jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zijian Pan | Lyujie Wei | Tianying Wang | Zirui Zhao | Haifeng Li | Rundong Zhao
Source: AAPPS bulletin (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Machine learning for functional materials: insights from compositional, electronic, and dynamic processes Abstract Functional materials design frequently involves phenomena that span multiple length and time scales, where exhaustive exploration by first-principles calculations or experiments is impractical. Given the current capabilities of artificial intelligence, machine learning in this area typically serves less as a stand-alone predictor but more as a practical tool for guiding exploration within complex design spaces. After briefly reviewing key developments, this perspective adopts a case-driven approach and organizes machine learning applications in this field into three recurring levels: macroscopic compositional design, where the main challenge is combinatorial complexity; microscopic electronic and structural responses, where properties depend sensitively on subtle local variations; and dynamic or time-dependent processes, where behavior is governed by pathways or sequential signals. Representative examples show how each level benefits from different methodological choices and practical tools, ranging from data-driven screening and optimization to physics-aware surrogates and pathway-based or sequential learning. Examining these cases in parallel allows us to extract transferable principles, including physically informed feature design, data efficiency, and uncertainty awareness, while also clarifying the practical limitations of current approaches. Field (mathematics) ; Perspective (graphical) ; Key (lock) ; Feature (linguistics) ; Experimental data ; Current (fluid) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Block Copolymer Self-Assembly
Authors: Houssam Metni | Laura Ruple | Lauren N. Walters | Luca Torresi | Jonas Teufel | Henrik Schopmans | Jona Östreicher | Yumeng Zhang | Marlen Neubert | Yuri Koide | Kevin Steiner | Paul Link | Lukas Bär | M. Petrova | Gerbrand Ceder | Pascal Friederich
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Generative Models for Crystalline Materials Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery. Generative grammar ; Generative model ; Work (physics) ; Crystal (programming language) ; Software ; Scientific modelling Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Tomonori Saito | Nicholas J. Galan | Britney Baez | Ju-An Kim | Beibei Yao | Nduka D. Ogbonna | Dishner | Jared I. Bowman | Jesse McGaha | Ankit Shrivastava | Stephen DeWitt | Sheng Dai | Craig Bridges | Jeffrey C. Foster
Source: Research Square (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

AI-guided Autonomous Workflows Accelerate Kinetic Analysis of Polymer Depolymerization Depolymerization ; Workflow ; Kinetic energy ; Throughput ; Polymer ; Polymerization ; Work (physics) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Wenhao Xu | Jianhui Sang | Qidong Gong | Wenbin Lin | Zhihong Lin | Faheem Mushtaq | Hamza Mushtaq | Zhenyu Hong | Hong Zhao
Source: World Electric Vehicle Journal (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Development of COFs Materials as Solid Electrolytes for Lithium-Ion Batteries—A Mini Review Covalent organic frameworks (COFs) have emerged as promising candidates for solid-state electrolytes (SSEs) in lithium-ion batteries (LIBs) due to their tunable pore sizes, high surface areas, and exceptional thermal stability. However, the rational design of COF-based SSEs is hindered by the vast combinatorial chemical space, synthetic complexity, and the need for precise control over structure-property relationships. Machine learning (ML) has revolutionized the development of COF materials by enabling high-throughput screening, predictive modeling, and optimization of synthesis conditions. This review systematically explores the integration of ML in COF-based SSE development, focusing on structure prediction, synthesis-performance optimization, and the application of digital twin strategies. We highlight the role of ML in accelerating the discovery of high-performance COF-based solid-state electrolytes, optimizing ionic conductivity, and enhancing interfacial stability. By summarizing the synergistic pathways between computational simulations and experimental validation, this review offers strategic guidelines for overcoming traditional “trial-and-error” R&amp;D bottlenecks, paving the way for the next generation of high-energy-density LIBs. Surface modification ; Rational design ; Development (topology) ; Solid surface ; Ionic bonding Covalent Organic Framework Applications ; Advanced Battery Materials and Technologies ; Advanced battery technologies research
Authors: ماجستير الإرشاد النفسي، قسم العلوم النفسية والسلوكية، كلية الفنون والعلوم الإنسانية، جامعة جازان، المملكة العربية السعودية | الباحث/ مريسي الغامدي
Source: Academic Journal of Research and Scientific Publishing (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

استخدام الذكاء الاصطناعي لتوليد واختبار الفرضيات ذاتيا في التجارب العلمية (تطبيق عملي على تحسين شروط الإشابة في المواد شبه الموصلة) يقدم هذا البحث إطارًا حسابيًا تجريبيًا لدراسة وتحسين خصائص الإشابة في أشباه الموصلات عبر الدمج بين البيانات الاصطناعية، والنماذج البديلة، وخوارزميات التحسين. أُنشئت مجموعة بيانات اصطناعية من 2000 عيّنة تمثل تأثير ثلاثة معلمات أساسية هي: درجة الحرارة، وزمن المعالجة، وتركيز الشائبة، على مخرجات فيزيائية رئيسة تشمل مؤشر التجانس، وحركية الحاملات، وكثافة العيوب. جرى توليد هذه البيانات باستخدام علاقات شبه فيزيائية مع تضمين ضوضاء تجريبية لزيادة الواقعية. اعتمادًا على هذه البيانات، دُرّب نموذج بديل قائم على خوارزمية Random Forest حقق دقة عالية، ما أتاح استبدال المحاكاة الفيزيائية المكلفة ببيئة حسابية سريعة للاستكشاف والتحسين المتكرر. ثم استُخدمت خوارزمية Bayesian Optimization للبحث عن ظروف تشغيل محسّنة تحقق توازنًا بين تحسين التجانس وزيادة الحركية وتقليل العيوب، وأسفرت عن نطاقات تشغيل مثلى تقريبية عند درجات حرارة 845–860°م، وأزمنة 80–90 ثانية، مع تركيزات دوبانت مرتفعة. كما طُوّر نظام أولي لتوليد الفرضيات يعتمد آلية احتمالية تكرارية لاختبار مجموعات جديدة من المعلمات وتحديثها عبر النموذج البديل. ولتقييم الموثوقية، استُخدمت أساليب إحصائية متعددة وتحليل الحساسية ومحاكاة مونتِ كارلو، وأظهرت النتائج استقرارًا جيدًا واتساقًا في السلوك. يبرهن البحث أن هذا الدمج المنهجي يمثل نهجًا عمليًا يقلل الكلفة الحسابية والتجريبية ويمهّد لتطوير أطر متقدمة لدعم تصميم المواد وتحسين عملياتها. Bayesian probability ; Random forest ; Noise (video) ; Process (computing) ; Imprecise probability Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Infrared Thermography in Medicine
Authors: Louis Longley | Francisco Munguia-Galeano | Yushu Han | Rob Clowes | Sriram Vijayakrishnan | Adam Edwards | Gabriella Pizzuto | Hatem Fakhruldeen | Andrew Cooper
Source: ChemRxiv (article, 2026-01-12)
Field: Chemistry — Organic Chemistry

RobInHood: A Robotic Chemist in a Fume Hood Fume hoods protect chemists and the environment from hazardous vapours and airborne substances produced during experiments. They are standard in chemistry laboratories worldwide. However, fume hoods were designed for manual chemistry, and there are still relatively few robotic systems designed to operate within these inherently confined spaces. It is challenging to design robotic systems that can perform the same variety of operations within fume hoods that can be performed by a dexterous human chemist. Here, we present an automated platform comprising a robotic arm that can perform liquid handling, solid handling, capping/decapping, heating and stirring, filtration, and sample imaging within a standard laboratory fume hood (50 cm × 120 cm × 170 cm). The broad applicability of this system was demonstrated in two materials research problems (a dye-based porosity screening workflow and the synthesis of a porous organic cage) and in a pthalimide synthesis. The success of the synthesis workflows was validated offline by NMR, X-ray diffraction, mass spectrometry and FTIR. Workflow ; Hazardous waste ; Robotic arm ; Robotics ; Robot ; Variety (cybernetics) Supramolecular Chemistry and Complexes ; Covalent Organic Framework Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Haotong Liang | Yunlong Sun | Ryan Paxson | Chih‐Yu Lee | Alex T. Hall | Zoey Warecki | John Cumings | Hideomi Koinuma Hideomi Koinuma | A. Gilad Kusne | Mikk Lippmaa | Ichiro Takeuchi
Source: ArXiv.org (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Autonomous epitaxial atomic-layer synthesis via real-time computer vision of electron diffraction Autonomous science platforms which make decisions on the fly are fundamentally changing the outlook for materials development. AI-driven schemes can effectively reduce the total number of iterations needed to arrive at the best stoichiometry for desired properties or optimum synthesis parameters by significant margins. Here, we demonstrate real-time closed-loop autonomous navigation of a multi-dimensional synthesis parameter space for fabricating phase-pure epitaxial films of a metastable functional oxide phase using pulsed laser deposition. Sequential growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision of electron diffraction images at the unit-cell level. Our scheme regularly resulted in > 30-fold reduction in the number of experiments compared to comprehensive parameter-space mapping. The real-time workflow developed here can be readily extended to other thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous semiconductor manufacturing. Workflow ; Thin film ; Epitaxy ; Electron diffraction ; Setpoint ; Reflection (computer programming) ; Deposition (geology) ; Reduction (mathematics) ; Phase (matter) Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Vijaya Kumar Talari | Krishna Reddy K | Vamsi Ramkrishna M | Purushotham Theegala | Mukunda Vani M
Source:  (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

NANOTECHNOLOGY EMPOWERED BY ARTIFICIAL INTELLIGENCE: A STATE-OF-THE-ART REVIEW ON MATERIAL SYNTHESIS AND CHARACTERIZATION The convergence of artificial intelligence (AI) and nanotechnology is an emergent paradigm of materials science, especially with regards to the synthesis and characterization of materials. This review clarifies the fast-growing synergy of AI algorithms including machine learning (ML) model or deep neural networks, are accelerating the discovery and optimization of nanomaterials more than ever before. Synthesizing AI-based methods, including generative adversarial networks and reinforcement learning, allow the logical programming of the nanostructures so that experiments take days instead of years. Properties, such as conductivity, biocompatibility, and catalytic activity, can be engineered. As an example, carbon-based nanomaterials and metal-organic structures have been simplified through data-driven platforms and can be scaled up to be applicable in the industry. Under characterization, AI has demonstrated itself to excel in the processing of large datasets based on such techniques as X-ray diffraction, Raman spectroscopy, and electron microscopy, and uses convolutional neural networks to automatically detect defects, identify phases, and predict properties with over 95% accuracy. This synthesis does not only mitigate human biases but also reveals concealed correlations in complicated nanoscale processes, paving the way to breakthroughs in energy storage, nanomedicine and environmental remediation. Alongside these developments, there are still problems such as the lack of data and the possibility of interpreting AI-driven experiments and ethical issues. In the future, AI-quantum computing systems will be optimistic and will transform the operation of molecular simulations, leading to sustainable, next-generation nanomaterials. Key words: Artificial Intelligence, Nanotechnology, Material Synthesis, Characterization Techniques, Machine Learning. Characterization (materials science) ; Deep learning ; Key (lock) ; Artificial neural network ; Convolutional neural network ; Nanomedicine ; Reinforcement learning Machine Learning in Materials Science ; Advanced Technologies and Applied Computing ; Big Data and Digital Economy
Authors: Xufeng Hong | Xizhe Wang | Stephen J. Harris | Hongbo Zhao | Jiashen Meng | Qingshan Jia | Qianchuan Zhao | Kang Xu | Quanquan Pang | Benben Jiang
Source: Nature Communications (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Deep active learning and knowledge transfer for rapid discovery of lithium metal battery electrolytes Designing electrolyte materials for high-energy lithium metal batteries requires navigating vast, discrete chemical spaces, where intricate interphasial and electrolyte chemistries render component interactions largely unclear. Traditional trial-and-error methods struggle with discontinuous electrolyte-performance relationships and inefficient adaptation to new molecular candidates, hindering discovery. Here, we propose a two-stage deep active learning framework with knowledge transfer for rapid electrolyte design. In stage one, deep active learning with deep kernel learning selects informative experiments and models discontinuous relationships between formulation and performance, improving sample efficiency and reducing experimental cost. In stage two, target statistic coding quantifies what was learned and transfers it to new design settings, such as expanded formulation spaces and newly introduced components, using only a small number of additional measurements. Using this framework, we found electrolytes that increase the average lifetime of lithium metal symmetric cells by threefold after three learning iterations, and we rapidly identified improved formulations for Li<sup>0</sup> | |LiNi<sub>0.8</sub>Co<sub>0.1</sub>Mn<sub>0.1</sub>O<sub>2</sub> full cells in expanded chemical spaces. This work provides an experiment-driven, sample-efficient route to explore complex electrolyte formulation spaces and quantify inter-component correlations, as well as a realistic, high-cost, small-data benchmark for probabilistic surrogate modeling and sequential decision-making in discrete chemical spaces. Electrolyte ; Transfer of learning ; Lithium (medication) ; Deep learning ; Probabilistic logic ; Active learning (machine learning) ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Connor Ganley | Yuqi Feng | Himadri S. Karmakar | Howard E. Katz | Paulette Clancy
Source: Polymer Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Bayesian approach providing design choices and chemical insight for solution-processed thermoelectric polymers Physics-informed Bayesian optimization reveals insights into solution-processed planar conjugated semiconducting polymer design. Bayesian optimization ; Bayesian probability ; Polymer ; Planar ; Conjugated system ; Thermoelectric effect Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials
Authors: Nooshin Zeinali Galabi | Chenghao Liu | Moksh Jain | Marc Kamel | Shipeng Jia | Yoshua Bengio | Eric McCalla
Source: Advanced Materials (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Navigating Ternary Doping in Li‐ion Cathodes With Closed‐Loop Multi‐Objective Bayesian Optimization To further improve secondary battery materials, we are increasingly exploring highly complex composition spaces in attempts to optimize multiple properties simultaneously. While our past work has done this in systematic manners using high-throughput experimentation, the exponential increase in the search space with triple doping makes grid search prohibitively expensive. Here, we demonstrate a closed-loop, multi-objective machine learning approach to guide the high-throughput workflow to efficiently navigate a space with approximately 14 million unique combinations. The test system is LiCoPO<sub>4</sub>, which we have previously explored using systematic codoping that was effective in optimizing one property only: energy density. To learn multiple electrochemical metrics, we first pretrain a set transformer on the public Materials Project database as a feature extractor, then attach a multi-task Gaussian process head and finetune the entire model on our high-throughput data. Through 3 rounds of active learning, we demonstrate that with a very small number of samples (as few as 125 random compositions and 63 predicted), we are able to simultaneously optimize four key electrochemical properties. Relative to the undoped system, the best composition raises our composite figure of merit up to five times. This establishes an end-to-end workflow for accelerated battery materials design to be used in the rapidly growing field of autonomous materials discovery. Workflow ; Bayesian optimization ; Ternary operation ; Gaussian process ; Cathode ; Grid ; Battery (electricity) ; Figure of merit ; Transformer ; Process (computing) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Big Data and Digital Economy
Authors: Tian Tian | Yinyin Bao
Source: Macromolecular Rapid Communications (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers Light-emitting polymers (LEPs) combine the luminescent properties of organic emitters with the structural versatility of polymers, supporting applications in solid-state display, chemical sensing, and bioimaging, owning to the efficient tuning of their performance across multiple scales, from monomer units and chain sequence to solid-state packing and solution processing. Recent strategies have expanded emission color space, improved quantum yields, and simplified design rules, evolving from traditional π-conjugated systems to mechanisms driven by aggregation and charge transfer. Yet this multiscale flexibility also creates a vast and complex design space, where the interplay of monomer choice, polymer architecture, and processing methods makes it impossible to exhaustively map their structure-property relationships by empirical means. In this perspective, we review the development of recent design strategies in LEPs, highlighting the key experimental challenges they reveal, and discuss how data-driven approaches, particularly machine learning, can help navigate this complexity and accelerate the discovery and optimization of next-generation LEPs. Flexibility (engineering) ; Set (abstract data type) ; Key (lock) ; Polymer ; Sequence (biology) ; Monomer Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics ; Organic Light-Emitting Diodes Research
Authors: Intazar Hussain Shah | Iqra Anjum | Laiba Javed | Waqar Yousaf | Irsa Liaqat | Muhammad Hasnain Khan | Laiba Attique | Waheed Zaman Khan
Source: Scholars International Journal of Chemistry and Material Sciences (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

A Cross-Scale Design Grammar for Functional Matter: Integrating Synthetic Pathways, Reaction Mechanisms, Self-Assembly, Interfaces, Defect Chemistry, Multimodal Characterization, Predictive Modeling, and Performance Validation Functional matter is often designed and reported as disconnected advances in synthesis, structure, and performance, which limits transferability across materials classes and length scales. This review introduces a cross-scale design grammar that unifies how researchers specify, test, and validate causal links from processing and reactions to architecture, interfaces, defects, and device-level outcomes. We formalize grammar units as controllable operators, interaction rules, constraints, and measurable metrics, and show how uncertainty and failure modes propagate along the synthesis to structure to function chain. The framework integrates mechanistic control across solution, solid-state, vapor, electrochemical, and mechanochemical routes; mesoscale assembly and hierarchical architectures; interfaces and interphases as transport gatekeepers; and defect chemistry as both a performance lever and a degradation driver. We then treat multimodal characterization as an inference and evidence-fusion problem and map it onto a modeling ladder that spans mechanistic, continuum, statistical, and hybrid approaches with uncertainty quantification and validation. Finally, we provide benchmarking and reporting templates, truth-table criteria for what counts as improvement, and case-study scorecards that identify the dominant bottlenecks in representative applications. By converting fragmented knowledge into a reusable grammar, this review offers a practical, end-to-end playbook for designing functional matter with measurable causality, reproducibility, and observation-ready. Toolbox ; Benchmarking ; Grammar ; Inference ; Robustness (evolution) ; Transferability ; Function (biology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Modular Robots and Swarm Intelligence