jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Myungjoon Kim | Hyeonjin Park | Jonghwa Shin
Source: Nanophotonics (article, 2025-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanophotonic device design based on large language models: multilayer and metasurface examples Large language models (LLMs) have gained significant prominence in language processing, demonstrating remarkable performance across a wide range of tasks. Recently, LLMs have been explored in various scientific fields beyond language-based tasks. However, their application in the design of nanophotonic devices remains less explored. Here, we investigate the capabilities of LLMs to address nanophotonic design problems without requiring domain-specific expertise of the user. Our findings show that an LLM with in-context learning enables nonexpert users to calculate optical responses of multilayer films via numerical simulations. Through conversational interaction and feedback between the LLM and the user, an optimal design of the multilayer films can be also produced for the user-provided target optical properties. Furthermore, we fine-tune the LLM using text-based representations of the structure and properties of optical metasurfaces. We demonstrate that the fine-tuned LLM can generate metasurface designs with target properties by reversing the input and output text. This research highlights the potential of LLMs to expedite the nanophotonic design process and to make it more accessible to a wider audience. Nanophotonics ; Computer science ; Context (archaeology) ; Process (computing) ; Human–computer interaction ; Nanotechnology ; Materials science ; Programming language Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Neural Networks and Reservoir Computing
Authors: Zhenzhi Tan | Qi Yang | Sanzhong Luo
Source: Organic Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI molecular catalysis: where are we now? Artificial intelligence (AI) is transforming molecular catalysis by addressing long-standing challenges in retrosynthetic design, catalyst design, reaction development, and autonomous experimentation. Chemistry ; Catalysis ; Combinatorial chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Daniel J. Fowles | Benedict J. Connaughton | James W. Carter | John B. O. Mitchell | David Scott Palmer
Source: Chemical Reviews (review, 2025-07-29)
Field: Chemistry — Spectroscopy

Physics-Based Solubility Prediction for Organic Molecules Accurate prediction of aqueous solubility for organic molecules is of great importance across a range of fields, from the design and manufacturing of energy materials, to assessing the environmental impact of potential pollutants. It is of particular significance to the pharmaceutical industry, in which problems with low aqueous solubility frequently hamper the development of new drugs. Experimental measurements of solubility are used extensively, but are often time-consuming, resource intensive and only applicable to already synthesized molecules. As such, there is a need for the development of computational approaches to predict solubility. In recent years, there have been considerable advances in physics-based methods, with several contrasting techniques able to give accurate predictions of solubility and a wealth of thermodynamic data for structural optimization. Here, we provide the reader with a thorough understanding of the theoretical background and practical applications of these physics-based methods to predict solubility. This includes discussions of the various advantages and disadvantages of each approach, and an indication of areas of continuing research. Experimental and data-driven methods to assess solubility are also discussed to provide context. Solubility ; Context (archaeology) ; Biochemical engineering ; Chemistry ; Molecule ; Organic molecules ; Resource (disambiguation) ; Process engineering ; Nanotechnology ; Computational chemistry ; Computer science ; Organic chemistry ; Materials science ; Engineering Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Phase Equilibria and Thermodynamics
Authors: Joshua W. Sin | Siu Lun Chau | Ryan P. Burwood | Kurt Püntener | Raphael Bigler | Philippe Schwaller
Source: Nature Communications (article, 2025-07-12)
Field: Materials Science — Materials Chemistry

Highly parallel optimisation of chemical reactions through automation and machine intelligence We report the development and application of a scalable machine learning (ML) framework (Minerva) for highly parallel multi-objective reaction optimisation with automated high-throughput experimentation (HTE). Minerva demonstrates robust performance with experimental data-derived benchmarks, efficiently handling large parallel batches, high-dimensional search spaces, reaction noise, and batch constraints present in real-world laboratories. Validating our approach experimentally, we apply Minerva in a 96-well HTE reaction optimisation campaign for a nickel-catalysed Suzuki reaction, tackling challenges in non-precious metal catalysis. Our approach effectively navigates the complex reaction landscape with unexpected chemical reactivity, outperforming traditional experimentalist-driven methods. Extending to industrial applications, we deploy Minerva in pharmaceutical process development, successfully optimising two active pharmaceutical ingredient (API) syntheses. For both a Ni-catalysed Suzuki coupling and a Pd-catalysed Buchwald-Hartwig reaction, our approach identifies multiple conditions achieving >95 area percent (AP) yield and selectivity, directly translating to improved process conditions at scale. Scalability ; Computer science ; Throughput ; Automation ; Yield (engineering) ; Reaction conditions ; Process (computing) ; Biochemical engineering ; Combinatorial chemistry ; Catalysis ; Process engineering ; Chemistry ; Database ; Materials science ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Man Luo | Zikai Xie | Huirong Li | Baicheng Zhang | Jiaqi Cao | Yan Huang | Hang Qu | Qing Zhu | Linjiang Chen | Jun Jiang | Yi Luo
Source: Matter (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Physics-informed, dual-objective optimization of high-entropy-alloy nanozymes by a robotic AI chemist Chemist ; Dual (grammatical number) ; Alloy ; Dual purpose ; Entropy (arrow of time) ; Computer science ; Nanotechnology ; Chemistry ; Engineering ; Materials science ; Mechanical engineering ; Physics ; Philosophy ; Thermodynamics ; Organic chemistry Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials
Authors: Tarun Parangi
Source: Reviews in Inorganic Chemistry (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Heterogeneous catalysis: an alternative approach for energy and environment Abstract The uncontrolled increase in population and human activities has led to a significant rise in the demand for energy resources. The excessive use of limited fuel sources, unplanned deforestation, and greenhouse gas emissions have necessitated the search for and development of alternative, sustainable energy resources that cause minimal or no harm to the environment. The introduction of green and eco-friendly technologies offers a potential solution to address the growing demand in fields such as petroleum and hydrogen production, with the ultimate goal of promoting environmental sustainability. This review article highlights recent innovations in the field of heterogeneous catalysis, focusing on the development of various catalytic materials and processes, as well as their future prospects for both industrial and academic advancements. A brief discussion is presented on the efficient performance of solid acid and base catalysts, metal-organic frameworks (MOFs), electrocatalysts, and photocatalysts. The discussed catalytic systems have been explored for their potential applications, including biodiesel and hydrogen production as alternative energy sources, as well as CO 2 reduction and the degradation of toxic dyes and organic pollutants for environmental remediation. Overall, the significance of heterogeneous catalysts has been explored, emphasizing their critical role in driving industrial progress and economic growth. Chemistry ; Catalysis ; Biochemical engineering ; Energy (signal processing) ; Organic chemistry ; Nanotechnology ; Combinatorial chemistry Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Layered Double Hydroxides Synthesis and Applications
Authors: Ali Hamzehlouy | Sara Zarei | Razhan Salah Othman | Farangis Shahi | Hana Afshar | Arsia Afshar Taromi | Hossein Ali Khonakdar
Source: Polymers for Advanced Technologies (review, 2025-02-01)
Field: Materials Science — Materials Chemistry

Recent Advances in Biomedical Applications of <scp>MXene</scp>‐Integrated Electrospun Fibers: A Review ABSTRACT The integration of MXenes into electrospun fibers represents a significant leap forward in advancing biomedical applications. This review delves into the synergistic combination of MXenes and electrospun fibers, highlighting their multifaceted capabilities in tissue engineering, drug delivery, antimicrobial activity, cancer therapy, and biosensing. Exceptional properties of MXene, including electrical conductivity, hydrophilicity, mechanical robustness, and surface tunability, offer unique advantages when incorporated into nanofibrous scaffolds. By enhancing mechanical strength, promoting cellular interactions, and enabling targeted therapeutic functions, MXene‐based electrospun fibers demonstrate immense potential in addressing critical challenges in biomedicine. This review provides a comprehensive overview of recent advances in MXene synthesis, their incorporation into electrospun matrices, and their biomedical applications, while also identifying challenges and future directions in this emerging field. Materials science ; Electrospinning ; Nanotechnology ; Composite material ; Polymer MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Ashok K. Giri | Md Aquib | Anmol Choudhury | Vinod K. Kannaujiya | Jennifer I. Lim | Zi Gu | Megan D. Lenardon | Cyrille Boyer
Source: Macromolecular Rapid Communications (article, 2025-06-17)
Field: Chemistry — Organic Chemistry

Lipoic Acid Based Redox‐Responsive Degradable Antimicrobial Polymers The rise of multidrug-resistant (MDR) pathogens poses a critical threat to global health, exacerbated by the overuse of antibiotics and the lack of effective alternatives. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their broad-spectrum activity and unique mechanisms of action. However, several challenges such as enzymatic degradation, high production costs, and potential cytotoxicity have hindered their clinical translation. To overcome these limitations, antimicrobial polymers (APs) inspired by AMPs have been developed using controlled/living polymerization techniques. In this study, a series of degradable, disulfide-containing antimicrobial polymers incorporating benzyl lipoate, a lipoic acid (LA) derivative, is synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization. Benzyl lipoate is prepared by modification of LA with benzyl alcohol to introduce a hydrophobic moiety and copolymerized with a primary amine-containing cationic monomer and hydrophilic co-monomers, including hydroxyethyl acrylamide (HEAm) and poly(ethylene glycol) methyl ether acrylate (PEGMEA). The resulting polymers demonstrated antimicrobial activity against drug-resistant Pseudomonas aeruginosa, improved hemocompatibility, and redox-responsive degradability. This study highlights the potential of disulfide-based APs as a next-generation strategy for combating MDR infections while ensuring controlled degradability. Combinatorial chemistry ; Cationic polymerization ; Ethylene glycol ; Chemistry ; Antimicrobial ; Acrylate ; Monomer ; Polymerization ; Chain transfer ; Organic chemistry ; Polymer ; Radical polymerization ; Materials science Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Click Chemistry and Applications
Authors: Ibrahim Orhan | Yuankai Zhao | Ravichandar Babarao | Aaron W. Thornton | Tu C. Le
Source: Molecules (review, 2025-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning Descriptors for CO2 Capture Materials The influence of machine learning (ML) on scientific domains continues to grow, and the number of publications at the intersection of ML, CO<sub>2</sub> capture, and material science is growing rapidly. Approaches for building ML models vary in both objectives and the methods through which materials are represented (i.e., featurised). Featurisation based on descriptors, being a crucial step in building ML models, is the focus of this review. Metal organic frameworks, ionic liquids, and other materials are discussed in this paper with a focus on the descriptors used in the representation of CO<sub>2</sub>-capturing materials. It is shown that operating conditions must be included in ML models in which multiple temperatures and/or pressures are used. Material descriptors can be used to differentiate the CO<sub>2</sub> capture candidates through descriptors falling under the broad categories of charge and orbital, thermodynamic, structural, and chemical composition-based descriptors. Depending on the application, dataset, and ML model used, these descriptors carry varying degrees of importance in the predictions made. Design strategies can then be derived based on a selection of important features. Overall, this review predicts that ML will play an even greater role in future innovations in CO<sub>2</sub> capture. Computer science ; Intersection (aeronautics) ; Representation (politics) ; Machine learning ; Focus (optics) ; Artificial intelligence ; Biochemical engineering ; Aerospace engineering ; Physics ; Engineering Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Weixian Li | Yi Dong | Mingchu Ran | Saisai Lin | Peng Liu | Hao Song | Jun‐Dong Yi | Chaoyang Zhu | Zhifu Qi | Chenghang Zheng | Xiao Zhang | Xiang Gao
Source: Journal of Energy Chemistry (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts Catalysis ; Methanol ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Kong Meng | Run Long
Source: JACS Au (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

A Universal Machine Learning Framework Driven by Artificial Intelligence for Ion Battery Cathode Material Design Graph neural networks for crystal property prediction typically require precise atomic positions and types, limiting their applicability for novel materials with unknown structures. To address this limitation, we introduce BatteryFormer, a versatile machine learning model that employs average interatomic radius distance instead of precise bond lengths as edge embedding, enabling rapid, high-throughput material screening based solely on composition and structural prototypes. BatteryFormer demonstrates robust predictive performance across a wide range of intervals. It accurately predicted high redox potentials for four distinct cathode materials: layered oxides, fluorophosphate salts, vanadium fluorophosphate salts, and ferric pyrophosphate salts. Notably, it also correctly predicted the low redox potential (1.56 V) of the recently reported cathode material Na<sub>6</sub>CoS<sub>4</sub>, highlighting its reliability in diverse chemical spaces. Beyond numerical accuracy, BatteryFormer captures crucial local structural features, such as the linear Na-O-Li configuration in layered transition metal oxide cathodes, essential for enhancing redox potentials. The model also maintains high predictive accuracy for a variety of lithium-ion battery cathode materials, further validating its strong generalization capability. Integrating knowledge graphs and knowledge inference, this work provides a visual mapping of relationships among material types, doping element combinations, doping ratios, redox potentials, capacities, and energy densities. This integration offers practical guidance for synthesizing high-entropy sodium-ion battery cathodes with enhanced cycling stability and energy density. The proposed data-driven approach provides a robust framework for accelerating materials discovery and transitioning from empirical materials design strategies. Battery (electricity) ; Computer science ; Artificial intelligence ; Cathode ; Machine learning ; Engineering ; Electrical engineering ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Rituraj Dubey | Anees A. Ansari | Youngil Lee | Shili Gai | Ruichan Lv | Ziyue Ju | Shafiya Mohammad | Piaoping Yang | Laxman Singh
Source: Renewable and Sustainable Energy Reviews (article, 2025-01-19)
Field: Materials Science — Materials Chemistry

Molecular dynamics-to-machine learning for deep eutectics in energy storages Eutectic system ; Molecular dynamics ; Computer science ; Energy (signal processing) ; Dynamics (music) ; Artificial intelligence ; Materials science ; Chemistry ; Physics ; Metallurgy ; Computational chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Catalysis and Oxidation Reactions
Authors: Kwang S. Kim
Source: Advanced Energy Materials (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency Abstract Machine learning (ML) has revolutionized energy materials discovery through two key paradigms: ML potentials enabling quantum‐accurate atomistic simulations with 2‐4 orders of magnitude speedup over density functional theory, and ML‐driven screening that efficiently navigates vast chemical spaces for rapid materials optimization. Advanced approaches, including graph neural networks and sparse Gaussian process regression incorporate physical symmetries and conservation laws, going beyond traditional statistical methods. Applications span battery materials, electrocatalysts, solar cells, phase change memory, and hydrogen storage systems, enabling simulations of thousands of atoms over extended timescales beyond the reach of quantum mechanical methods. Together, these complementary ML approaches enable predictive computational models spanning atomic to macroscopic scales. Current challenges include data quality, extrapolation to new chemical spaces, and physical interpretability. Emerging solutions involve equivariant architectures, active learning strategies, and physics‐informed neural networks. The convergence of ML methodologies with experimental workflows can accelerate materials discovery and optimization. This addresses critical sustainable energy challenges in conversion, storage, and utilization while supporting the development of autonomous discovery platforms. In this way, ML helps overcome computational limitations in multiscale energy materials research and supports the efficient design of novel materials with tailored properties. Bridging (networking) ; Gaussian process ; Kriging ; Artificial neural network ; Workflow ; Materials informatics ; Key (lock) ; Computational complexity theory ; Extrapolation ; Efficient energy use Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Aditya Venkatraman | Mark Wilson | David Montes de Oca Zapiain
Source: npj Computational Materials (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications Abstract Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields. Molecular dynamics ; Artificial neural network ; Estimation ; Dynamics (music) ; Statistical physics ; Charge (physics) ; Corrosion ; Computer science ; Physics ; Materials science ; Artificial intelligence ; Systems engineering ; Engineering ; Quantum mechanics ; Acoustics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: R. Alagulakshmi | R. Ramalakshmi | V. Arumugaprabu | Geetha Palani | Manickam Selvaraj | Sanjay Basumatary
Source: Polymer Bulletin (review, 2025-01-09)
Field: Materials Science — Polymers and Plastics

Advancements of machine learning techniques in fiber-filled polymer composites: a review Aerospace ; Automotive industry ; Composite number ; Materials science ; Composite material ; Scope (computer science) ; Polymer ; Characterization (materials science) ; Fiber ; Fabrication ; Computer science ; Process (computing) ; Mechanical engineering ; Manufacturing engineering ; Nanotechnology ; Aerospace engineering ; Engineering Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes