jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guo Tian | Honghao Chen | Runyu Jiang | Chenxi Zhang | Xi Ning Lu | Xiaonan Wang | Fei Wei
Source: Journal of the American Chemical Society (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

A Dual-Engine Artificial Intelligence Framework Accelerates Sustainable Aviation Fuel Component Synthesis Decarbonizing aviation with sustainable drop-in fuels is essential for credible net-zero pathways. However, assembling energy-dense jet fuel hydrocarbons from C<sub>1</sub> feedstocks demands multifunctional catalysts whose performance arises from nonlinear, high-dimensional interactions─beyond single-descriptor design rules. Here we present a dual-engine artificial intelligence framework that couples closed-loop active learning with interpretable machine learning, demonstrated for syngas conversion to sustainable aviation fuel. The approach autonomously explores vast catalyst spaces while distilling human-interpretable principles. We identify previously unreported compositions and a general rule: on a stable spinel backbone, placing a d-block metal at the tetrahedral (A)-site and an early lanthanide at the octahedral (B)-site creates cooperative d-f interactions that enable π-back-donation into the π* orbitals of oxygenated intermediates, strengthening adsorption and lowering formation barriers to accelerate intermediate generation and C-C coupling to jet-range aromatics. Guided by this rule, active sites such as Zn-Ce/Sm, Fe-Pr/La, and Ni-Ce achieve >75% selectivity to jet-fuel-range aromatic hydrocarbons with high space-time yields. Overall, the dual-engine approach not only accelerates discovery but also yields transparent, experimentally validated design rules─a generalizable blueprint for interpretable, AI-enabled catalyst design in complex sustainable chemistries. Blueprint ; Aviation ; Component (thermodynamics) ; Catalysis ; Syngas ; Flue gas ; Coupling (piping) ; Tetrahedron Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Ahmetcan Alkoç | Afife Binnaz Hazar Yoruç | Adem Can Uşak | Caglayan Aras | Mete Bakır
Source: Surfaces and Interfaces (article, 2026-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Artificial neural network-based modeling of sustainable plasma process parameters for predicting wettability of aircraft composite surfaces Contact angle ; Wetting ; Surface energy ; Diiodomethane ; Composite number ; X-ray photoelectron spectroscopy ; Plasma ; Nozzle Surface Modification and Superhydrophobicity ; High voltage insulation and dielectric phenomena ; Fiber-reinforced polymer composites
Authors: Yan Guo | Menglan Luo | Wenbo Zhang | Peidong Liu | Jin Liu | Shudong Huang | Jiancheng Lv | Bowen Ke | Xianggen Liu
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Few-shot molecular property optimization <i>via</i> a domain-specialized large language model Large language models (LLMs) have revolutionized machine learning with their few-shot learning and reasoning capabilities, demonstrating impressive results in fields like natural language processing and computer vision. However, when applied to the domains of biology and chemistry, current LLMs face substantial limitations, particularly in capturing the nuanced relationships between the molecular structure and pharmacochemical properties. This challenge has constrained the application of few-shot learning for small-molecule generation and optimization in drug discovery. Here, we introduce DrugLLM, a novel LLM tailored specifically for molecular optimization. DrugLLM leverages Functional Group Tokenization (FGT), which effectively tokenizes molecules for LLM learning, achieving over 53% token compression compared to SMILES. Besides, we propose a new pre-training strategy that enables DrugLLM to iteratively predict and modify molecular structures based on a few prior modifications, aligning each modification toward optimizing a specified pharmacological property. In multiple computational experiments, DrugLLM achieved state-of-the-art performance in few-shot molecular generation, surpassing all the mainstream LLMs including GPT-4. Furthermore, by applying DrugLLM to optimize HCN2 inhibitors, two bioactive compounds were obtained and successfully validated through wet-lab experiments. These results highlight the robust potential of DrugLLM in accelerating the optimization of molecules and AI-driven drug discovery. Property (philosophy) ; Security token ; Face (sociological concept) ; Lexical analysis ; Language model ; Natural language ; Modeling language Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Dhruvi S. Kher | Raeesh Muhammad | Sandeep Kumar | Vijay Gupta | Swapnil Varhade | Brahim Aïssa | Manas Pal | Krunal Modi | Hyunchul Oh | Abdulkarem I. Amhamed
Source: Renewable and Sustainable Energy Reviews (article, 2026-02-19)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks and their derivatives for green energy fuel: Hydrogen production Hydrogen production ; Environmentally friendly ; Sustainability ; Fossil fuel ; Hydrogen ; Hydrogen economy ; Production (economics) ; Catalysis ; Energy carrier Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Ian Störmer | Julija Zavadlav
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations Interatomic potential ; Workflow ; Scaling ; Molecular dynamics ; Artificial neural network ; Deformation (meteorology) ; Morphing ; Threading (protein sequence) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: G. Laskaris | D. Morozov | D. Tarpanov | A. Seth | J. Procelewska | G. Sai Gautam | A. Sagingalieva | R. Brasher | A. Melnikov
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason we apply multi-objective hyperparameter optimization to the two objectives. Additionally, we experiment with modified architectures by making variants of Allegro some by adding strictly classical multi-layer perceptron (MLP) layers and some by adding quantum-classical hybrid layers. We compare the results from QM9, rMD17-aspirin, rMD17-benzene and our own proprietary dataset consisting of copper and lithium atoms. As results, we have a list of variants that surpass the Allegro in accuracy and also results which demonstrate the trade-off with inference times. Inference ; Deep learning ; Hyperparameter ; Perceptron ; Artificial neural network ; Equivariant map ; Interatomic potential ; Lithium (medication) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: A. Nyíri | Michael J. Waters | James M. Rondinelli
Source: ACS Photonics (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Normalized Descriptor for Unbiased Screening of Second-Order Nonlinear Optical Materials Second-order nonlinear optical materials enable frequency doubling of light (second-harmonic generation, SHG), which is essential for optoelectronic applications ranging from materials characterization to quantum technologies. However, comparing SHG performance across materials remains challenging as the second-order nonlinear susceptibility, χ(2), spans several orders of magnitude and strongly depends on the band gap, Eg. To address this, we empirically validate a theoretical upper bound on χ(2) using new databases of ab initio-computed nonlinear optical (NLO) properties. We then formulate a normalized descriptor, d̂, which expresses the NLO response of a material relative to the band gap-dependent physical limit. We show that d̂ exhibits a similar distribution across a wide range of band gap energies. We also demonstrate that the formalism used to compute χ(2) leads to a competition with methodological uncertainty driven by band gap predictions to systematically alter the prediction of the second-order nonlinear response. The universality of d̂ supports its use as a robust, generalizable metric for chemistry-informed machine-learning models and quantification of NLO performance, enabling accelerated materials discovery and optimization across broad application frequencies. Nonlinear system ; Ranging ; Band gap ; Universality (dynamical systems) ; Nonlinear optics ; Metric (unit) ; Nonlinear optical ; Upper and lower bounds Machine Learning in Materials Science ; Nonlinear Optical Materials Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Ian Störmer | Julija Zavadlav
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine experimentally. Molecular dynamics can bridge this gap, but only if the interatomic model is both accurate and computationally efficient. Here, we develop a Machine Learning Potential (MLP) for aluminum and demonstrate its near ab initio fidelity when trained with the sequential-refinement workflow that fine-tunes the model on low-energy structures. The favorable scaling of the model enables nanosecond simulations involving millions of atoms, thereby overcoming finite-size effects in simulations of polycrystalline solidification and subsequent mechanical testing. Comparison with classical potentials and recent MLP models, including a general-purpose model, shows that inaccuracies in stacking-fault energetics and diffusion can lead to qualitatively incorrect solidified grain structures and post-solidification mechanical behavior. Since our framework is based on an equivariant graph neural network, it allows for straightforward extensions to multi-component systems, providing valuable guidance for the future design and fine-tuning of both specialized and universal MLPs in computational mechanics simulations. Interatomic potential ; Scaling ; Workflow ; Molecular dynamics ; Artificial neural network ; Deformation (meteorology) ; Morphing ; Multiscale modeling ; Nucleation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Viktor Lilja | Albin J. Svärdsby | Timo Gahlmann | Philippe Tassin
Source: Laser & Photonics Review (article, 2026-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A General Framework for Knowledge Integration in Machine Learning for Electromagnetic Scattering Using Quasinormal Modes ABSTRACT Neural networks have been demonstrated to be able to accelerate the modeling and inverse design of optical and electromagnetic devices by serving as fast surrogates for electromagnetic solvers. Nevertheless, such neural networks can be unreliable and normally require extreme amounts of data to train. Here it is shown that these limitations can be alleviated by constraining neural‐network models using prior knowledge about the governing physics. We propose a universal physics‐informed neural network framework for electromagnetic scattering based on the quasinormal mode expansion of the scattering matrix. The neural networks learn the resonant structure underlying the scattering spectrum, are guaranteed to obey energy conservation and causality, and are shown to have significantly improved data efficiency for photonic‐crystal slabs and all‐dielectric free‐form metasurfaces. Furthermore, the framework allows additional problem‐specific constraints, such as losslessness, symmetries, and number of modes, to be imposed manually when they are available. The method can be applied to a wide range of optical and electromagnetic devices owing to the generality of the quasinormal mode formalism. Artificial neural network ; Scattering ; Inverse scattering problem ; Mode (computer interface) ; Range (aeronautics) ; Generality ; Electromagnetism ; Electromagnetic radiation Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Photonic Crystals and Applications
Authors: Kaiwen Jiao | Yingzhe Zhao | Yiran Dong | Lirong Han | Yali Chen | X.G. Qiao | Mingyuan Yin
Source: Journal of Future Foods (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted ratiometric fluorescence sensor array integrated multi-emission signal single sensing element for recognition of diverse tea based on boronic acid functional bimetallic lanthanide metal-organic frameworks • A LMOFs ratiometric fluorescence sensor array based on multi-emission signal single sensing element composites was prepared. • A highly sensitive LMOFs ratiometric fluorescence sensor array for TPs was designed and developed. • The machine learning techniques were combined with the LMOFs ratiometric fluorescence sensor array for the recognition of diverse tea products. The quality control and authentication of tea is important in the food safety. The developed fluorescence sensor array that enables to simultaneously determinate and discriminate diverse tea is still a nontrivial task. Herein, we proposed a ratiometric fluorescence sensor array based on a multi-emission signal single sensing element of bimetallic lanthanide metal-organic frameworks composites (LMOFs), wherein LMOFs were constructed by the coordination polymerization of lanthanide metals (europium and terbium ions) and the functional ligands (3,5-dicarboxybenzeneboronic acid) through one-pot method. The resulting LMOFs (excitation/emission with 260 nm/430 nm, 497 nm, 552 nm, 597 nm, and 622 nm) exhibited obvious differential fluorescence response change against tea polyphenols (TPs) due to that the antenna effect interfered by the borate ester covalent structure formed between the phenolic hydroxyl group of TPs with the boric acid of LMOFs. The generated ratiometric logical operation in the LMOFs fluorescence sensor array could reflect the “fluorescence fingerprints” of TPs and tea products, which were combined with machine learning techniques (linear discriminant analysis, hierarchical cluster analysis, and artificial neural networks) to realize the excellent recognition for five TPs (even at 1.0 µM) and the effective identification of 15 teas and 7 tea beverages as well as 100 % accuracy in blind samples test. Such LMOFs ratiometric fluorescence sensor array might provide a simple and efficient detection method for the authentication of tea products. Bimetallic strip ; Fluorescence ; Sensor array ; SIGNAL (programming language) ; Lanthanide ; Boronic acid Carbon and Quantum Dots Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Mauludi Ariesto Pamungkas | Annida Early Rofiah | Agus Naba
Source: Research Square (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

Bandgap Prediction of Binary Compounds via a Machine Learning Approach Utilizing Gradient Boosting Regression Overfitting ; Gradient boosting ; Boosting (machine learning) ; Binary number ; Mean squared error ; Mean squared prediction error ; Outlier ; Regression ; Binary classification ; Interpretability Machine Learning in Materials Science ; Material Selection and Properties ; Block Copolymer Self-Assembly
Authors: Duvvuri Surya Bhaskaram
Source: Critical Reviews in Analytical Chemistry (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Raman Spectroscopy of Reduced Graphene Oxide: A Review Highlighting Defect-Induced Bands and a Glimpse into Machine Learning Approaches Raman spectroscopy is a powerful tool for probing the structural and electronic properties of graphene-based materials, however, a focused review on reduced graphene oxide (RGO) is limited This review critically examines the Raman signatures of RGO, with a particular emphasis on defect-related bands arising from structural disorder and residual functionalities. The reliability and limitations of widely used intensity ratios and quantitative models are discussed, highlighting unconventional ratios for better analysis. By comparing recent advances, defect evolution can be decoded to better connect Raman features with physical and chemical properties. Emerging machine learning approaches for automated Raman analysis including preprocessing pipelines and algorithm strategies are also discussed. This work provides both a consolidated reference and a forward-looking perspective on Raman spectroscopy as a noninvasive tool for functional graphene materials. Raman spectroscopy ; Graphene ; Preprocessor ; Reliability (semiconductor) ; Spectroscopy ; Pipeline (software) Graphene research and applications ; Graphene and Nanomaterials Applications ; 2D Materials and Applications
Authors: Yang Hu | Min XIE | Xiaoyi ZHANG | Xiang Li | Xinwei Guo | Nan JIANG | Wenhan Zhou | Shengli ZHANG | Haibo ZENG
Source: Journal of Inorganic Materials (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Research Progress on Computational and Data-Driven Environmentally Friendly Luminescent Materials The development of traditional luminescent materials, such as cadmium-based quantum dots and lead halide perovskites, are intrinsically limited by their reliance on toxic heavy metals (e.g., Cd and Pb), which raises severe environmental and health risks throughout their lifecycles.Therefore, the transition toward eco-friendly alternatives-including cadmium-free quantum dots, lead-free halide perovskites, and rare-earth-doped phosphorshas become a pivotal research imperative.Currently, the design and optimization of such materials often rely on inefficient trial-and-error experimental paradigms, which often fail to overcome critical bottlenecks in luminous efficiency, environmental stability, and interfacial compatibility.This review systematically outlines the current landscape and technical challenges of eco-friendly luminescent materials.We highlight how computational techniques, particularly density functional theory (DFT), allow the accurate prediction of optoelectronic properties in core-shell structures and the elucidation of defect-induced non-radiative recombination mechanisms, thus facilitating rational material design and property optimization.In addition to theoretical calculations, data-driven technologies further accelerate material screening by leveraging standardized databases and machine learning models, having already yielded high-stability phosphors and high-efficiency narrowband emitters.Finally, we provide an outlook on the synergy between computational and data-driven approaches to overcome existing research and development barriers.Future efforts must focus on deepening the integration of these technologies to advance the practical deployment of eco-friendly luminescent materials in display and lighting applications, thereby driving the sustainable transformation of the optoelectronics industry. Environmentally friendly ; Luminescence ; Biocompatible material Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes
Authors: Ian Störmer | Julija Zavadlav
Source: arXiv (Cornell University) (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine experimentally. Molecular dynamics can bridge this gap, but only if the interatomic model is both accurate and computationally efficient. Here, we develop a Machine Learning Potential (MLP) for aluminum and demonstrate its near ab initio fidelity when trained with the sequential-refinement workflow that fine-tunes the model on low-energy structures. The favorable scaling of the model enables nanosecond simulations involving millions of atoms, thereby overcoming finite-size effects in simulations of polycrystalline solidification and subsequent mechanical testing. Comparison with classical potentials and recent MLP models, including a general-purpose model, shows that inaccuracies in stacking-fault energetics and diffusion can lead to qualitatively incorrect solidified grain structures and post-solidification mechanical behavior. Since our framework is based on an equivariant graph neural network, it allows for straightforward extensions to multi-component systems, providing valuable guidance for the future design and fine-tuning of both specialized and universal MLPs in computational mechanics simulations. Interatomic potential ; Scaling ; Workflow ; Molecular dynamics ; Artificial neural network ; Deformation (meteorology) ; Morphing ; Multiscale modeling ; Nucleation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: G. Laskaris | D. Morozov | D. Tarpanov | A. Seth | J. Procelewska | G. Sai Gautam | A. Sagingalieva | R. Brasher | A. Melnikov
Source: arXiv (Cornell University) (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason we apply multi-objective hyperparameter optimization to the two objectives. Additionally, we experiment with modified architectures by making variants of Allegro some by adding strictly classical multi-layer perceptron (MLP) layers and some by adding quantum-classical hybrid layers. We compare the results from QM9, rMD17-aspirin, rMD17-benzene and our own proprietary dataset consisting of copper and lithium atoms. As results, we have a list of variants that surpass the Allegro in accuracy and also results which demonstrate the trade-off with inference times. Inference ; Deep learning ; Hyperparameter ; Perceptron ; Artificial neural network ; Equivariant map ; Interatomic potential ; Lithium (medication) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks