jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhenzhi Tan | Qi Yang | Li Zhang | Sanzhong Luo
Source: Angewandte Chemie (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Synthetic Applicability Domain (SynAD): Navigating Chemical Space for Reliable AI‐Driven Reaction Prediction Abstract Organic synthetic chemistry has undergone a paradigm shift driven by breakthroughs in artificial intelligence (AI). Data‐driven methods help accelerate hypothesis evaluation and reduce experimental trial‐and‐error efforts. However, its practical utility is constrained by the out‐of‐distribution (OOD) issue, where predictions usually fail when extrapolating to unseen reactions with new catalysts, substrates, or conditions. Here, we introduce SynAD (synthetic applicability domain), a machine learning framework for assessing the predictive capability of AI models trained with existing data. SynAD combines descriptors with model‐adaptive distance metrics to automatically demarcate reliable and unreliable reactions. Validated on the Ullmann Ligand Dataset (ULD, &gt;5000 reactions), SynAD a priori distinguishes predictable chemical space, resulting in a prediction accuracy of R 2 = 0.90 (at 12.3% coverage) from a baseline of R 2 = −0.21. This capacity to target reliable chemical space is consistently observed across 6 additional datasets. We also enable a SynAD score to quantify reaction class predictability, guiding experimental focus on OOD spaces. By defining model limits, SynAD provides a critical guardrail for chemists to trust AI, allocate resources strategically, and accelerate de novo discovery. Chemical space ; A priori and a posteriori ; Applicability domain ; Domain (mathematical analysis) ; Class (philosophy) ; Focus (optics) ; Baseline (sea) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Matej Martinc | Goran Dražić | Anton Kokalj | Katarina Žiberna | Janina Roknić | Matic Poberžnik | Sašo Džeroski | Andreja Benčan Golob
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Benchmarking Machine Learning Approaches for Polarization Mapping in Ferroelectrics Using 4D-STEM Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical properties - such as polarization directions essential for understanding functional properties of ferroelectrics - remains a significant challenge. In this study, we systematically benchmark multiple machine learning models, namely ResNet, VGG, a custom convolutional neural network, and PCA-informed k-Nearest Neighbors, to automate the detection of polarization directions from 4D-STEM diffraction patterns in ferroelectric potassium sodium niobate. While models trained on synthetic data achieve high accuracy on idealized synthetic diffraction patterns of equivalent thickness, the domain gap between simulation and experiment remains a critical barrier to real-world deployment. In this context, a custom made prototype representation training regime and PCA-based methods, combined with data augmentation and filtering, can better bridge this gap. Error analysis reveals periodic missclassification patterns, indicating that not all diffraction patterns carry enough information for a successful classification. Additionally, our qualitative analysis demonstrates that irregularities in the model's prediction patterns correlate with defects in the crystal structure, suggesting that supervised models could be used for detecting structural defects. These findings guide the development of robust, transferable machine learning tools for electron microscopy analysis. External Data Representation ; Polarization (electrochemistry) ; Benchmarking ; Diffraction ; Benchmark (surveying) ; Ferroelectricity ; Convolutional neural network ; Experimental data Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Ferroelectric and Piezoelectric Materials
Authors: Hiroya Nakata | Cheol Ho Choi
Source: Journal of Chemical Theory and Computation (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Autonomous Reaction Discovery of CO <sub>2</sub> Capture in Aqueous Ammonia through Active-Learning Neural Networks The mechanistic origin of CO<sub>2</sub> capture in aqueous ammonia has long remained debated, with competing proposals invoking concerted versus stepwise pathways, ambiguous catalytic roles of ammonia, and uncertain product distributions. Here, we introduce an active learning, data-driven framework (ADRML) that integrates reactive molecular dynamics (RMD) with dimensionality-reduced sampling. RMD simulations employing the trained machine-learned interatomic potentials (MLIPs) on cluster models with periodic boundary conditions reveal that the [CO<sub>2</sub>]/[NH<sub>3</sub>] concentration ratio (<i>R</i><sub>[<i>C</i>]/[<i>A</i>]</sub>) is a critical determinant of product distributions, kinetics, and underlying mechanisms. At high <i>R</i><sub>[<i>C</i>]/[<i>A</i>]</sub>, carbonic species are favored via water-mediated hydration, whereas low <i>R</i><sub>[<i>C</i>]/[<i>A</i>]</sub> markedly promotes carbamate formation through a concerted ammonia-ammonia pair mechanism that becomes accessible only under ammonia-rich (low <i>R</i><sub>[<i>C</i>]/[<i>A</i>]</sub>) conditions. This disparity underscores a distinct concentration-dependent mechanistic shift in carbamate formation─from a stepwise to a concerted pathway. In addition, the hydronium ion (H<sub>3</sub>O<sup>+</sup>) generated in the carbonate-formation channel ultimately suppresses further reactivity by promoting the reverse process of carbonate hydrolysis and by depleting NH<sub>3</sub> through protonation. Overall, at low <i>R</i><sub>[<i>C</i>]/[<i>A</i>]</sub> ≈ 0.2, carbamate production surpasses carbonate formation in both rates and yields, occurring before significant H<sub>3</sub>O<sup>+</sup> accumulation from the carbonate channel, thereby maximizing CO<sub>2</sub> uptake. Both carbonate and carbamate formation reactions compete across the entire range of <i>R</i><sub>[<i>C</i>]/[<i>A</i>]</sub>. However, the dramatic enhancement of carbamate formation at low <i>R</i><sub>[<i>C</i>]/[<i>A</i>]</sub> is likely the primary source of long-standing ambiguities in these systems. Carbamate ; Hydronium ; Reactivity (psychology) ; Carbonate ; Aqueous solution ; Ammonia production ; Ammonia ; Concerted reaction Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; CO2 Reduction Techniques and Catalysts
Authors: Pengfei Li | Shilin Zhang | Jieming Wang | Siyu Liu | Xue Kang | Hui Shao | Jian Qi
Source: Small (article, 2026-02-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Confined Space in Hollow Micro/Nano Structures: Boosting Supercapacitor Performance to New Heights High-performance electrode materials are key to advancing supercapacitor technology. Hollow micro- and nanostructured materials with confined space effects act as precise "nanoreactors." These materials effectively regulate ion transport kinetics, enhance interfacial interactions, and stabilize the electrode/electrolyte interface, achieving rapid ion storage and efficient diffusion. This review systematically summarizes the latest advances in the application of such materials in supercapacitors. We first elucidate the definition, classification, and preparation methods of hollow micro-nano structures, then explore the advantages of confined space effects in ion restriction and selection, reaction kinetics optimization, and other aspects. Furthermore, structural design and functionalization modifications can further enhance specific capacitance, rate performance, and cycling stability. We also discuss the physicochemical mechanisms involved in promoting charge storage, suppressing ion aggregation, and mitigating volume changes. Notably, machine learning has shown great potential in guiding the precise and controllable synthesis of complex hollow micro/nano structures, providing new insights and strategies for material design. Despite potential, challenges like precise synthesis, mass production of complex structures, in-depth ionic behavior study in confined spaces, and long-term stability remain. Finally, we outline future research directions to offer theoretical and technical guidance for designing next-generation high-performance supercapacitor electrodes based on confinement effects. Supercapacitor ; Boosting (machine learning) ; Ion ; Confined space ; Electrode ; Surface modification ; Space charge ; Ionic bonding Supercapacitor Materials and Fabrication ; Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion
Authors: Damián Rodríguez‐Trujillo | Alicia E. Torres‐García | Mikel Aldea | Jorge Teniente | Asier Marzo‐Pérez | Miguel Beruete
Source: Advanced Optical Materials (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase Smoothing for Mutual Coupling Mitigation in Multifunctional Metasurfaces Designed with Diffractive Neural Networks (Advanced Optical Materials 1/2026) Multifunctional mmWave Metasurfaces This work presents a multifunctional diffractive neural network–designed metasurface capable of generating multiple holographic patterns in the W-band (86?GHz) while mitigating mutual coupling through phase smoothing. Experimental validation and broadband analysis (72–100 GHz) confirm high-fidelity wavefront control, demonstrating a passive, multifunctional mmWave holographic platform. More details can be found in the Research Article by Miguel Beruete and co-workers (DOI: 10.1002/adom.202502746). Holography ; Broadband ; Wavefront ; Coupling (piping) ; Phase (matter) ; Smoothing Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Neural Networks and Reservoir Computing
Authors: Tin Hadži Veljković | Joshua J. C. Rosenthal | Ivor Lončarić | Jan-Willem van de Meent
Source: ArXiv.org (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Crystalite: A Lightweight Transformer for Efficient Crystal Modeling Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We present Crystalite, a lightweight diffusion Transformer for crystal modeling built around two simple inductive biases. The first is Subatomic Tokenization, a compact chemically structured atom representation that replaces high-dimensional one-hot encodings and is better suited to continuous diffusion. The second is the Geometry Enhancement Module (GEM), which injects periodic minimum-image pair geometry directly into attention through additive geometric biases. Together, these components preserve the simplicity and efficiency of a standard Transformer while making it better matched to the structure of crystalline materials. Crystalite achieves state-of-the-art results on crystal structure prediction benchmarks, and de novo generation performance, attaining the best S.U.N. discovery score among the evaluated baselines while sampling substantially faster than geometry-heavy alternatives. Transformer ; Representation (politics) ; Crystal structure ; Geometric modeling ; Topology (electrical circuits) ; Crystal (programming language) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Z. Y. Zhang | Congwen Duan | Wenhao Xiao | Yidan Chen | Lunzhi Yin | Yu Cao | Haixiang Huang | Jianguang Yuan | Xia Yang | Sihan Tong | Ying Wu
Source: ENGINEERING Chemical Engineering (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Design and performance modulation of V-based hydrogen storage alloys: a review Hydrogen storage ; Quinary ; Enabling ; Energy storage ; Hydrogen ; Service (business) Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Catalysts for Methane Reforming
Authors: Guanming Chen | Maximilian Fleck | Thijs Stuyver
Source: ChemRxiv (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Leveraging Hidden-Space Representations Effectively in Bayesian Optimization for Experiment Design through Dimension-Aware Hyperpriors Hidden-space molecular representations derived from pretrained graph neural networks, Transformer models, and large language models offer an appealing alternative to conventional encodings for Bayesian optimization (BO) in chemical experiment design. However, their performance benefits in BO remain poorly understood and difficult to assess in practice, and, when deployed within current chemical BO frameworks, they often do not yield clear advantages over traditional representations. Here, we show that this behavior arises from a previously underappreciated interaction between representation dimensionality and Gaussian process kernel hyperpriors, rather than from limitations of the representations themselves. We demonstrate that different chemical representations induce substantial variations in search-space dimensionality, which, when paired with fixed or mismatched lengthscale hyperpriors, lead to flat marginal likelihood landscapes and severely degrade surrogate learning and acquisition optimization. To address this issue, we introduce a dimension-aware adaptive hyperprior, tailored to reaction optimization, whose characteristic lengthscale scales with the square root of the search space dimensionality. Benchmarking across multiple reaction optimization datasets and molecular representations shows that this adaptive hyperprior consistently restores and amplifies the performance advantages of hidden-space featurizations over traditional representations, such as Mordred or one-hot encoding. Our results identify hyperprior calibration as a critical requirement for fair evaluation and effective deployment of modern molecular representations in chemical BO. Bayesian optimization ; Gaussian process ; Curse of dimensionality ; Chemical space ; Bayesian probability ; Design of experiments ; Probabilistic logic ; Boosting (machine learning) ; Representation (politics) ; Process (computing) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms
Authors: Haoran Cui | Theodore Maranets | Yan Wang | Yifei Jin | Lei Cao
Source: Crystals (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Lattice Thermal Transport in Polymers: Atomistic Insights, Modeling Advances, and Design Perspectives Polymers are widely used in applications ranging from flexible electronics and thermal interface materials to structural composites and textile fabrics. Their inherently low κ, strongly governed by molecular structure and morphology, makes polymers a challenging yet scientifically rich class of materials for thermal transport studies. Over the past two decades, modeling and simulation have played a central role in elucidating heat transport mechanisms in polymers and in guiding the rational design of polymer systems with enhanced or tunable thermal properties. This review provides a comprehensive overview of the theoretical frameworks and computational approaches used to model thermal transport in polymers. We discuss atomistic methods including density functional theory, molecular dynamics, and first-principles Boltzmann transport equation approaches, as well as emerging data-driven and machine learning-based techniques. Special attention is devoted to the effects of chain conformation, crystallinity, orientation, interchain coupling, interfaces, and nanocomposite architectures. Current challenges and future research directions are highlighted, with particular emphasis on multiscale modeling, method integration, and predictive materials design. Thermal ; Thermal conductivity ; Molecular dynamics ; Electronics ; Polymer ; Boltzmann equation ; Multiscale modeling ; Polymer nanocomposite ; Lattice Boltzmann methods Thermal properties of materials ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Jakub Grudzien Kuba | Benjamin Kurt Miller | Sergey Levine | Pieter Abbeel
Source: arXiv (Cornell University) (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Offline Materials Optimization with CliqueFlowmer Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling methods are ineffective at boldly exploring attractive regions of the materials space due to their maximum likelihood training. In this work, we offer an alternative CMD technique based on offline model-based optimization (MBO) that fuses direct optimization of a target material property into generation. To that end, we introduce a domain-specific model, dubbed CliqueFlowmer, that incorporates recent advances of clique-based MBO into transformer and flow generation. We validate CliqueFlowmer's optimization abilities and show that materials it produces strongly outperform those provided by generative baselines. To enable its use in specialized materials discovery problems and support interdisciplinary research, we open-source our code and provide additional project information at https://github.com/znowu/CliqueFlowmer. Generative grammar ; Property (philosophy) ; Transformer ; Field (mathematics) ; Optimization problem ; Artificial neural network ; Code (set theory) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Material Selection and Properties
Authors: Marwan S. Mousa | Anas  Yousef Alreyahi | Saber Sâad Essaoud | Said M. Al Azar | Mufeed Maghrabi | Watheq Al-Basheer | Issam Trrad | Enas A. Arrasheed | Ammar Alsoud | Alexandr Knápek | Dinara Sobola
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Composition-Dependent Electronic, Optical, and Thermoelectric Properties of CsIn1-xGaxBr3 from DFT and Machine Learning Thermoelectric effect ; Density functional theory ; Band gap ; Semiconductor ; Figure of merit ; Electronic band structure ; Perovskite (structure) ; Dielectric Heusler alloys: electronic and magnetic properties ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Muhammad Irfan | Nabeela Bashir | AbdulGuddoos S. A. Gaid | Turke Althobaiti | Hamood Ur Rehman | Andrea Semaničová-Feňovčíková
Source: Scientific Reports (article, 2026-01-31)
Field: Chemistry — Inorganic Chemistry

Comparative entropy analysis of 2D transition metal tetrahydroxyquinones via machine learning approaches This paper focuses on the study of the framework of transition metal tetrahydroxyquinone (TM-THQ) which is a chemical belonging to the metal-organic framework (MOF) family. These materials are becoming relevant due to their stability and high surface area that makes them prospective in terms of carbon-dioxide conversion and other uses. To explore the structure of TM-THQ, we apply molecular graph theory, in which atoms and bonds are represented as mathematical graphs. We determine some topological indices, the Zagreb indices, Randić index, the atom-bond connectivity index, the geometric-arithmetic index, and the sum-connectivity index to explain the connection between atoms and predict the stability and reactivity of the material used. Moreover, Shannon's entropy model is applied to estimate the entropy of the indices. To this end, we develop and compare three machine learning based regression models: logarithmic, random forest, and XGBoost. All computational work, data analysis, and machine learning modeling were performed using Python. The findings reveal a strong predictive relationship, indicating that the entropy of TM-THQ can be accurately forecasted from its topological descriptors, with the ensemble methods providing superior predictive performance. Entropy (arrow of time) ; Stability (learning theory) ; Graph ; Regression ; Ensemble learning ; Topological index ; Principle of maximum entropy Metal-Organic Frameworks: Synthesis and Applications ; Graph theory and applications ; Computational Drug Discovery Methods
Authors: Soma Kosakai | Hiromasa Kaneko
Source: Results in Chemistry (article, 2026-03-26)
Field: Chemistry — Physical and Theoretical Chemistry

A method to define a domain in chemical space where only stable molecular structures exist In molecular designs using machine learning, molecules with good predicted values are searched by a model constructed with numerical structural information of the explanatory variable X, and the physical properties and activity values of the molecule as the objective variable Y. Molecular structures are mainly searched inside the applicability domain (AD) of the model, which defines the domain of reliable X values for predicted Y values. If no molecular structure with a good predicted value of y is found, the outer edges of the AD are also searched. However, if the attempt to synthesize the proposed molecular structure fails because it is unstable, the synthesis is inefficient because of the low prediction reliability due to the outer edges of AD. Here, we develop a method to define a chemical stability domain (CSD), where only stable molecular structures exist in chemical space, as a domain where the stability of the proposed molecular structure can be identified. CSD is a domain that can be judged stable if a molecular structure of unknown stability is inside it and unstable if outside it. Application of CSD to molecular design using machine learning makes it possible to filter out unstable molecules in advance. The domain defined by the k-nearest-neighbor method from the descriptor space occupied by real molecules was able to filter out unstable molecular structures, with 99.5% of the prepared stable compounds and 8.66% of the unstable molecular structures in the domain. Domain (mathematical analysis) ; Space (punctuation) ; Sequence (biology) ; Stability (learning theory) ; Topology (electrical circuits) Advanced Physical and Chemical Molecular Interactions ; Supramolecular Chemistry and Complexes ; Magnetism in coordination complexes
Authors: Mingyang Zhu | Sijie Wang | Yiting Lin | Honglai Liu | Cheng Lian
Source: Chemical Engineering Science (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Modeling the solvent effect on electric double layers using machine learning enhanced mesoscopic thermodynamics model Mesoscopic physics ; Process (computing) Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Advanced Physical and Chemical Molecular Interactions
Authors: Tao Sun | Jianmei Yuan
Source: Materials Today Physics (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

SPFlow: A flow-based symmetry-preserving generative model for crystalline materials Generative grammar ; Generative model ; Representation (politics) ; Mathematical model Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition
Authors: Eya Hadrich | Taha Chabbah | Saber Chatti | Houyem Abderrazak | Ibtissem Jlalia | Régis Merçier | Hamdi Ben Halima | Nicole Jaffrezic-Renault | Catherine Marestin | Hatem Ben Romdhane
Source: Emergent Materials (article, 2026-04-01)
Field: Chemistry — Organic Chemistry

Engineering pyridinic poly(arylene ether)s for olive oil by-product adsorption: experiments coupled with AI Adsorption ; Hydroxytyrosol ; Polymer ; Monomer ; Thermal stability ; Morpholine ; Effluent ; Selectivity Edible Oils Quality and Analysis ; Adsorption and biosorption for pollutant removal ; Surface Modification and Superhydrophobicity