jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiyoung Lee | Logan J. Augustine | Graeme Henkelman | Ping Yang | Danny Pérez
Source: ChemRxiv (preprint, 2025-01-31)
Field: Chemistry — Inorganic Chemistry

Data-Driven Kinetic Reaction Networks for Separation Chemistry Understanding complex, multi-step chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. In this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid-liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models which are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process, while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO2(NO3)2(DEHiBA)2, agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at low computational cost. Interpretability ; Leverage (statistics) ; Computer science ; Chemistry ; Biological system ; Machine learning Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Catalysis and Oxidation Reactions
Authors: Muhammad Asif Iqbal | Jian Zhi Hu | Naeem‐Ul‐Haq Khan | Hany M. Mohamed | Safaa N. Abdou | Salah M. El‐Bahy
Source: Solid State Communications (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Data-driven design of polymers for possible use for fluorescent applications Fluorescence ; Polymer ; Similarity (geometry) ; Cluster analysis ; Quantum yield ; Identification (biology) ; Set (abstract data type) ; Photoluminescence Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Jianfeng Li | Farag M. A. Altalbawy | Hardik Doshi | Anurag Yadav | R Jyothi R | Aditya Kashyap | Karthikeyan Jayabalan | Subhashree Ray | Dilsora Abduvalieva | Hojjat Abbasi | Ahmad Alkhayyat
Source: Journal of Energetic Materials (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Machine learning approaches for estimating formation enthalpies in energetic materials Work (physics) ; Process (computing) ; Energy (signal processing) Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Energetic Materials and Combustion
Authors: Paula Aniceto‐Ocaña | José Marqueses-Rodríguez | Juan M. Muñoz‐Ocaña | M. Jesús Fernández‐Trujillo | Andrés G. Algarra | Antonio M. Rodríguez‐Chía | José J. Calvino | Carmen E. Castillo | Miguel López‐Haro
Source: Small Methods (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

An AI‐Powered Methodology for Atomic‐Scale Analysis of Heterogenized Correlated Single‐Atom Catalysts Correlated single-atom catalysts offer transformative potential in catalysis, particularly in the field of electrocatalysis, with a focus on oxygen evolution reactions. Advanced characterization is critical to understanding their atomic-scale properties when techniques usually used in molecular science (Nuclear Magnetic Resonance (NMR), X-ray Diffraction (XRD), Infrared spectroscopy (IR), or Mass Spectrometry (MS)) cannot be applied after dispersing them on a carrier material. Here, a methodology that combines machine learning and mathematical optimization techniques to detect and quantify metal-metal interactions within heterobinuclear Au(III)-Pd(II) macrocyclic complexes on atomically resolved high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) images is introduced. Both supervised and unsupervised machine learning methods are evaluated, with the U-net architecture demonstrating superior performance in distinguishing the two involved chemical species. Mathematical optimization models further enhance the reliability of metal pair identification by providing precise distance metrics for the pairs. This methodology allows for the study of both the dynamics and bond interaction of heterobinuclear Au(III)-Pd(II) complexes. Notably, the analysis of time series of images reveals that most metal pairs remained stable under the high-energy electron beam irradiation conditions. Likewise, the Au-Pd distance within the pairs remains unchanged, indicating a robust interaction of the two metals with the ligand even after being deposited on the amorphous carbon substrate. Catalysis ; Atom (system on chip) ; Atomic units ; Scale (ratio) ; Materials science ; Chemistry ; Chemical engineering ; Computer science ; Physics ; Engineering ; Organic chemistry ; Parallel computing Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Zhen Tan (384977) | Xinyu Li (568185) | Rui Bai (376536) | Chang Guo (833590) | Xiao Han (102316) | Javen Qinfeng Shi (17297022) | Jian Zhang (1682) | Haobo Li (1454857)
Source: Figshare (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

AI for ComplexCatalytic Systems: High-Entropy Alloysin Electrocatalytic Acetylene Semihydrogenation AI-driven big data set analysis offers opportunities for theoretical research on systems that combine complex catalyst materials with intricate catalytic reactions. In this study, we explore high-entropy alloys (HEAs) as potential electrocatalysts for the electrochemical semihydrogenation of acetylene. HEAs provide a variety of surface active sites due to the diverse combination of constituent elements, while the presence of bidentate dicarbon species further complicates surface interactions. By integrating density functional theory computations, geometric optimizer development, and graph neural network-based machine learning predictions, we efficiently compile a comprehensive database of 52,900 adsorption properties for AgAuCuNiPd HEA surfaces. Lasso regression and t-SNE projection reveal the distinct influences of the five metal components on adsorption and reaction properties. Using Cu as a reference, logistic regression assesses the potential for other components to surpass Cu in terms of catalytic activity and selectivity toward ethylene. Our findings suggest that, while HEAs can enhance the reaction, the ternary AgAuCu alloy achieves optimal results, indicating that high entropy is not essential. This research methodology can be extended to other complex catalytic systems, providing valuable insights into catalytic mechanisms and facilitating experimental endeavors. Catalysis ; Ternary operation ; Adsorption ; Density functional theory ; Electrochemistry ; Artificial neural network ; Selectivity ; Alloy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: B. Jijila | S Ezhilarasi | V. Nirmala
Source: Journal of Molecular Modeling (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Machine-learned density functional based quantum chemical computations for ethane: performance of DeepMind 21 on potential energy surface and molecular properties Python (programming language) ; Density functional theory ; Potential energy ; Computation ; Inference ; Deep learning ; Potential energy surface ; Feature (linguistics) ; Artificial neural network Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Daisuke Wakabayashi | Kohei Shimamura | Fuyuki Shimojo | Akihide Koura
Source: Nihon Kessho Gakkaishi (article, 2025-05-31)
Field: Materials Science — Ceramics and Composites

Large-scale Molecular-dynamics Simulations of SiO&lt;sub&gt;2&lt;/sub&gt; Melt and Glass under High Pressure with Robust Machine-learning Interatomic Potentials We have carried out molecular-dynamics simulations of SiO2 melt and glass at 60 GPa in a system of about 30,000 atoms, with robust machine-learning interatomic potentials based on ab initio calculations. The large-scale simulations not only reproduced the ab initio calculations, but were also found to significantly improve the precision of the intermediate-range structure and reproduce the emergence of a nanometer-scale heterogeneous intermediate state during the pressure-induced phase transformation. Interatomic potentials determined using machine learning easily destabilize simulations of highly covalent melts under high pressure in an extended system with over one order of magnitude more atoms than those in the initial calculations used for data generation. The combination of potential averaging and active learning can efficiently suppress computational instability. Molecular dynamics ; Scale (ratio) ; Interatomic potential ; Materials science ; Dynamics (music) ; High pressure ; Chemical physics ; Chemistry ; Statistical physics ; Physics ; Thermodynamics ; Computational chemistry Glass properties and applications ; High-pressure geophysics and materials ; Machine Learning in Materials Science
Authors: Jiahui Liu | Shuo Cao | Yanzhou Wang | Zheyong Fan | Guocai Lv | Ping Qian | Yanjing Su
Source: ArXiv.org (preprint, 2025-07-16)
Field: Materials Science — Materials Chemistry

Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential The effect of chemical short-range order (CSRO) on primary radiation damage in MoNbTaVW high-entropy alloys is investigated using hybrid Monte Carlo/molecular dynamics simulations with a machine-learned potential. We show that CSRO enhances radiation tolerance by promoting interstitial diffusion while suppressing vacancy migration, thereby increasing defect recombination efficiency during recovery stage. However, CSRO is rapidly degraded under cumulative irradiation, with Warren-Cowley parameters dropping below 0.3 at a dose of only 0.03~dpa. This loss of ordering reduces the long-term enhancement of CSRO on radiation resistance. Our results highlight that while CSRO can effectively improve the radiation tolerance of MoNbTaVW, its stability under irradiation is critical to realizing and sustaining this benefit. Radiation damage ; Irradiation ; Radiation ; Radiation tolerance ; Diffusion ; Vacancy defect ; Stability (learning theory) ; Radiation hardening Nuclear Materials and Properties ; High Entropy Alloys Studies ; High Temperature Alloys and Creep
Authors: Ayat Bahaa Abdulhussein | Izzet F. Senturk
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Artificial Intelligence in Composite Materials: A Comprehensive Review Artificial neural network ; Composite number ; Feature (linguistics) ; Applications of artificial intelligence ; Expert system Machine Learning in Materials Science ; Smart Materials for Construction ; Advanced Sensor and Energy Harvesting Materials
Authors: Blake G. Hudson | Yueh‐Lin Lee | Harry Abernathy | Wissam A. Saidi
Source: The Journal of Physical Chemistry Letters (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr<sub>0.78</sub>Y<sub>0.22</sub>O<sub>3-δ</sub> Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein, we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr<sub>0.78</sub>Y<sub>0.22</sub>O<sub>3-δ</sub> using machine learning-accelerated <i>ab initio</i> molecular dynamics simulations. Our results reproduce the experimentally observed nonmonotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration, two mechanisms that influence the lattice expansion in opposite directions. This work provides fundamental insight into the coupling between hydration thermodynamics and lattice dynamics in BaZr<sub>1-<i>x</i></sub>Y<sub><i>x</i></sub>O<sub>3-δ</sub>, a prototypical proton-conducting perovskite, and offers a predictive framework for modeling temperature- and humidity-dependent behavior, critical to solid oxide fuel cell performance. Thermal expansion ; Lattice (music) ; Statistical physics ; Molecular dynamics ; Physics ; Thermodynamics ; Quantum mechanics Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Domenico Di Sante | Anirvan M. Sengupta
Source: The European Physical Journal Plus (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Focus point on machine learning for materials physics: from pitfalls to best practice Focus (optics) ; Point (geometry) ; Computer science ; Engineering physics ; Physics ; Nanotechnology ; Materials science ; Optics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Luisa Castellanos | CYPHER Workshop on Machine Learning for Complex Flows
Source: Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning Dynamical Latencies for the Parameterization of Complex Chemical Kinetics In this work, the implementation of time-lag autoencoders for chemical kinetics reductions is expanded to complex chemical kinetics (i.e, mechanisms with hundreds of species). A sampling methodology is analyzed for efficient hyperparameter optimization, enabling for a data-efficient methodology. Kinetics ; Chemical kinetics ; Deep learning ; Sampling (signal processing) ; Hyperparameter ; Chemical reaction kinetics ; Chemical Dynamics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Generative Adversarial Networks and Image Synthesis
Authors: Ramirez-Tagle Rodrigo
Source: Coordination Chemistry Reviews (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Computacional modeling of chirality in porous frameworks: Advanced methods and applications in enantioselective catalysis and separation Enantioselective synthesis ; Modular design ; Chirality (physics) ; Molecular dynamics ; Porous medium ; Quantum ; Rational design ; Multiscale modeling ; Scale (ratio) Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Surface Chemistry and Catalysis
Authors: Faisal Ahmed | Md. Rabiul Hasan | Mohammad Kamal Hossain
Source: Scientia. Technology, science and society. (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Review on Structural Disorder in Crystalline Materials: Atomic-Scale Origins, Characterization, and Implications This paper offers a thorough analysis of the atomic-scale causes of structural disorder, including lattice distortions, point defects, substitutional disorder, and correlations at both short and long ranges. Modern characterisation methods such as X-ray and neutron scattering, EXAFS, Raman/IR spectroscopy, transmission electron microscopy, and atom probe tomography are discussed, along with computational methods for defect prediction and property mapping like density functional theory, molecular dynamics, Monte Carlo simulations, and machine learning. We analyse the functional effects of disorder in various material classes, such as high-entropy alloys, perovskites, transition metal dichalcogenides, oxide semiconductors, and thermoelectrics, showing how intentional defect engineering can improve mechanical strength, optical response, conductivity, catalytic activity, and quantum functionalities. Real materials always contain atomic-scale structural disorder, such as vacancies, interstitials, antisite defects, substitutional dopants, lattice distortions, and local compositional fluctuations, despite the fact that crystalline materials are frequently idealised as perfectly periodic lattices. Such disorder is not merely an imperfection; rather, it can have a significant impact on mechanical, optical, magnetic, catalytic, and electrical characteristics, providing a flexible tool for materials design. Last but not least, we list the main obstacles—controlled synthesis, multiscale disorder-performance correlation, and integrating AI-driven high-throughput techniques and suggest future paths for using atomic-scale disorder as a design paradigm for functional materials of the next generation. Lattice (music) ; Density functional theory ; Crystallographic defect ; Characterization (materials science) ; Quantum ; Property (philosophy) ; Point (geometry) ; Monte Carlo method Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Entropy Alloys Studies
Authors: Aleksandr S. Bedniakov | Denis Bokhan | Maria M. Kolchenko | Dmitrii N. Trubnikov
Source: The Journal of Physical Chemistry A (article, 2025-06-17)
Field: Chemistry — Physical and Theoretical Chemistry

Basis-Free Study of Confined Atoms and Molecules Based on a Neural Network Approach One of the main problems in the analysis of cavity systems is the necessity to construct special basis sets that meet boundary conditions. The task often involves variational optimization of parameters for each molecular system, each type of potential, and cavity size, which makes the procedure very time-consuming. Here, we present a solution to this problem by using the variational quantum Monte Carlo method based on a neural networks approach, which needs no basis set and, hence, can be used "out-of-the-box" for a broad range of systems and potentials. This approach is particularly efficient for the calculation of systems in cavities of variable sizes, which is important in the study of high-pressure effects. Even for small systems, it provides results comparable to highly accurate variational estimates. For many-electron systems, it often outperforms methods based on Gaussian basis sets. Basis (linear algebra) ; Artificial neural network ; Molecule ; Computer science ; Statistical physics ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Geometry Various Chemistry Research Topics ; Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality
Authors: Jian Kang
Source: Applied and Computational Engineering (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

From Quantum to Data-Driven Models: The Role of Computational Methods in Battery Design Battery systems are important to humanity's daily life; among them, the lithium-ion battery is widely used in electric vehicles and portable electronics. The pursuit of better battery materials must remain ongoing to meet the growing demands of future society's needs. Computational study, unlike a traditional experimental study, is based on a theoretical study that relies on the prediction of material behavior and atomic-scale structural analysis through computational simulation. Recent advances in first-principles calculations and machine learning will bring more opportunities to the battery research field. This review focuses on the density functional theory and machine learning, discussing their recent advances, limitations, and future outlook on battery science, and analyzing their position on material development. In conclusion, the first principle calculation has significantly improved the efficiency for the new material development by analyzing their behavior theoretically, saving both time and cost. By integrating machine learning with the first principle simulation, it will significantly advance the progress of battery research and pave the way for future data-driven methodologies, such as neural networks that can address complex systems more efficiently, and quantum computing. Battery (electricity) ; Position (finance) ; Artificial neural network ; Quantum ; Computational model ; Position paper ; Quantum computer Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Physical and Chemical Molecular Interactions
Authors: Swati Tyagi | Pushpendra Singh | Kunwar Laiq Ahmad Khan
Source:  (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

A Comprehensive Analysis of Machine Learning Techniques in Advancing Composite Materials Composite materials are heavily implemented throughout many industries because they offer immense strength in combination with the ability to be lightweight without compromising their flexibility in a many-faceted environment. However, these are nonlinear and multi-dimensional materials; that require sophisticated models to predict the optimal properties and performance. Currently, an impressive method making predictions, optimizing, detecting defects in composites is ML as compared with considerable experimental tests. This paper reviews the current state of the art of advanced ML in the field of composite materials for different aspects such as property prediction, defect identification, design optimization and additive manufacturing. A comparative assessment also shows the challenges and the methods used and final results in various types of composites. The study also recognises the ML's capability to open a new path in composites material design and test with existing scales, interpretability, and integration issues. Computer science ; Composite number Machine Learning in Materials Science ; Internet of Things and AI ; Material Selection and Properties
Authors: Xiangwen Wang | Muyang Chen | Gengyi Bao | Lei Yan | Ji Cheng Gao | Xinhua Liu | Rui Tan
Source: ChemRxiv (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

High-Conductivity Electrolytes Screened using Fragment- and Composition-Aware Deep Learning Interpretability ; Electrolyte ; Deep learning ; Benchmark (surveying) ; Ranking (information retrieval) ; Battery (electricity) ; SPARK (programming language) Machine Learning in Materials Science
Authors: Masrur Ahmed | Jindal K. Shah
Source: ChemRxiv (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Discovery of IonicLiquid-Solvent Electrolytes Exhibiting HighIonic Conductivity Generalization ; NIST ; Electrolyte ; Ionic conductivity ; Overfitting ; Mixing (physics) ; Work (physics) ; Conductivity Machine Learning in Materials Science ; Ionic liquids properties and applications ; Electrochemical Analysis and Applications