jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexey Sanin | Jackson Flowers | Tobias H. Piotrowiak | Frederic Felsen | LEON MERKER | Alfred Ludwig | Dominic Bresser | Helge Sören Stein
Source: KITopen (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes High-performance batteries need accelerated discovery and optimization of new anode materials. Herein, we explore the Si─Ge─Sn ternary alloy system as a candidate fast-charging anode materials system by utilizing a scanning droplet cell (SDC) as an autonomous electrochemical characterization tool with the goal of subsequent upscaling. As the SDC is performing experiments sequentially, an exploration of the entire ternary space is unfeasible due to time constraints. Thus, closed-loop optimization, guided by real-time data analysis and sequential learning algorithms, is utilized to direct experiments. The lead material identified is scaled up to a coin cell to validate the findings from the autonomous millimeter-scale thin-film electrochemical experimentation. Explainable machine learning (ML) models incorporating data from high-throughput Raman spectroscopy and X-ray diffraction (XRD) are used to elucidate the effect of short and long-range ordering on material performance. Anode ; Characterization (materials science) ; Ternary operation ; Battery (electricity) ; Electrochemistry ; Raman spectroscopy Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Ying Zhao | Guanhua Chen | Jie Liu
Source: ArXiv.org (preprint, 2025-05-15)
Field: Materials Science — Materials Chemistry

Polymer Data Challenges in the AI Era: Bridging Gaps for Next-Generation Energy Materials The pursuit of advanced polymers for energy technologies, spanning photovoltaics, solid-state batteries, and hydrogen storage, is hindered by fragmented data ecosystems that fail to capture the hierarchical complexity of these materials. Polymer science lacks interoperable databases, forcing reliance on disconnected literature and legacy records riddled with unstructured formats and irreproducible testing protocols. This fragmentation stifles machine learning (ML) applications and delays the discovery of materials critical for global decarbonization. Three systemic barriers compound the challenge. First, academic-industrial data silos restrict access to proprietary industrial datasets, while academic publications often omit critical synthesis details. Second, inconsistent testing methods undermine cross-study comparability. Third, incomplete metadata in existing databases limits their utility for training reliable ML models. Emerging solutions address these gaps through technological and collaborative innovation. Natural language processing (NLP) tools extract structured polymer data from decades of literature, while high-throughput robotic platforms generate self-consistent datasets via autonomous experimentation. Central to these advances is the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles, adapted to polymer-specific ontologies, ensuring machine-readability and reproducibility. Future breakthroughs hinge on cultural shifts toward open science, accelerated by decentralized data markets and autonomous laboratories that merge robotic experimentation with real-time ML validation. By addressing data fragmentation through technological innovation, collaborative governance, and ethical stewardship, the polymer community can transform bottlenecks into accelerants. Bridging (networking) ; Interoperability ; Metadata ; Merge (version control) ; Data sharing ; Scripting language ; Forcing (mathematics) ; Data access Machine Learning in Materials Science
Authors: Stephen G. Dale | N. Kazeev | Alastair J. A. Price | Víctor Posligua | Stephan Roche | O. Anatole von Lilienfeld | Kostya S. Novoselov | Xavier Bresson | Gianmarco Mengaldo | Xudong Chen | T. Okane | Emily R. Lines | Matthew J. Allen | Amandine E. Debus | Clayton Miller | Jiayu Zhou | Hiroko H. Dodge | D. Rousseau | A. Ustyuzhanin | Zonghuan Yan | Mario Lanza | Fabio Sciarrino | Ryo Yoshida | Zhidong Leong | Teck Leong Tan | Qianxiao Li | Adil Kabylda | Igor Poltavsky | Alexandre Tkatchenko | Sherif Abdulkader Tawfik | P.S. Kamath | Theo Jaffrelot Inizan | Kristin A. Persson | Bryant Y. Li | Vir Karan | Chenru Duan | Haojun Jia | Qiyuan Zhao | Hiroyuki Hayashi | Atsuto Seko | Isao Tanaka | Omar M. Yaghi | Tim Gould | Bun Chan | Stefan Vuckovic | T. S. Li | Min Lin | Zichao Tang | Yang Li | Yong Xu | Amrita Joshi | Xiaonan Wang | Leonard W. T. Ng | Sergei V. Kalinin | Mahshid Ahmadi | Jiyizhe Zhang | Shuyuan Zhang | Alexei A. Lapkin | Ming Xiao | Zhe Wu | Kedar Hippalgaonkar | Limsoon Wong | Lorenzo Bastonero | Nicola Marzari | Dorye Luis Esteras Cordoba | Andrei Tomut | Andreus Cristhian Linhares Andrade | Jose-Hugo Garcia
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments. Trustworthiness ; Generative grammar ; Workflow ; Chart ; Scientific discovery ; Generative model Machine Learning in Materials Science ; Scientific Computing and Data Management ; Neural Networks and Reservoir Computing
Authors: Sang Soo Han | H.J. Lee | Daeho Kim | Hyein Lee | Namyoung Gwak | Nayeon Kim | Hyuk Jun Yoo | Taekyung Yu | Nuri Oh | Seok Su Sohn
Source: Research Square (preprint, 2025-07-29)
Field: Materials Science — Materials Chemistry

You Only Put Your Nanoparticle: A Fully Automated System for Nanoparticle Washing Enabled by Vision and Language AI Nanoparticle ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Biosensors and Analytical Detection ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Andrew Ritchhart | Sarah I. Allec | Heather Job | Pravalika Butreddy | Maxim Ziatdinov | Elias Nakouzi
Source: APL Machine Learning (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Toward autonomous materials synthesis via reaction–diffusion coupling Autonomous experimentation presents enormous opportunities for materials synthesis under far-from-equilibrium conditions due to the vast parameter space that can be controlled, the complexity of the emerging products, and the potential for machine learning to drive synthesis beyond traditional methods. This work presents important steps toward such autonomous materials synthesis via reaction–diffusion coupling. As a model system, we used the periodic precipitation of copper(II) hydroxide and targeted the synthesis of “Liesegang” precipitation bands with a well-defined spacing. Our workflow involved conducting high-throughput synthesis experiments, mapping the spatial distribution of the products as a function of initial reactant concentrations, and scalarizing the emerging patterns using empirical scaling laws. Subsequently, machine learning models were deployed to process the scalarized descriptors and inform the next round of experimental conditions in order to converge toward a target precipitation pattern, without human input. We anticipate that these results open pathways for autonomous materials synthesis via reaction–diffusion coupling to create complex products with user-defined chemistry, morphology, and spatial distribution within a reaction medium. Reaction–diffusion system ; Coupling reaction ; Coupling (piping) ; Diffusion ; Materials science ; Computer science ; Biological system ; Chemistry ; Physics ; Composite material ; Thermodynamics ; Catalysis ; Organic chemistry ; Biology Machine Learning in Materials Science ; Functional Brain Connectivity Studies ; Advanced Memory and Neural Computing
Authors: Lauren N. Walters | Matthew J. McDermott | Amalie Trewartha | Steven B. Torrisi | Joseph H. Montoya | Anubhav Jain | Gerbrand Ceder
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Calibrating Thermodynamic Models to Experimental Conditions for Accurate Solid State Synthesis Prediction Accurate, calculated formation energies are necessary for predictive modeling of solid state synthesis of energy materials. Previous studies have extensively benchmarked workflows and corrections to 0K enthalpies of formation generated from first principles calculations. However, little is reported on standards and errors of calculated free energies of formation, and associated critical reaction temperatures, for modeling solid state synthesis. Here, we present methods and considerations for constructing a “virtual furnace”. We fit solid phase enthalpies from density functional theory and common gaseous atmospheres for finite temperature models. Errors associated with critical thermodynamic stability changes are benchmarked for different adjustment schema. We validate our choice of thermodynamic models through comparison with solid state electrolyte synthesis results within our self-driving laboratory, A-Lab. Application of our models to carefully selected redox experiments allow us to explore the possibility of required underpotentials for achieving reduction. Our methods unite traditional Gibbs reaction energy, grand potential diagram, and Ellingham diagram formalism to (automated) experimental materials formation to enable a future of optimizable synthesis-by-design of battery materials. Gibbs free energy ; Phase diagram ; Formalism (music) ; Solid-state ; Stability (learning theory) ; Standard enthalpy of formation ; Thermodynamic process ; Energetic material ; Electrolyte Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced battery technologies research
Authors: Kedar Hippalgaonkar
Source:  (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

AI for Materials Discovery Artificial Intelligence (AI) is revolutionizing materials discovery by accelerating the design, synthesis, and characterization of novel materials with targeted properties. By leveraging machine learning models trained on experimental and computational datasets, AI enables predictive design, structure-property correlation mapping, and autonomous experimentation. Recent advancements, including generative models, graph neural networks, and large language models, have enhanced our ability to explore complex chemical spaces efficiently. Integrated with high-throughput synthesis and automated characterization, AI-driven workflows can potentially enable the discovery of functional materials for energy, electronics, and sustainability applications. I will illustrate our foray into using AI in the whole materials discovery process, including atomistic simulations, property predictions, generative design and experimental optimization. I will also provide an overview of where the field stands and why this remains an unsolved problem. Generative grammar ; Workflow ; Field (mathematics) ; Property (philosophy) ; Applications of artificial intelligence ; Generative model Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Meric Eren Cakar
Source: International Journal of Advances in Scientific Research and Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Materials Characterization: Methods, Applications, and Future Perspectives Materials characterization relies on techniques such as electron microscopy, diffraction, and spectroscopy to extract structural, chemical, and physical information from materials. While these traditional methods provide essential insights, their manual interpretation is time-consuming, operator-dependent, and difficult to scale for high-throughput analysis. In recent years, artificial intelligence (AI) has emerged as a transformative approach, enabling automation in image and pattern recognition, improvingreproducibility, and accelerating property inference from experimental data. This paper reviews the current applications of AI in materials characterization, including microscopy, spectroscopy, and mechanical testing, and explains how algorithms assist insegmentation, phase identification, peak analysis, and predictive modelling. The review also discusses challenges such as data quality, model interpretability, and standardization, along with future opportunities like physics-informed learning, multimodaldata fusion, and self-driving laboratories. Overall, AI is shown not only to enhance existing characterization workflows but also to redefine how materials data are generated, interpreted, and utilized in scientific decision-making. Workflow ; Applications of artificial intelligence ; Transformative learning ; Characterization (materials science) ; Interpretation (philosophy) ; Inference Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Sokhiba Inoyatova | Sitora Shoislomova | Shokhida Raupova | Makhsuda Israilova | Lobarkhan Karakhanova
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Harnessing artificial intelligence for the design and discovery of next-generation optical materials This paper explores the integration of artificial intelligence (AI) and machine learning (ML) techniques into the design and discovery of next-generation optical materials. Optical materials such as 0D chalcogenides, sulfides, and nano-structured compounds are critical to the advancement of photonic applications but remain challenging to engineer due to the vastness of chemical design space and the complexity of material behaviors. The study outlines the evolution of optical materials, including their historical development, physical classification, and the persistent challenges in meeting modern demands—especially in the UV and IR spectrums. Emphasis is placed on AI’s role in accelerating discovery via inverse design, data-driven workflows, and predictive modeling. Case studies demonstrate how AI has been used to discover materials with specific optical properties, such as refractive index and chromatic dispersion. Additionally, ethical considerations and interdisciplinary barriers in the adoption of AI are discussed. The paper concludes by emphasizing the transformative potential of self-driving laboratories and AI-enabled modeling in achieving high-throughput synthesis, optimization, and real-time experimentation, laying the groundwork for a paradigm shift in materials science. Transformative learning ; Applications of artificial intelligence ; Optical materials ; Photonics ; Big data Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Perovskite Materials and Applications
Authors: Guangcun Shan | Zhe Ding | Liujiang Xi | Hongbin Zhao | Jiliang Zhang | Feng Xu
Source: Rare Metals (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects Abstract Artificial intelligence (AI) technologies have transformed the field of materials science by enabling efficient data‐driven approaches for property prediction and material discovery. Here, we provide an in‐depth analysis of AI applications in materials science, focusing on data collection, property prediction, material discovery, and autonomous experimentation. We summarize the primary data sources and increased utility of large language models, which have significantly expedited the material discovery process. Additionally, we examine the application of AI to predict the key properties, emphasizing the transformative role of AI for lithium batteries. Although numerous challenges persist, advancements in AI‐driven tools and methodologies provide avenues for accelerating innovation in materials science. Transformative learning ; Key (lock) ; Field (mathematics) ; Property (philosophy) ; Applications of artificial intelligence ; Battery (electricity) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials
Authors: Yeongjun Yoon | Geun Ho Gu | K O Kim
Source: Research Square (preprint, 2025-12-19)
Field: Materials Science — Materials Chemistry

Closed-Loop Workflow of High-Entropy Materials Discovery: Efficient and Accurate Synthesizability Prediction via Domain-Specific Local LLMs Workflow ; Bottleneck ; Work (physics) ; Space (punctuation) ; Key (lock) ; Fragmentation (computing) ; Data modeling Machine Learning in Materials Science ; High Entropy Alloys Studies ; Catalysis and Oxidation Reactions
Authors: Rajeev S. Assary
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Atomistic Modeling-Enabled Machine Learning for Designing Improved Materials for Energy Storage A priori and reliable simulations can enable timely and cost-efficient design and discovery of materials for energy. Therefore, ‘ Compute first ’ is an optimal approach to initialize modern day R&amp;D processes. In energy storage, beyond lithium-ion (BLI) research has the potential to revolutionize consumer electronics including portable and stationary power, transportation, and grid energy storage sectors. Multi-valent (Mg, Ca, Zn) energy storage or economically viable monovalent (Na, K) batteries, high-density metal-air, metal-sulfur batteries, or grid-storage systems are considered in the beyond lithium-ion research and development. All these R&amp;D efforts require significant fundamental knowledge via a priori computations for materials discovery, property prediction, and optimization . Atomistic modeling when coupled with reliable Artificial Intelligence (AI) approaches can provide accurate insights to accelerate discovery of optimal electrolytes, electrodes, and membranes for BLI systems to reduce the cost . Thus, coupled with AI and multi-scale simulations techniques, atomistic modeling can address prediction of molecular level properties of materials (redox potentials, solvation, spectroscopic, diffusion, and reactivity) to down-select optimal materials or material combinations . In this presentation, I will describe some of our recent published efforts in active learning coupled with first principles simulations to down select/optimize desired materials for energy storage . This concept can be utilized for design of experiments using autonomous experimentation. Energy storage ; A priori and a posteriori ; Computation ; Energy (signal processing) ; Property (philosophy) ; Electronics ; Grid Machine Learning in Materials Science ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Ansuman Halder | Maher B. Alghalayini | Shuan Cheng | Nikil Thalanki | Tung V. N. Nguyen | Abigail R. Hering | Do-Kyoung Lee | Simon Arnold | Marina S. Leite | Edward S. Barnard | Aleksandr Razumtcev | Matthew A. Wall | Arian Gashi | Marcus M. Noack | Shijing Sun | Carolin M. Sutter‐Fella
Source: ChemRxiv (preprint, 2025-04-21)
Field: Materials Science — Materials Chemistry

AI-driven robot enables synthesis-property relation prediction for metal halide perovskites in humid atmosphere Materials Acceleration Platforms (MAPs) – also known as self-driving laboratories, present a new paradigm for materials science and promise an order of magnitude accelerated materials discovery and optimization compared to the traditional trial and error approach. Metal halide perovskites (MHPs) are an emerging class of materials for optoelectronic applications but are plagued by irreproducible optoelectronic quality, in particular for films fabricated in humid atmosphere. This challenge is widely observed across various inorganic material synthesis processes. Automating decision-making hinges on the challenge to reduce data to machine-readable metrics that accurately represent the material quality. Here we develop a machine learning (ML)-guided closed-loop platform (AutoBot) with a multimodal data fusion approach combining results from three characterization techniques to predict synthesis - property relations for the optical quality of MHP thin films in relative humidities (RHs) ranging from 5 - 55%. The efficiency of this approach is confirmed by the fast-dropping learning rate to 2% after experimentally sampling less than 1% of the possible 5,000+ combinations. The validation of predicted synthesis - property relations was done by in situ photoluminescence characterization and revealed an avenue for controlling the MHP crystallization by fine-tuning the synthesis, MACl additive, and RH. Doing so decreases the energetic barrier to form heterogeneously nucleating photo-active MHP. By adjusting the antisolvent drop time, comparable MHP film quality can be obtained in a relative humidity window between 5 - 25% lifting the need for stringent atmosphere control. Our MAP enables an accelerated screening and understanding of the synthesis design space, facilitating rational synthesis recipe choice and higher reproducibility for a wide range of materials. Halide ; Atmosphere (unit) ; Relation (database) ; Property (philosophy) ; Robot ; Perovskite (structure) ; Metal ; Materials science ; Environmental science ; Computer science ; Chemical engineering ; Artificial intelligence ; Meteorology ; Engineering ; Inorganic chemistry ; Physics ; Chemistry ; Metallurgy ; Data mining ; Philosophy Catalytic Processes in Materials Science