jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Persaud | Mike Werezak | Mark Xu | Melyne Zhou | Frank Benkel | Xin Pang | Vahid Attari | Brian DeCost | Ashley S. Dale | Nicholas A. Senior | Gabriel Birsan | Jason Hattrick-Simpers
Source: ArXiv.org (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Materials Acceleration Platform for Electrochemistry (MAP-E): a Platform for Autonomous Electrochemistry Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. We introduce the Materials Acceleration Platform for Electrochemistry (MAP-E), an autonomous, high-throughput system capable of performing parallel electrochemical experiments. MAP-E integrates robotic liquid handling, sample transfer, and multi-channel potentiostatic control and extract corrosion metrics without human intervention. Validation against an ASTM G61-analog benchmark demonstrates reproducibility, with a standard deviation of 76 mV in pitting potential across 32 automated measurements. The platform was then employed to autonomously construct pH-chloride stability diagrams for 304 stainless steel using an uncertainty-driven sampling strategy on a Gaussian Process surrogate model. This approach reduces operator involvement and accelerates the exploration of environmental spaces. MAP-E establishes a framework for autonomous electrochemical experimentation, enabling generation of corrosion datasets that inform materials discovery, alloy design, and durability assessment in service environments. Corrosion ; Benchmark (surveying) ; Electrochemical noise ; Acceleration ; Electrochemistry ; Process (computing) ; Durability ; Sampling (signal processing) ; Automation Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Adrian J. Mulholland | Luciano A. Abriata
Source: Protein Science (article, 2026-01-20)
Field: Chemistry — Physical and Theoretical Chemistry

Elements and roadmap for interactive molecular graphics and modeling “in the Holodeck” Molecular graphics have been instrumental in advancing chemistry, drug discovery, materials science and structural biology, enabling visualization of molecular systems from static images to dynamic web displays and immersive platforms. While effective visualization is largely a solved problem, in this perspective we argue that the next significant leap lies beyond passive viewing. Rather, the frontier is in enabling intuitive, immersive, direct 3D manipulation and interaction with molecular systems to address the inherent limitations of carrying out complex 3D tasks via 2D interfaces. The vision, which is starting to be realized, is of immersive molecular science environments in which researchers leverage multi-modal inputs that feel natural (with hands plus possibly haptic or pseudo-haptic feedback, voice user interfaces and AI-based assistance), collaborating seamlessly in concurrent sessions, naturally visualizing and connecting different types of data and models, and engaging with molecules assisted by real-time simulation engines. Many of the technologies required to develop such environments already exist, at least in basic forms. In this Perspective, we discuss current prototypes and software solutions that incorporate some of the elements needed, and that are available for use. We discuss applications and practical demonstrations, and outline the developments that are required to make the Molecular Holodeck a reality. We also discuss challenges that need to be addressed in order to achieve this vision. The coming shift toward hands-on, multiuser, immersive, natural and physics-informed manipulation will transform hypothesis generation, molecular design, fundamental understanding, collaborative working, discussions, and thus research and education in the chemical sciences, as others have envisioned for "molecular visualization in the holodeck." Visualization ; Molecular graphics ; Leverage (statistics) ; Graphics ; Computer graphics ; Software ; Haptic technology ; Perspective (graphical) Crystallography and molecular interactions ; Teleoperation and Haptic Systems ; Supramolecular Chemistry and Complexes
Authors: Justin C. Hughes | Dylan J. York | Kevin G. Yager | Chinedum O. Osuji | Russell J. Composto
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends Integrating Flory–Huggins theory into a Bayesian optimization workflow enhances the objectivity of mapping phase behavior in polymer blends. Phase diagram ; Polymer blend ; Phase (matter) ; Diagram ; Bayesian probability Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Aolin Hou | Prashant Dhakal | Jingjing Qiu | Shiren Wang
Source: Journal of Neuromorphic Intelligence (article, 2026-03-23)
Field: Materials Science — Polymers and Plastics

&lt;b&gt;AI-&lt;/b&gt;&lt;b&gt;d&lt;/b&gt;&lt;b&gt;riven &lt;/b&gt;&lt;b&gt;S&lt;/b&gt;&lt;b&gt;elf-healing of &lt;/b&gt;&lt;b&gt;G&lt;/b&gt;&lt;b&gt;eopolymer &lt;/b&gt;&lt;b&gt;C&lt;/b&gt;&lt;b&gt;omposite for &lt;/b&gt;&lt;b&gt;E&lt;/b&gt;&lt;b&gt;xtreme &lt;/b&gt;&lt;b&gt;E&lt;/b&gt;&lt;b&gt;nvironments&lt;/b&gt;&lt;b&gt;&lt;/b&gt; Geopolymer composites are increasingly regarded as promising materials for infrastructure operating in extreme environments, including high-temperature, high-pressure, chemically aggressive, and multi-hazard conditions. However, passive durability alone is not sufficient to ensure long-term performance under such coupled extreme conditions. Once cracking occurs, transport pathways can rapidly form for fluids, ions, and reactive species, leading to permeability increase, loss of sealing integrity, structural degradation, and premature failure. This Perspective argues that the central challenge is therefore not simply to improve the intrinsic stability of geopolymers, but to design them for functional resilience through self-healing under coupled thermal, chemical, and pressure-driven stresses. We further contend that many current self-healing strategies remain overly generic and often fail to account for the distinct demands of different extreme environments. To address this gap, we propose a shift from empirical trial-and-error and forward property prediction toward physics-informed, mechanism-aware AI frameworks capable of inverse design, multimodal data integration, and digital-twin-assisted adaptation. Such an approach could enable geopolymer composites that are not only durable, but also damage-tolerant, recoverable, and better suited for long-term service in extreme environments. Resilience (materials science) ; Durability ; Property (philosophy) ; Perspective (graphical) ; Vulnerability (computing) ; Service (business) Polymer composites and self-healing ; Polymer Nanocomposites and Properties ; Microbial Applications in Construction Materials
Authors: Siyu Qin | Zeyuan Sun | Simiao Sun | J. Wang | Charleen M. Rahman | Thomas E. Gartner | Xuanhong Cheng | Elsa Reichmanis
Source: npj Biosensing (article, 2026-02-19)
Field: Materials Science — Polymers and Plastics

Organic mixed ionic-electronic conductor device platforms for emerging biosensor application Organic mixed ionic-electronic conductors are promising materials for next generation bioelectronic devices. While they readily interface with biological systems, the volumetric doping mechanism renders them susceptible to biofouling and operational instability. Thus, there is a materials science challenge requiring strategic focus on rational, molecular-level design of materials to control the complex bio-abiotic interface. Here, biofouling and operational instability are explored, and strategies developed to address these interconnected problems are traced. Biofouling ; Electrical conductor ; Interface (matter) ; Conductor ; Biosensor ; Conductive polymer Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Abdelouahad El Abbassi | Paul Christian Fürstenwerth | Christian Würth | Isabella Tavernaro | Ute Resch-Genger
Source: Analytical and Bioanalytical Chemistry (article, 2026-03-18)
Field: Materials Science — Surfaces, Coatings and Films

Validated workflows for preparing and characterizing core-stained and surface-labeled fluorescent polymer particles with simple commercial automation tools Preparation workflows of fluorescent nm- and µm-sized polymer particles used as reporters in fluorescent assays, bioimaging, and sensing studies or calibration tools for fluorescence methods in the life sciences can be time-consuming and labor-intensive. Also, the outcome can be operator dependent. Here, we present simple and cost-efficient automated workflows for dye loading and surface labeling of polystyrene particles (PSP), using a commercial self-programmable pipetting robot. For developing and fine-tuning automated staining workflows, hydrophobic Nile Red (NR) was incorporated into 100 nm, 200 nm, and 1000 nm PSP by an adapted swelling procedure of premanufactured particles in the presence of hydrophobic dyes, thereby confirming its reliability and versatility. Subsequent expansion of our automation concept to the labeling of carboxylated PSP with pH-sensitive 6-aminofluorescein (6-AMF) and aminated PSP with pH-responsive fluorescein isocyanate (FITC) demonstrated its broad applicability. All automated workflows were optimized and validated by gravimetry and spectroscopic measurements with a microtiter plate (MTP) reader in absorption and fluorescence mode to ensure particle recovery and reproducible fluorescence features and determine PSP dye loading and labeling efficiencies. Comparison with the manual fabrication of NR-stained PSP using an established swelling protocol showed that our automation approach utilizing a pipetting robot considerably reduced the variability in particle recovery and dye loading efficiency. Overall, our simple, labor- and time-efficient workflows with inexpensive and broadly available commercial automation tools present attractive alternatives to manual particle loading and labeling and provide the basis for fast parameter screening, parallel processing, and decreased hands-on time. Automation ; Fluorescence ; Particle (ecology) ; Workflow ; Polystyrene ; Polymer ; Pipette ; Swelling Polymer Surface Interaction Studies ; Biosensors and Analytical Detection ; Advanced Polymer Synthesis and Characterization
Authors: Brian Lamb | Simant R. Upreti | Yunfei Wang | Daniel Struble | Chenhui Zhu | Guillaume Freychet | Xiaodan Gu | Boran Ma
Source: Macromolecules (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (<i>R</i> <sup>2</sup> > 0.75), while AFM-based property predictions were less accurate (<i>R</i> <sup>2</sup> < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape. Interpretability ; Thin film ; Characterization (materials science) ; Block (permutation group theory) ; Convolutional neural network ; Copolymer ; Morphology (biology) ; Scattering Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Kevin Dedecker | Martin Drobek | Mikhael Bechelany | Anne Julbe
Source: ACS Applied Materials & Interfaces (article, 2026-02-16)
Field: Chemistry — Inorganic Chemistry

Rational Design of High-Quality ZIF-8 Membranes: Machine Learning-Guided Optimization of ALD ZnO Conversion Converting atomic layer deposited (ALD) ZnO thin films into high-quality zeolitic imidazolate framework-8 (ZIF-8) membranes poses significant challenges in identifying optimal synthesis conditions. This study employs a comprehensive machine learning approach to predict conversion outcomes based on 68 experimental conditions with varying solvent systems, temperatures, and reaction durations. We systematically evaluated 7 classification algorithms including k-nearest neighbors (k-NN), random forests, neural networks, and decision trees using stratified 10-fold cross-validation. The optimized k-NN classifier (<i>k</i> = 5) achieved 92.6% accuracy with a Kappa statistic of 0.791, demonstrating excellent discrimination between high- and low-quality membrane layer outcomes. Feature importance analysis identified the primary solvent as the most influential predictor, followed by temperature and reaction duration within specific regimes. Decision tree analysis further revealed a critical temperature threshold of 80 °C for methanol-based systems, below which extended reaction times are required. Application of Synthetic Minority Oversampling Technique (SMOTE) improved minority class detection while maintaining high specificity. The developed predictive framework enables the screening of conversion conditions with over 90% confidence, potentially reducing the number of experimental trials significantly while accelerating the discovery and optimization of ZIF-8 membrane fabrication protocols. This data-driven methodology provides a blueprint for extending machine learning-based optimization to other metal-organic framework systems and complex materials synthesis challenges. Decision tree ; Oversampling ; Rational design ; Atomic layer deposition ; Classifier (UML) ; Random forest ; Fabrication ; Statistic Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Covalent Organic Framework Applications
Authors: Ritesh Kumar | Ke-Hsin Wang | Chibueze V. Amanchukwu
Source: Molecular Systems Design & Engineering (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Using electrolyte solvent embeddings to guide battery electrolyte discovery Faithful reduced-dimensional solvent representations guide efficient exploration of electrolyte chemical space, accelerating the identification of high-performance solvents for lithium metal batteries. Electrolyte ; Battery (electricity) ; Solvent ; Space (punctuation) ; Organic solvent Machine Learning in Materials Science ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Roshan Patel | Mingxuan Li | Chin-Fei Chang | Louis de Lescure | Saeed Moayedpour | Paul Chauvin | Alan Cherney | Sven Jäger | Yasser Jangjou
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Distilling and exploiting quantitative insights from large language models for enhanced Bayesian optimization of chemical reactions Preference learning on LLM-completed surveys is used to distill their chemical insights into a utility function. The utility function is used to focus Bayesian optimization efforts, leading to significantly enhanced chemical reaction optimization. Bayesian optimization ; Bayesian probability ; Transfer of learning ; Bayesian statistics ; Bayesian inference ; Chemical process Machine Learning in Materials Science ; Machine Learning and Data Classification ; Asymmetric Hydrogenation and Catalysis
Authors: Mariya L. Ivanova | Michael E. R. Nicholls | Nicola Russo | Gueorgui Mihaylov | Konstantin Nikolić
Source: Advanced Intelligent Discovery (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions This review assesses the current state of predictive modeling for forecasting nanoparticles (NPs) in biomedical applications. Significant achievements have been realized through computational tools such as nano‐quantitative structure–activity relationship frameworks and molecular dynamics simulations. These methods successfully accelerate nanocarrier screening by predicting critical parameters, including protein corona formation, cellular uptake kinetics, and initial toxicity profiles. Despite these advances, the efficacy of current models is significantly constrained. A key challenge is the inability of simplified computational platforms to accurately replicate the immense complexity and dynamic nature of the in vivo environment. Furthermore, the development of robust predictive algorithms is hampered by a scarcity of large, standardized experimental datasets, leading to limited accuracy in modeling time‐dependent processes such as NP degradation and long‐term bioaccumulation. To overcome these hurdles, future efforts must prioritize the global adoption of standardized data generation protocols. The review urgently calls for integrating artificial intelligence and machine learning with systems biology approaches, alongside the creation of open‐access predictive databases. Ultimately, these collaborative efforts are essential to bridging the gap between materials science, clinical translation, and regulation, thus achieving predictive and safe NPs. Bridging (networking) ; Scarcity ; Computational model ; Replicate ; Key (lock) ; Predictive modelling ; Current (fluid) Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods
Authors: Nasrin Mohammadi | Max Dreger | Diego Collarana | Mohammad J. Eslamibidgoli | Kourosh Malek | M. Eikerling
Source: ChemRxiv (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

KNOWLEDGE GRAPH CONSTRUCTION FROM MATERIALS SCIENCE LITERATURE USING LARGE LANGUAGE MODELS AND ADVANCED DATA PREPROCESSING In this work, we present a pipeline for automated knowledge graph construction from materials science literature using large language models (LLMs). The proposed method performs entity and relationship extraction guided by a data model based on the logic of the Elementary Multiperspective Material Ontology (EMMO), structuring the output into a machine-interpretable graph format. The pipeline integrates several key components, including prompt-based extraction, a hierarchical chunking strategy that leverages document structure and section headers, and post-processing steps such as normalization, LLM-assisted deduplication, and alignment of node identifiers. A central focus of this study is the evaluation of different chunking strategies. Specifically we compare fixed-size splitting with a hierarchical chunking approach that incorporates document structure and header information. Our results show that hierarchical chunking consistently outperforms fixed-size chunking across both entity and relationship extraction tasks, achieving higher precision, recall, and F1 scores through more context-aware segmentation. Extracted entities and relationships are aligned with a curated ground truth dataset through manual verification to ensure semantic correctness. Overall, these findings indicate that LLMs, when combined with domain-specific ontological guidance and well-designed pre-and post-processing, can effectively extract high quality knowledge graphs from complex materials science literature. This benefits materials scientists and researchers by reducing manual curation effort and accelerating data-driven materials discovery. Chunking (psychology) ; Pipeline (software) ; Graph ; Preprocessor ; Knowledge extraction ; Knowledge base ; Information extraction ; Ground truth ; Header Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Amalya C. Johnson | Chris Fajardo | Leena Sansguiri | Weike Ye | Steven B. Torrisi
Source: arXiv (Cornell University) (article, 2026-03-06)
Field: Chemistry — Analytical Chemistry

Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. However, many spectroscopic techniques present challenges in development of interpretable, performant, and reliable supervised learning models due to the wide range of possible nonlinear correlations that can exist between the signal and the response variable (target) of interest. Here, we present Spectra-Scope, an open-source AutoML framework for automatic characterization of material properties from spectroscopy data using interpretable machine learning (ML) models. The software is implemented in Python and a no-code web application. It comprises tools for data preprocessing, nonlinear feature extraction, machine learning model training, and feature downselection. Users can easily train different types of simple, interpretable ML models on a set of feature transformations quickly and with modest computational resources. In this work, we outline the methods of Spectra-Scope and its effectiveness across diverse datasets, with applications to materials and agricultural spectroscopy data. We show that Spectra-Scope can reproduce performance of comparable models in the literature, and highlight how our emphasis on interpretability can be used to rationalize the behavior of individual models and understand the physical processes behind spectral features. Interpretability ; Python (programming language) ; Feature (linguistics) ; Spectral properties ; Feature selection ; Characterization (materials science) ; Nonlinear system ; Software ; Set (abstract data type) Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Remote Sensing in Agriculture