jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yujie Ma | Yang Gao | Ludi Wang | Ming Chen | Wenjuan Cui | Bin Wang | Yi Du
Source: The Innovation Informatics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating materials discovery through active learning: Methods, challenges and opportunities &lt;p&gt;The convergence of large-scale experimentation and computation has transformed materials science into a discipline where data plays a central role. However, the vast design space and high costs of experiments and simulations still slow progress. In contrast to traditional approaches, active learning (AL) identifies which experiments or simulations will provide the most valuable information. This minimizes unnecessary work and cost. By prioritizing the most informative data points, AL enables more efficient and targeted exploration, particularly when resources are limited. This approach accelerates breakthroughs in complex systems, such as superconductors, catalysts, and batteries. This review introduces a framework that categorizes AL in both experimental and computational settings. It highlights the importance of integrating domain knowledge and addresses interpretability challenges. By providing a pragmatic roadmap, it makes a strong case for adopting AL, especially to maximize impact in resource-constrained materials research.To facilitate provide additional details, we have made the supplementary materials available in the repository at https://github.com/kg4sci/awsome-AL.&lt;/p&gt; Interpretability ; Domain (mathematical analysis) ; Key (lock) ; Work (physics) ; Convergence (economics) ; Space (punctuation) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Siyu Liu | Tongqi Wen | Beilin Ye | Zhuoyuan Li | Han Liu | Yang Ren | David J. Srolovitz
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Large language models for material property predictions: elastic constant tensor prediction and materials design A multifunctional LLM for predicting finite temperature elastic constant tensor, RAG-enhanced prediction, and inverse material design. Property (philosophy) ; Constant (computer programming) ; Tensor (intrinsic definition) ; Material Design ; Materials science ; Computer science ; Mathematics ; Geometry ; Composite material ; Programming language ; Philosophy ; Epistemology Machine Learning in Materials Science
Authors: Xiaoqi Sun | Jiannan Liu | Babak Mahjour | Klavs F. Jensen | Connor W. Coley
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis The identification of suitable reaction conditions is a crucial step in organic synthesis. Computer-aided synthesis planning promises to improve the efficiency of chemistry and enable robot-assisted workflows, but there remains a gap in bridging computational tools with experimental execution due to the challenge of reaction condition prediction. The conditions used to carry out a reaction consist of qualitative details, such as the discrete identities of "above-the-arrow" agents (catalysts, additives, solvents, <i>etc.</i>) as well as quantitative details, such as temperature and concentrations of both reactants (product contributing) and agents. These procedural aspects of organic chemistry exert a direct influence over the outcome of a chemical transformation and must be provided in any hypothetical autonomous synthesis workflow. In this work, we push beyond qualitative reaction condition recommendation by developing a data-driven framework that incorporates quantitative details, specifically equivalence ratios. We frame the condition recommendation problem as four sub-tasks: predicting agent identities, reaction temperature, reactant amounts, and agent amounts, and evaluate our model accordingly. We demonstrate improved performance over popularity and nearest neighbor baselines and highlight the model's practical utility for predicting conditions in diverse reaction classes <i>via</i> representative case studies. Equivalence (formal languages) ; Organic synthesis ; Combinatorial chemistry ; Computer science ; Chemistry ; Information retrieval ; Biological system ; Organic chemistry ; Mathematics ; Discrete mathematics ; Biology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Ye Wei | Bo Peng | Ruiwen Xie | Yangtao Chen | Yuwen Qin | Peng Wen | Stefan Bauer | Po‐Yen Tung | Dierk Raabe
Source: Nature Computational Science (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Deep active optimization for complex systems Inferring optimal solutions from limited data is considered the ultimate goal in scientific discovery. Artificial intelligence offers a promising avenue to greatly accelerate this process. Existing methods often depend on large datasets, strong assumptions about objective functions, and classic machine learning techniques, restricting their effectiveness to low-dimensional or data-rich problems. Here we introduce an optimization pipeline that can effectively tackle complex, high-dimensional problems with limited data. This approach utilizes a deep neural surrogate to iteratively find optimal solutions and introduces additional mechanisms to avoid local optima, thereby minimizing the required samples. Our method finds superior solutions in problems with up to 2,000 dimensions, whereas existing approaches are confined to 100 dimensions and need considerably more data. It excels across varied real-world systems, outperforming current algorithms and enabling efficient knowledge discovery. Although focused on scientific problems, its benefits extend to numerous quantitative fields, paving the way for advanced self-driving laboratories. Computer science ; Pipeline (software) ; Process (computing) ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Metaheuristic Optimization Algorithms Research
Authors: Hongjian Chen | Duoyu Sun | Yuyu Sun | Yong Zhang | Huan Yang
Source: Buildings (article, 2025-08-31)
Field: Materials Science — Polymers and Plastics

Multi-Agent Deep Reinforcement Learning-Based HVAC and Electrochromic Window Control Framework Deep reinforcement learning (DRL)-based HVAC control has shown clear advantages over rule-based and model predictive methods. However, most prior studies remain limited to HVAC-only optimization or simple coordination with operable windows. Such approaches do not adequately address buildings with fixed glazing systems—a common feature in high-rise offices—where the lack of operable windows restricts adaptive envelope interaction. To address this gap, this study proposes a multi-zone control framework that integrates HVAC systems with electrochromic windows (ECWs). The framework leverages the Q-value Mixing (QMIX) algorithm to dynamically coordinate ECW transmittance with HVAC setpoints, aiming to enhance energy efficiency and thermal comfort, particularly in high-consumption buildings such as offices. Its performance is evaluated using EnergyPlus simulations. The results show that the proposed approach reduces HVAC energy use by 19.8% compared to the DQN-based HVAC-only control and by 40.28% relative to conventional rule-based control (RBC). In comparison with leading multi-agent deep reinforcement learning (MADRL) algorithms, including MADQN, VDN, and MAPPO, the framework reduces HVAC energy consumption by 1–5% and maintains a thermal comfort violation rate (TCVR) of less than 1% with an average temperature variation of 0.35 °C. Moreover, the model demonstrates strong generalizability, achieving 16.58–58.12% energy savings across six distinct climatic regions—ranging from tropical (Singapore) to temperate (Beijing)—with up to 48.2% savings observed in Chengdu. Our framework indicates the potential of coordinating HVAC systems with ECWs in simulation, while also identifying limitations that need to be addressed for real-world deployment. Reinforcement learning ; Electrochromism ; HVAC ; Window (computing) ; Reinforcement ; Control (management) ; Computer science ; Artificial intelligence ; Control engineering ; Engineering ; Mechanical engineering ; Structural engineering ; Air conditioning Transition Metal Oxide Nanomaterials ; Elevator Systems and Control ; Smart Parking Systems Research
Authors: Aleksandar Kondinski
Source: ChemistryEurope (article, 2025-03-05)
Field: Chemistry — Organic Chemistry

Digital Metallo‐Supramolecular Chemistry Abstract Metal‐organic cages (MOCs) are supramolecular assemblies designed through complex chemical and spatial reasoning. While chemical creativity and modelling have advanced greatly the engineering of new and functional MOCs, their synthesis and characterisation have remained labour‐intensive. Recently, the Cooper group developed a self‐driving laboratory system that automates the synthesis and characterisation of MOCs, their building units and host‐guest derivatives, through benchtop instrumentation, robotics, and heuristic decision‐making. The overall system provides a critical step towards the merging of digital and supramolecular chemistry. Supramolecular chemistry ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Crystallography ; Crystal structure Supramolecular Chemistry and Complexes ; Mass Spectrometry Techniques and Applications ; Radioactive element chemistry and processing
Authors: Yu Zheng | Catherine Blake | Layla Mravac | Fengxue Zhang | Yuxin Chen | Shuolong Yang
Source: npj Computational Materials (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

A self-driving physical vapor deposition system making sample-specific decisions on the fly We present an autonomous physical vapor deposition system that integrates hardware automation, in-situ optical spectroscopy, and Bayesian machine learning into a complete self-driving laboratory framework making decisions on the fly. Using silver thin films as a model material, our platform efficiently navigates a complex parameter space through active learning. By introducing a thin physical layer denoted as calibration layer, the machine learning models adapt to sample-specific conditions on the fly and reliably predict the deposition conditions to achieve user-specified optical properties. Moreover, from the high-throughput experimental data, the algorithm systematically captures the complex parameter-property relationships that are challenging to deduce by conventional trial-and-error methods. This study demonstrates the potential of self-driving laboratories for both reducing human labor and gaining new understanding of materials, providing a streamlined approach to enable self-driving physical vapor deposition systems. Deposition (geology) ; Physical vapor deposition ; Calibration ; On the fly ; Layer (electronics) ; Vapour deposition ; Chemical vapor deposition Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Optical Coatings and Gratings
Authors: Robert Waelder | W. Kim | Mark A. Pitt | Jay I. Myung | Benji Maruyama
Source: APL Machine Learning (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Multi-objective Bayesian optimization of carbon nanotube yield and diameter control at synthesis Carbon nanotube (CNT) synthesis is a ripe area for autonomous experimentation. It is a high-dimensional problem, with both a large number of experimental inputs and critically important outputs, since different single-walled CNT (SWCNT) structures exhibit vastly different properties. In particular, the electronic properties of SWCNTs can vary from metallic to semiconducting based on the diameter and helical twist angle of the structure. Diameter selection has been achieved by post-growth purification at the expense of yield. We show results where both yield and selectivity are increased. This work presents the first multi-objective autonomous experimentation campaign for CNT synthesis, maximizing both overall CNT yield and diameter control. Many attempts at SWCNT diameter control do so via intensive engineering of the catalyst particles: composition, diameter, packing density, etc. Here, we begin with a disordered film of oxidized cobalt on a defective alumina support. By varying the thermodynamic conditions of the synthesis environment, we can control the size distribution and population dynamics of the emergent nanoparticle catalyst bed and, therefore, control the diameters of the resulting CNTs. Using this thermodynamic understanding, we identify regions of experimental space conducive to yield- and diameter control-optimized growth, demonstrating that, for the conditions tested here, these objectives are nearly independent, and with some exception at the extremes, arbitrary degrees of diameter control can be realized for all experimentally achieved yields. In addition, we show that this thermodynamic understanding of CNT catalyst behavior enables effective planning in a vast, information-poor environment, enabling diameter control beyond what might be expected, given the limitations of the in situ diameter characterization methods. Carbon nanotube ; Yield (engineering) ; Bayesian optimization ; Bayesian probability ; Nanotechnology ; Process engineering ; Materials science ; Computer science ; Mathematical optimization ; Mathematics ; Artificial intelligence ; Engineering ; Composite material Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Machine Learning in Materials Science
Authors: Yaru Gao | Niamh Mac Fhionnlaoich | Maximilian O. Besenhard | Arun Pankajakshan | Federico Galvanin | Stefan Guldin
Source: Advanced Functional Materials (article, 2025-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting Aggregation Behavior of Nanoparticles in Liquid Crystals via Automated Data‐Driven Workflows Abstract Gold nanoparticles (AuNPs) have gained prominence as versatile nanoscale building blocks in chemical and biomedical research. Liquid crystals (LCs) offer a promising composite matrix for fundamental research and in a variety of applications. However, optimizing the solubility of AuNPs within the LC matrix remains challenging due to the interplay of multiple experimental variables, necessitating extensive combinatorial trials. In this study, an automated AuNP synthesis platform combined with a Design of Experiment (DoE) framework was employed to streamline the optimization process. A random forest model, trained on a relatively small dataset, successfully predicted nanoparticle aggregate classifications with high accuracy. Aggregate behavior was further analyzed using UV–vis spectroscopy with automated data processing for feature reduction. These steps enhanced the closed‐loop optimization workflow by iteratively constructing a generalized additive model for predicting spectral characteristics. AuNPs optimized for solubility were deployed in subsequent experiments for temperature‐induced hierarchical assembly driven by the phase transition of the thermotropic LC. Computer vision methods were used to quantify the reversibility of LC‐AuNP composites during self‐assembly, utilizing entropy values derived from a pattern recognition algorithm. This study highlights the benefits of integrating cross‐disciplinary approaches to refine analytical workflows, advancing the discovery of nanomaterial systems with programmable and reconfigurable features. Workflow ; Materials science ; Nanotechnology ; Colloidal gold ; Aggregate (composite) ; Nanomaterials ; Nanoparticle ; Computer science ; Thermotropic crystal ; Biological system ; Liquid crystal ; Database ; Liquid crystalline Liquid Crystal Research Advancements ; Pickering emulsions and particle stabilization ; Neurobiology and Insect Physiology Research
Authors: Helena Ros | Youssef Abdalla | Michael T. Cook | David Shorthouse
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Efficient discovery of new medicine formulations using a semi-self-driven robotic formulator A self driving system is presented for rapidly optimising the solubility of drugs into medicines. Computer science ; Artificial intelligence ; Computational biology ; Biology Chromatography in Natural Products ; Pharmacological Effects of Natural Compounds ; Computational Drug Discovery Methods
Authors: Min Ook Kim
Source: Polymers (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications Polymeric coatings play a pivotal role in enhancing the durability, functionality, and sustainability of structural materials exposed to harsh environmental conditions. Recent advances in artificial intelligence (AI) have transformed the development, optimization, and evaluation of these coatings by enabling data-driven material discovery, predictive performance modeling, and autonomous inspection. This review aims to provide a comprehensive overview on AI-driven polymeric coating strategies for structural applications, emphasizing the integration of machine learning, computer vision, and multi-physics simulations into traditional materials engineering frameworks. The discussion encompasses AI-assisted material selection methods for polymers, fillers, and surface modifiers; predictive models for corrosion, fatigue, and degradation; and intelligent evaluation systems using digital imaging, sensor fusion, and data analytics. Case studies highlight emerging trends such as self-healing, smart, and sustainable coatings that leverage AI to balance mechanical performance, environmental resistance, and carbon footprint. The review concludes with identifying current challenges-including data scarcity, model interpretability, and cross-domain integration-and proposes future research directions toward explainable, autonomous, and circular coating design pipelines. Sustainability ; Coating ; Leverage (statistics) ; Material selection ; Selection (genetic algorithm) ; Predictive modelling ; Context (archaeology) ; Structural health monitoring Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Polymer composites and self-healing
Authors: Xin Hu | Zixu Wang | Hao Zhang | Yaduo Song | Junfeng Cui | Jinming Guo | Minglei Cao | Zhiqiang Wang | Yonggang Yao | Yunhui Huang
Source: Interdisciplinary materials (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

High‐Entropy Design in Battery Materials for High Performance Electrochemical Energy Storage ABSTRACT The growing demand for advanced electrochemical energy storage devices highlights challenges in battery materials, such as limited storage sites, slow ion/electron transport, and structural instability, which collectively impede improvements in energy density, rate performance, cycle life, and battery safety. To address these challenges, high‐entropy design—a strategy integrating multiple elements through doping, compositional gradients, or alloying—has emerged as a transformative approach to simultaneously enhance thermodynamic stability and unlock synergistic “cocktail effects” in battery materials. By strategically combining elements with tailored atomic‐scale interactions, such systems can achieve unprecedented performance between structural robustness and electrochemical activity. However, the design principles and synergistic effects within high‐entropy materials (cathodes, electrolytes, anodes) remain poorly understood, complicated by their vast compositional and structural possibilities. In this review, we present a systematic analysis of how high‐entropy strategies optimize material properties across three interdependent dimensions: (1) structural engineering (e.g., surface/interface engineering), (2) physical effects (e.g., lattice strain and size mismatch), and (3) electronic/chemical interactions (e.g., valence state modulation and electron delocalization). While entropy alone does not guarantee superior performance, we highlight that rational element selection and configuration design are critical to activating these mechanisms. Importantly, AI‐driven framework integrating machine learning with first‐principles modeling, can enable data‐guided material discovery to decode the complexity of high‐entropy systems. This framework systematically deciphers design principles, predicts performance trade‐offs, and accelerates the translation of high‐entropy materials into practical energy storage solutions. Electrochemical energy storage ; Energy storage ; Battery (electricity) ; Robustness (evolution) ; Hydrogen storage ; Rational design ; Structural complexity ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials
Authors: Maha Ismail | Maria Angeles Cabañero | Joseba Orive | Lakshmipriya Musuvadhi Babulal | Javier García | Maria C. Morant‐Miñana | Jean‐Luc Dauvergne | Francisco Bonilla | Icíar Monterrubio | Javier Carrasco | Amaia Saracibar | Marine Reynaud
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

ADEL: an automated drop-cast electrode setup for high-throughput screening of battery materials ADEL is an automated setup for preparing high-loading electrodes in battery research. Integrated into the MAITENA platform, it provides reliable, high-quality datasets for fast screening of battery materials, significantly accelerating research and development efforts. Throughput ; Electrode ; Drop (telecommunication) ; Battery (electricity) ; Materials science ; Electrical engineering ; Computer science ; Automotive engineering ; Engineering ; Physics ; Telecommunications ; Wireless ; Power (physics) Electron and X-Ray Spectroscopy Techniques ; Advanced Battery Technologies Research ; Advancements in Battery Materials