrosion-resistant coatings; (6) metal matrix composites, demonstrating how graphene enhances metallic matrices through tailored interface engineering and multiscale hybrid architectures. Finally, we address critical challenges — scalable manufacturing, interfacial stability and multiperformance optimization — and project future directions, including artificial intelligence (AI)-assisted materials design, multifield-coupled intelligent responsiveness and green, sustainable fabrication pathways. This work provides theoretical guidance and technical reference for the translation of 2D materials from laboratory innovations to industrial deployment. Characterization (materials science) ; Electronics ; Graphene ; Electrocatalyst ; Fabrication ; Flexible electronics ; Chemical vapor deposition 2D Materials and Applications ; Thermal properties of materials ; Solar-Powered Water Purification Methods
Authors: Pawan Bohane | V. B. Rajkumar | Trushar B. Gohil | Ajeet K. Srivastav
Source: Modelling and Simulation in Materials Science and Engineering (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Data-driven discovery of copper alloys: resolving the strength-conductivity paradox Abstract Achieving a simultaneous improvement in hardness (HV) and electrical conductivity (EC) remains a critical challenge in the development of copper alloys, where strengthening mechanisms often compromise conductivity. In the present work, we employed an integrated design strategy combining Bayesian optimization with an extreme gradient boosting predictive model to optimize both alloy composition and processing parameters in a Cu–Cr–Zr system. This approach identified a Cu–0.88Cr–0.14Zr (wt.%) alloy as the optimal composition, with solution treatment at 1213 K <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>T</mml:mi> <mml:mrow> <mml:mtext>ss</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> and aging at 873 K <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>T</mml:mi> <mml:mrow> <mml:mtext>ag</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> tuned to balance precipitation hardening and conductivity retention. CALPHAD based thermodynamic calculations using the Cu–Cr–Zr model parameters indicate that Cr-rich phases and Cu 5 Zr precipitates are stable under the selected conditions. These predictions were further validated through experimental synthesis and characterization. The alloy exhibited a HV of 247.4 HV and an EC of 76.8% IACS. Microstructural examination revealed a fine and homogeneous dispersion of nanoscale precipitates, which effectively contributed to the simultaneous enhancement of mechanical and electrical properties. This combined computational-experimental framework offers a promising pathway for designing copper alloys with potential applications in advanced electrical and electronic systems that require high strength and conductivity. Alloy ; Precipitation hardening ; Copper ; CALPHAD ; Electrical resistivity and conductivity ; Precipitation ; Bayesian optimization ; Conductivity Machine Learning in Materials Science ; Microstructure and mechanical properties ; Copper Interconnects and Reliability
Authors: Jördis Sieburg-Rockel | Stephanie Helmling | Lars Nieradzik | Stephanie Wrage | Thomas Weibel | Tim Lehne | Volker Haag | Immo Heinz | Jonas Heddier | Henrike Stephani | Andrea Olbrich
Source: Holzforschung (article, 2026-02-05)
Field: Chemistry — Organic Chemistry

Wood identification in fiber materials: a comparative blind test study of artificial intelligence and human experts Abstract The conversion of processes controlled by human expertise and know-how into automated operations is the goal of a wide range of modern research. This study reports on a blind test in which twelve unknown samples were analyzed by human experts and an AI system developed to check the genera of hardwoods used in paper production. Trained to detect hardwood cells in microscopic images of maceration slides, it assigns them to the nine most commonly used hardwood species. Softwoods were also added to the samples, to make the test as realistic as possible. Human experts achieved an accuracy of 0.96 for recognizing a genus contained in a sample. The two-stage machine recognition system achieved an accuracy of 0.79 at the defined threshold values of 0.7 for object recognition and 0.75 for genus probability. Human experts failed to recognize some genera contained in the samples, whereas the AI models did not miss any genus at these thresholds. A comparison of the results revealed the strengths and weaknesses of the independent methods. This study is an important step towards optimizing the automated image recognition system developed to support the implementation of the European Union Deforestation Regulation (EUDR) with regard to deforestation-free products/supply chains. Identification (biology) ; Hardwood ; Wood industry ; European union ; Pattern recognition (psychology) ; Strengths and weaknesses ; Test (biology) ; Wood processing Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Haoran Zhang | Yuen Wu
Source: Precision Chemistry (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Field-Programmable Dynamic Catalytic Technology to Make Catalyst Intelligent Conventional heterogeneous catalysts are constrained by static atomic structures, which are ill-suited for the diverse requirements of multistep reactions. This article introduces Field-Programmable Dynamic Catalysis (PDC), a paradigm enabling real-time, reversible programming of atomic structures via external fields and intelligent feedback. Using copper-based liquid metal catalysts, we demonstrate adaptive restructuring that optimizes methane oxidation performance. With magnetically tunable iron-based catalysts, we achieve reversible switching between atomically dispersed and clustered states, thus validating on-demand control over reaction pathways. This paradigm holds great promise for inert small molecule activation, such as nitrogen conversion, yet key challenges remain in boosting liquid metal surface area and designing scalable reactors. To address this multidimensional complexity, artificial intelligence is indispensable for accelerating the discovery and development of advanced PDC catalysts. Ultimately, PDC transforms catalysts from passive materials into active information-processing systems, enabling intelligent and precise chemical control at the atomic scale. Catalysis ; Scalability ; Inert ; Boosting (machine learning) ; Key (lock) ; Intelligent control Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: Reveka Kushwah | Samy G. Alamir | Sulaiman Al-Sulaimi | Ahmed Al-Harrasi | Adel Ehab Ibrahim
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multifaceted boron nitride nanomaterials: a comprehensive review of synthesis, property engineering and multidisciplinary applications Boron nitride (BN) nanomaterials, spanning zero-dimensional (0D) quantum dots, 1D nanotubes, 2D nanosheets, 3D and macroscopic architectures, have garnered emerging attention as functional analogs to carbon-based nanomaterials. Their wide band gap, high thermal conductivity, electrical insulation, and chemical inertness make them versatile candidates for advanced technologies. This review highlights the synthesis-structure-function relationships achieved through top-down methods, such as exfoliation and ball milling, and bottom-up strategies, including chemical vapor deposition and plasma processing. Defect engineering, dopant incorporation, and surface functionalization are further discussed as key levers for tailoring BN nanomaterials' optical, electronic, and interfacial behavior. The review also explores the diverse range of BN nanomaterial applications across various disciplines, such as chemical analysis, biomedicine, catalysis, energy, and aerospace. The recent advances highlight opportunities for data-driven strategies coupled with <i>operando</i> characterization to control and optimize composition, morphology, and defect chemistry. These developments are expected to accelerate translation into practical technologies across wide applications. Boron nitride ; Surface modification ; Chemical vapor deposition ; Nanomaterials ; Dopant ; Multidisciplinary approach Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Thermal properties of materials
Authors: Júlia Didier Pedrosa de Amorim | Patrice H. N. Crosby | Jennifer Tran | Wren Kitchings | Rong Yang | Eleftheria Roumeli
Source: Annual Review of Chemical and Biomolecular Engineering (article, 2026-02-27)
Field: Chemistry — Organic Chemistry

Microbial Polymers and Living Interfaces: Interplay Between Matter and Microbes Polymer design is being reshaped by demands for low-carbon fabrication and bioactive/living function. We trace the bidirectional interface between microbes and polymers. First, we analyze how microbes synthesize polymers like polysaccharides, polyesters, and proteins and how post-synthesis processing (via mechanical and/or chemical treatments) reshapes molecular architecture and mechanical and thermal properties. We compare reported properties, highlight missing metrics, and evaluate sustainability levers including solvent recovery, cradle-to-gate impacts, and biodegradation/biocontainment constraints. Second, we examine how polymers shape the behavior of living organisms in the context of engineered living materials. Design is organized around four axes-regulating adhesion and detachment, sustaining or directing growth for regeneration, imposing spatial organization on consortia, and tuning phenotype-with implementations in drug delivery, carbon capture, antimicrobial screening, and structural composites. Finally, we outline how automation, artificial intelligence-guided experimentation, and robust sustainability metrics can couple performance with responsible deployment. Context (archaeology) ; Polymer ; Living systems ; Sustainability ; Synthetic biology ; Modularity (biology) ; Living matter Antimicrobial agents and applications ; Bacterial biofilms and quorum sensing ; Polymer Surface Interaction Studies