jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying Shen | Shichao Zhao | Yanfei Lv | Fei Chen | Li Fu | Hassan Karimi‐Maleh
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Large Language Model-Driven Knowledge Discovery for Designing Advanced Micro/Nano Electrocatalyst Materials This review presents a comprehensive and forward-looking analysis of how Large Language Models (LLMs) are transforming knowledge discovery in the rational design of advanced micro/nano electrocatalyst materials. Electrocatalysis is central to sustainable energy and environmental technologies, but traditional catalyst discovery is often hindered by high complexity, fragmented knowledge, and inefficiencies. LLMs, particularly those based on Transformer architectures, offer unprecedented capabilities in extracting, synthesizing, and generating scientific knowledge from vast unstructured textual corpora. This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks, including automated information extraction from literature, text-based property prediction, hypothesis generation, synthesis planning, and knowledge graph construction. We comparatively analyze leading LLMs and domain-specific frameworks (e.g., CatBERTa, CataLM, CatGPT) in terms of methodology, application scope, performance metrics, and limitations. Through curated case studies across key electrocatalytic reactions—HER, OER, ORR, and CO2RR—we highlight emerging trends such as the growing use of embedding-based prediction, retrieval-augmented generation, and fine-tuned scientific LLMs. The review also identifies persistent challenges, including data heterogeneity, hallucination risks, lack of standard benchmarks, and limited multimodal integration. Importantly, we articulate future research directions, such as the development of multimodal and physics-informed MatSci-LLMs, enhanced interpretability tools, and the integration of LLMs with self-driving laboratories for autonomous discovery. By consolidating fragmented advances and outlining a unified research roadmap, this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies. Nano- ; Nanotechnology ; Electrocatalyst ; Computer science ; Engineering ; Materials science ; Chemistry ; Chemical engineering ; Electrochemistry Machine Learning in Materials Science
Authors: Yu‐chen Karen Chen‐Wiegart | N. Huber | Kevin G. Yager
Source: MRS Bulletin (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Machine learning approaches for intentional materials engineering Computer science ; Materials science ; Artificial intelligence Nanoporous metals and alloys ; Anodic Oxide Films and Nanostructures ; Aluminum Alloys Composites Properties
Authors: Daniel Hollarek | Henrik Schopmans | Jona Östreicher | Jonas Teufel | Bin Cao | Adie Alwen | Simon Schweidler | Mriganka Singh | Tim Kodalle | Hanlin Hu | Gregoire Heymans | Maged Abdelsamie | Arthur Hardiagon | Alexander Wieczorek | Siarhei Zhuk | Ruth Schwaiger | Sebastian Siol | François‐Xavier Coudert | M. Wolf | Carolin M. Sutter‐Fella | Ben Breitung | Andrèa M. Hodge | Tong‐Yi Zhang | Pascal Friederich
Source: Advanced Intelligent Discovery (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

opXRD: Open Experimental Powder X‐Ray Diffraction Database Powder X‐ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high‐throughput discovery in self‐driving labs. Machine learning promises to resolve this bottleneck by enabling automated powder diffraction analysis. A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low‐quality experimental patterns with high noise levels and elevated backgrounds. With the Open Experimental Powder X‐ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, for example, through transfer learning methods. We collected 92,552 diffractograms, 2179 of them labeled, from a wide spectrum of material classes. We hope this ongoing effort can guide machine learning research toward fully automated analysis of pXRD data and thus enable future self‐driving materials labs. Powder diffraction ; Bottleneck ; Diffraction ; Materials science ; Computer science ; Experimental data ; Artificial intelligence ; Crystallography ; Optics ; Physics ; Chemistry ; Mathematics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Rohan Chand Sahu | Sanchit Arora | Dinesh Kumar | Ashish Kumar Agrawal
Source: Med Research (article, 2025-11-17)
Field: Materials Science — Biomaterials

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery ABSTRACT The integration of artificial intelligence (AI), and machine learning (ML) with nanomedicine represents a transformative approach in cancer drug delivery, offering solutions to long‐standing challenges such as formulation optimization, targeted delivery, and predictive efficacy. While nanocarriers like liposomes, dendrimers, and polymeric nanoparticles have enhanced site‐specific delivery, their development remains constrained by trial‐and‐error formulation methods. In this review, we critically examine how ML algorithms, including support vector machines, artificial neural networks, and deep learning models which are widely being applied to streamline nanoparticle design, predict drug release profiles, and personalize therapeutic regimens based on patient data. We highlight case studies where ML has accelerated nanoparticle fabrication, improved internalization modeling, and supported the development of self‐driving labs for formulation screening. We also discuss computational workflows, data handling strategies, and the operational principles behind commonly used ML models in pharmaceutical research. Importantly, the review addresses key limitations such as data scarcity, lack of model interpretability, and regulatory hurdles that hinder clinical translation. By synthesizing recent advances and identifying ongoing gaps, this article offers a roadmap for future interdisciplinary research at the interface of AI and nanomedicine. Transformative learning ; Nanocarriers ; Nanomedicine ; Artificial neural network ; Deep learning ; Support vector machine ; Drug delivery Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Computational Drug Discovery Methods
Authors: Nis Fisker‐Bødker | Daniel Persaud | Yang Bai | Mark Kozdras | Tejs Vegge | Jason Hattrick‐Simpers | Jin Hyun Chang
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Electrochemistry

AMPERE-2: an open-hardware, robotic platform for automated electrodeposition and electrochemical validation AMPERE-2 automates electrodeposition and in situ OER catalyst testing. The open-hardware workflow yields reproducible multi-metal catalysts in 65 min per sample. Computer science ; Computer hardware ; Embedded system Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Brenden G. Pelkie | Chi Yuet Yung | Zachery R. Wylie | Lilo D. Pozzo
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated sol–gel synthesis of nanoporous silica <i>via</i> integrated small angle X-ray scattering with an open-source automation platform An open-hardware automated workflow for mesoporous colloidal silica synthesis is developed and applied to study a compositional parameter space. Nanoporous ; Open source ; Automation ; Small-angle X-ray scattering ; Scattering ; Materials science ; Computer science ; Nanotechnology ; Optics ; Physics ; Engineering ; Mechanical engineering ; Operating system ; Software Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science
Authors: Markus Stricker | Lars Banko | Nik Sarazin | Niklas Siemer | Jan Janßen | Lei Zhang | Jörg Neugebauer | Alfred Ludwig
Source: npj Computational Materials (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Computationally accelerated experimental materials characterization—drawing inspiration from high-throughput simulation workflows Abstract Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of ‘machine learning’ in materials discovery campaigns. The benefits including automation, reproducibility, data provenance, and reusability of managed data, however, are not widely available in the experimental domain. We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided through active learning, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings. With data from all domains in the same framework, an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available. Workflow ; Acceleration ; Characterization (materials science) ; Experimental data ; Interface (matter) ; Reusability Machine Learning in Materials Science ; Scientific Computing and Data Management ; X-ray Diffraction in Crystallography
Authors: Nicola L. Bell | Marina Gladkikh | Cameron Fraser | Mostafa Elsayed | Emma Richards | R. Turnbull
Source: Angewandte Chemie International Edition (article, 2025-06-03)
Field: Chemistry — Organic Chemistry

A Cobotic, Digitally Controlled Schlenk‐line Unlocks Access to Elusive Lewis‐Base Stabilised Copper Bis(Disilylamides) Silylamides are important ligands in coordination chemistry for their ability to stabilise low coordination numbers and provide soluble, and even volatile, metal complexes. Such compounds provide valuable insights into the fundamental bonding and reactivity of their respective metals. Despite the wealth of homo- and heteroleptic hexamethyldisilazide complexes of divalent 3d ions (Sc-Ni, Zn), attempts to access the corresponding divalent copper complexes have yielded only Cu(I) species. Herein, we demonstrate the stabilisation and isolation of a formally Cu(II) bis-hexamethyldisilazide which was achieved by implementing novel digital chemistry tools. In order to successfully isolate (DMAP)Cu<sup>II</sup>(N{SiMe<sub>3</sub>}<sub>2</sub>)<sub>2</sub> (DMAP = N,N-dimethylaminopyridine), we investigated the roles of the co-ligand and silylamide transfer reagent in the kinetics of its formation. Crucial to these studies was our newly developed "cobotic" Schlenk line which provides digital control of the atmosphere under which we conduct our highly reactive syntheses. In digitising Schlenk-line handling, we have improved synthetic productivity by creating protocols for automated inertisation, solvent evaporation, liquid handling and crystallisation all while capturing reaction log data. Importantly, our Cu silylamide synthesis provides a case study showing that our cobotics approach allows for the discovery and isolation of unstable species which may remain elusive by traditional manual or fully autonomous methodologies. Copper ; Lewis acids and bases ; Base (topology) ; Line (geometry) ; Chemistry ; Polymer chemistry ; Organic chemistry ; Geometry ; Mathematics Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry ; Synthetic Organic Chemistry Methods
Authors: César E. Ramírez | Eman Ahmed | Elena Di Mare | Maria Pineiro-Goncalves | A. J. Maroulis | Prajakatta Mulay | D. C. Radford | Adam J. Gormley
Source: ACS Polymers Au (article, 2025-08-28)
Field: Chemistry — Organic Chemistry

Automation-Assisted Photoinduced Atom Transfer Radical Polymerization Oxygen-tolerant reversible-deactivation radical polymerizations (RDRP) now allow many of these reactions to proceed in open labware, such as well plates. This enables the high-throughput synthesis of tailored polymers and lowers the knowledge barrier required to obtain these materials. Building on our previous work automating photoinduced electron/energy transfer reversible addition-fragmentation chain transfer (PET-RAFT) and enzyme-assisted RAFT (Enz-RAFT) polymerization, we now introduce automated atom transfer radical polymerization (ATRP). Here, we demonstrate the potential of this platform for the high-throughput optimization of ATRP chemistry. Furthermore, we demonstrate that this workflow can help provide insights into the selection of reaction components, such as ligands and initiators, for the polymerization of kinetically difficult monomers such as methyl methacrylate with smaller rates of propagation than acrylates. This coupling paves the way for data-driven optimization of ATRP reactions, accelerated by the generation of high-throughput data sets. To facilitate the integration of robotics for high-throughput applications in polymer synthesis and optimization of photo-ATRP, we have made a Python package available to assist with experimental planning. The tool accepts Excel sheets with user-defined molar ratios, target monomer concentrations, and reagent stock concentrations and outputs an Excel sheet with actionable recipes that can be readily implemented in liquid handling transfer steps via manual or automated pipetting. Atom-transfer radical-polymerization ; Polymerization ; Automation ; Radical polymerization ; Living free-radical polymerization ; Transfer (computing) ; Photochemistry ; Chemistry ; Polymer chemistry ; Materials science ; Computer science ; Engineering ; Organic chemistry ; Polymer ; Mechanical engineering Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Photopolymerization techniques and applications
Authors: Ryo Toyama | Yuma Iwasaki | Prabhanjan D. Kulkarni | Hirofumi Suto | Tomoya Nakatani | Yuya Sakuraba
Source: npj Computational Materials (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

High-throughput materials exploration system for the anomalous Hall effect using combinatorial experiments and machine learning Abstract The development of new materials exhibiting large anomalous Hall effect (AHE) is essential for realizing highly efficient spintronic devices. However, this development has been a time-consuming process due to the combinatorial explosion for multielement systems and limited experimental throughput. In this study, we identify new materials exhibiting large AHE in heavy-metal-substituted Fe-based alloys using a high-throughput materials exploration method that combines deposition of composition-spread films using combinatorial sputtering, photoresist-free facile multiple-device fabrication using laser patterning, simultaneous AHE measurement of multiple devices using a customized multichannel probe, and prediction of candidate materials using machine learning. Based on experimental AHE data on Fe-based binary system alloyed with various single heavy metals, we perform machine learning analysis to predict the Fe - based ternary system containing two heavy metals for larger AHE. We experimentally confirm larger AHE in the predicted Fe–Ir–Pt system. Using scaling analysis, we reveal that the enhancement of AHE originates from the extrinsic contribution. Throughput ; Computer science ; Artificial intelligence ; Machine learning ; Telecommunications Machine Learning in Materials Science ; Magnetic properties of thin films ; Surface and Thin Film Phenomena
Authors: Sumner B. Harris | Roosevelt Fajardo | Alexander A. Puretzky | Kai Xiao | Feng Bao | Rama K. Vasudevan
Source: Nano Letters (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable real-time control of thin film synthesis by combining <i>in situ</i> optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD). We validated the approach using simulated and experimental reflectivity data for WSe<sub>2</sub> growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T'-MoTe<sub>2</sub>. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of <i>in situ</i> diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states. Kinetics ; Bayesian probability ; Thin film ; Estimation ; Materials science ; Computer science ; Nanotechnology ; Biological system ; Artificial intelligence ; Engineering ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Memory and Neural Computing
Authors: Ian Houson | Humera Siddique | Magdalene W. S. Chong | Murray N. Robertson | Alice Turner | Alison Nordon | Amal Osman | Amparo Galindo | Andrew S. Dunn | Blair T. Johnson | Brahim Benyahia | Cameron J. Brown | Chantal L. Mustoe | C. Price | Chris D. Reilly | Claire S. Adjiman | George Jackson | Elke Prasad | Gavin Halbert | Helen Feilden | Ján Šefčı́k | John McGinty | John Robertson | Ken Smith | Mais Al-Attili | Mark McGowan | Mariam Siddique | Momina Pathan | Nazer Rajoub | N. Gregory Hamilton | Rachel Gallant Feeney | Scott H. Brown | Stephanie J. Urwin | Thomas Bernet | Thomas Pickles | Wei Li | Alastair J. Florence
Source: International Journal of Pharmaceutics (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Quality by digital design in action: a workflow for crystallisation and isolation This paper exemplifies a Quality by Digital Design (QbDD) workflow for the crystallisation and isolation of active pharmaceutical ingredients (APIs). QbDD uses a digital first approach to improve manufacturability and sustainability whilst assuring product quality within practical constraints. This study uses three exemplar compounds (ibuprofen, lamivudine, and AZD0837), each of which presents a different challenge for crystallisation; these include agglomeration, solid state form, and slow growth rates, respectively. These cases are used to evaluate the benefits of the QbDD approach and identify gaps for future research. Results of this work show that the QbDD workflow reduces the number of physical experiments by 28% and the API material usage by 52-65% when compared to comparable API development processes not using this approach. This approach provides a route to practically implement and exploit the benefits of digital tools and overcome digital skill shortages. By exploiting digital tools for process simulation and optimisation, the workflow improves efficiency, even in complex cases where multiple workflow iterations are required. This workflow, therefore, paves the way for more sustainable and cost-effective API production and it promotes future standardisation of digital design in pharmaceutical development. Workflow ; Design for manufacturability ; Quality (philosophy) ; Workflow technology ; Exploit ; Process (computing) ; Quality by Design ; Isolation (microbiology) Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Massa Zahdeh | Rafik Karaman
Source: Molecules (article, 2025-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gold Nanoparticles in Biomedical Applications: Synthesis, Functionalization, and Recent Advances This review demonstrates the evolution of AuNPs in biomedicine from traditional nanoparticles to sophisticated multifunctional nanosystems. To the best of our knowledge, this is the first assessment that explicitly contrasts sophisticated AuNP techniques with conventional procedures in biomedical applications. Colloidal gold ; Biomedicine ; Nanoparticle ; Nanomedicine Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Amit Kumar Mishra | Yonglan Kan
Source: International Journal of Artificial Intelligence for Science (IJAI4S) (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

AI Meets Chemistry: Unlocking New Frontiers in Molecular Design and Reaction Prediction Artificial intelligence (AI) is revolutionising chemical research by significantly enhancing automated experimental processes, reaction prediction, and molecular design. Despite these advances, problems with data quality, computational resource limitations, and model interpretability persist. In order to further speed up chemical discoveries and advances, prospects include creating hybrid AI models, quantum AI, and multimodal frameworks. Using artificial intelligence (AI) to significantly improve automated experimental procedures, reaction prediction, and molecular design is revolutionising chemical research. Creating hybrid AI models, quantum AI and multimodal frameworks is a potential future avenue to accelerate chemical discoveries and further advances. Chemical research is being revolutionised by artificial intelligence (AI), which is significantly enhancing automated experimental procedures, reaction prediction, and molecular design. Recent advances in generative AI methods, such as diffusion models, GANs, and variational autoencoders (VAEs) that aid in creating unique molecular structures, are the main focus of this review effort. To increase the precision of reaction predictions, transformer-based designs and graph neural networks (GNNs) are being investigated. Some challenges remain, including low-quality data, a lack of processing capacity, and concerns over the model’s interpretability. The creation of hybrid AI models, quantum AI, and multimodal frameworks, among other exciting study topics, could accelerate future developments in chemistry. Computer science ; Biochemical engineering ; Chemistry ; Management science ; Data science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods