jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kareem Abdelmaqsoud | Daniel Sinclair | Venkata Satya Surya Amaranth Karra | S. Mohadeseh Taheri-Mousavi | Michael Widom | Bryan A. Webler | John R. Kitchin
Source: ArXiv.org (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Computational Design of Ductile Additively Manufactured Tungsten-Based Refractory Alloys Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications in extreme environments such as nuclear fusion reactors. Additive manufacturing offers geometrical design freedom and rapid prototyping capabilities for these applications, provided the intrinsic brittleness and low printability of tungsten can be overcome. Designing tungsten alloys with improved ductility, and thus printability in additive manufacturing, can be accelerated using a computationally derived performance predictor to screen out brittle compositions. Calculations of the Pugh ratio using density functional theory may serve this purpose, given its correlation with ductility. This process can be made more efficient through the use of machine learning interatomic potentials to accelerate density functional theory calculations. Here, we demonstrate that machine learning interatomic potentials can effectively identify optimal alloy compositions in the W-Ta-Nb system along the melting point-Pugh ratio Pareto front. The trend in Pugh ratio as a function of tungsten fraction is explained in terms of the electronic density of states at the Fermi level. Experimental validation reveals a strong correlation between the computed Pugh ratio and the observed crack fractions in additively manufactured alloys. Notably, the two alloys predicted to have the highest Pugh ratio values, W20Ta70Nb10 and W30Ta60Nb10, exhibit no intergranular microcracking in experiments. Tungsten ; Brittleness ; Density functional theory ; Fusion ; Intermetallic ; Alloy ; Refractory metals ; Interatomic potential Fusion materials and technologies ; Advanced materials and composites ; Additive Manufacturing Materials and Processes
Authors: Yunqing Liu | Yi Zhou (2723) | Fan, Wenqi
Source: ArXiv.org (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions Molecule representation learning is crucial for understanding and predicting molecular properties. However, conventional atom-centric models, which treat chemical bonds merely as pairwise interactions, often overlook complex bond-level phenomena like resonance and stereoselectivity. This oversight limits their predictive accuracy for nuanced chemical behaviors. To address this limitation, we introduce \textbf{DeMol}, a dual-graph framework whose architecture is motivated by a rigorous information-theoretic analysis demonstrating the information gain from a bond-centric perspective. DeMol explicitly models molecules through parallel atom-centric and bond-centric channels. These are synergistically fused by multi-scale Double-Helix Blocks designed to learn intricate atom-atom, atom-bond, and bond-bond interactions. The framework's geometric consistency is further enhanced by a regularization term based on covalent radii to enforce chemically plausible structures. Comprehensive evaluations on diverse benchmarks, including PCQM4Mv2, OC20 IS2RE, QM9, and MoleculeNet, show that DeMol establishes a new state-of-the-art, outperforming existing methods. These results confirm the superiority of explicitly modelling bond information and interactions, paving the way for more robust and accurate molecular machine learning. Consistency (knowledge bases) ; Pairwise comparison ; Representation (politics) ; Property (philosophy) ; Covalent bond ; Regularization (linguistics) ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Hamideh Talebi | Farzin Emami | Kamran Kazemi
Source: Physica B Condensed Matter (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Models for Predicting the Localized Surface Plasmon Resonance of Metallic and Core-Shell Nanoparticles Hyperparameter optimization ; Plasmon ; Surface plasmon resonance ; Hyperparameter ; Stability (learning theory) ; Artificial neural network ; Support vector machine ; Nanoparticle ; Supervised learning Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Machine Learning in Materials Science
Authors: Haibo Mao | Hong Zhang | Yuan Zhang | Cristiano Francisco Woellner | Huiqiong Zhou
Source: ACS Applied Electronic Materials (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Descriptor Engineering for Machine-Learning-Based Performance Prediction in Organic Solar Cells: A Mini Review Organic photovoltaics (OPVs) represent one of the most dynamic fields in renewable energy research due to their potential for lightweight, flexible, and low-cost solar energy conversion. In recent years, the number of reported donor–acceptor (D/A) materials has increased exponentially, creating a vast chemical design space that is no longer manageable through conventional trial-and-error experimentation. Machine learning (ML) has emerged as a transformative tool to accelerate material discovery and performance prediction, capable of correlating molecular structure, device fabrication, and photovoltaic efficiency with unprecedented efficiency and scalability. However, the predictive accuracy of ML models depends critically on the selection of descriptors, the numerical quantities that translate molecular or device information into computable features. This review summarizes the conceptual framework and recent progress in descriptor-based ML studies for OPVs. We discuss data generation and pretreatment, classification, and interpretation of descriptors, and the construction and evaluation of predictive models. The effects of intrinsic, electronic, and optical descriptors are analyzed in detail. Special attention is given to feature-selection techniques such as SHapley Additive exPlanations (SHAP) and Pearson correlation analysis, which provide interpretability and quantitative insight. Finally, we highlight emerging directions, including morphology-aware descriptors derived from microscopy, multimodal data sets integrating theoretical and experimental data, and the coupling of ML with generative and physics-informed frameworks. Together, these advances are redefining how structure–property–performance relationships are understood and exploited in organic solar cell research. Interpretability ; Photovoltaic system ; Renewable energy ; Data-driven ; Experimental data ; Predictive modelling ; Generative grammar ; Solar energy ; Performance prediction Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Eric Johnsson | Shrinjay Sharma | A. Gangoli Rao | David Dubbeldam | Sofia Calero | Thijs J. H. Vlugt
Source: The Journal of Physical Chemistry C (article, 2026-03-10)
Field: Chemistry — Inorganic Chemistry

Predicting the Maximum Loading in Zeolites for Hydroisomerization Applications: A Machine Learning Approach Hydroisomerization of alkane isomers is an important step in the manufacture of current kerosene and sustainable aviation fuels. Zeolites are used as acid catalysts in this process. It is therefore important to have predictions of the adsorption capacity or maximum loading of hydrocarbons in zeolites. Here, a cascade model using machine learning models is used to predict the maximum loading of alkane isomers in zeolites. The cascade is composed of a gradient-boosted tree classifier stage that predicts whether adsorption occurs and a regressor predicting the value of the maximum loading. The final data set consists of 45 different adsorbates (both linear and branched alkanes up to C16) and 97 different zeolite structures, resulting in 4365 data points. Descriptors include information on the geometry and topology of zeolite channels as well as the shape and size of the adsorbates. Extra composite descriptors are also present to provide the physical basis for predictions. Multiple regressors of different natures are considered: support vector regressors, gradient-boosted trees, extreme gradient-boosted trees, and the TabPFN pretrained model. TabPFN yields the highest generalization performance and the lowest error. An interpretability analysis using SHAP reveals that the most influential descriptors are physically meaningful, highlighting steric and volumetric constraints as the primary factors controlling the prediction of qmax. It is shown that despite both the classifier and the regressor being insensitive to random splits in data, the regressor is prone to overfitting at low fractions of data withheld for testing. The cascade model is compared to an Artificial Neural Network for training and resource efficiency. Despite training being longer for the neural network, the final model is lighter in both memory and storage. This work is built on our previous research in predicting the Henry coefficients of long-chain alkanes in zeolites. Using this previous model and the findings of this work, one could construct the adsorption isotherm for any alkane, thus enabling the analysis of adsorption behavior of alkane mixtures using IAST. Interpretability ; Overfitting ; Artificial neural network ; Random forest ; Support vector machine ; Decision tree ; Generalization ; Classifier (UML) Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis for Biomass Conversion
Authors: Annabella DeBernardo | Nicholas E. Jackson
Source: The Journal of Chemical Physics (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Machine-learning graph convolutional electronic propagators We present a graph-based machine-learning framework for simulating the time evolution of electronic wavefunctions and densities in quantum systems. Inspired by parallels between time-dependent quantum propagators and spectral graph convolutions, we employ a recursive Chebyshev graph neural network architecture capable of learning the dynamics of electronic processes across a range of external potentials and Hamiltonians. Two model variants are introduced: one that evolves the full complex-valued wavefunction and the other that propagates only the electron density. Both models are trained on trajectory data generated from tight-binding Hamiltonians and a time-dependent electron-phonon coupled system. Our results demonstrate that wavefunction-based models achieve near-exact long-time propagation across static and dynamic regimes, while density-only models maintain strong performance using physics-informed loss functions, even in the absence of phase information. This work lays the foundation for coarse-grained, resolution-independent propagators for electronic dynamics and opens new pathways for scalable quantum simulations in complex molecular and condensed-phase systems. Propagator ; Wave function ; Quantum ; Graph ; Electronic structure ; Chebyshev polynomials ; Graph theory ; Trajectory Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Durgesh Ameta | Ajeet Kumar | Tanishk Saini | Laxmidhar Behera
Source: ChemRxiv (article, 2026-03-09)
Field: Chemistry — Analytical Chemistry

A3M-CIR: An Attention-Aware Adversarial Masking Based Self-Supervised Chemometric Framework for Robust Infrared Spectral Analysis Infrared (IR) spectroscopy provides rich molecular fingerprint information and is widely used for qualitative and quantitative chemical analysis across biomedical, environmental, and materials science applications.However, its practical utilization is often constrained by instrument-dependent variability, baseline drift, acquisition noise, and limited labeled data.While deep-learning-based chemometric methods have improved performance over traditional preprocessing-driven pipelines, their reliance on supervised learning limits robustness and transferability across heterogeneous measurement conditions.To address these challenges, we introduce A3M-CIR, an attention-aware adversarial masking-based self-supervised chemometric framework for robust infrared spectral analysis.The framework performs large-scale selfsupervised pretraining on diverse unlabeled IR spectra to learn transferable representations without explicit calibration or manual annotation.Its core component, the Attention-Aware Adversarial Feature Masking Block (A3FMB), suppresses diagnostically salient absorption regions to generate informative contrastive views that emulate realistic perturbations, while a lightweight Dual-Mixing Attention (DMA) encoder captures both global spectral context and local continuity of one-dimensional vibrational signals.Extensive evaluations under linear-probing and fine-tuning protocols demonstrate consistent improvements over representative self-supervised baselines, and systematic signalto-noise ratio analyses further validate robustness and transferability under varying noise conditions.By explicitly addressing spectral variability, measurement noise, and limited supervision as intrinsic challenges of infrared spectroscopy, this work establishes A3M-CIR as a practical and extensible foundation for datadriven infrared spectral analysis. Masking (illustration) ; Pattern recognition (psychology) ; Adversarial system ; Principal component analysis ; Spectral analysis ; Noise (video) Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Computational Drug Discovery Methods
Authors: Zhiye Deng | Song Xue | Yang Zhang | Ting Song | Xilong Dou
Source: Computational Condensed Matter (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

High carrier mobility and thermoelectric transport in 2D FeOCl-type HfNX (X = F, Cl, Br) monolayers Thermoelectric effect ; Monolayer ; Thermal conductivity ; Electron mobility ; Thermoelectric materials ; Boltzmann equation ; Phonon ; Semiconductor Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Radu A. Talmazan | Christophe Chipot
Source: Journal of Chemical Information and Modeling (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

From Static Pathways to Dynamic Mechanisms: A Committor-Based Data-Driven Approach to Chemical Reactions As computational chemistry methods evolve, dynamic effects have been increasingly recognized to govern chemical reaction pathways in both organic and inorganic systems. Here, we introduce a committor-based workflow that integrates a path-committor-consistent artificial neural network (PCCANN) with an iteratively trained hybrid-DFT-level message passing atomic convolutional encoder (MACE) potential. Beginning with a static nudged elastic band path, PCCANN extracts a committor-consistent string to represent the reactive ensemble. We illustrate the power of this methodology through two representative applications. First, we investigate an S<sub><i>N</i></sub>Ar reaction using MACE trained at hybrid DFT level with implicit solvent. The mechanism is found to be concerted, and the dynamic approach reveals a lower barrier than static treatments. Second, we apply the same protocol to the isomerization of protonated isobutanol to protonated 2-butanol, yielding a quantitatively accurate free-energy landscape. We uncover three competing channels: the established concerted mechanism and two asynchronous stepwise routes mediated by water and methyl transfer, all with comparable activation barriers. Notably, the stepwise pathways traverse metastable intermediates that, to the best of our knowledge, have not been described in prior mechanistic studies. Calculated barrier heights and intermediate stabilities are in close agreement with high-level DFT benchmarks, demonstrating the framework's accuracy. Together, these studies highlight mechanistic diversity across distinct systems and establish the synergistic PCCANN-MACE protocol as a proof-of-concept approach for committor-based discovery of complex reaction dynamics. Chemical reaction ; Protonation ; Mechanism (biology) ; Reaction mechanism ; Reaction intermediate ; Isomerization ; Metastability Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Asymmetric Hydrogenation and Catalysis
Authors: S.F. Solodovnikov | M. Zeraati | R.E. Stefanov | E.S. Zolotova | A.R. Nasyrbaev | I.P. Gulyaev | I.K. Igumenov | R.A. Shutilov | E.A. Maksimovsky | D.P. Pishchur | V.N. Yudin | V.V. Lukashov | I.V. Korolkov | A.P. Maltsev | A.R. Oganov
Source: Ceramics International (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Thermal and mechanical properties of double perovskite-type Ba2YNbO6 ceramic Thermal conductivity ; Spark plasma sintering ; Thermal barrier coating ; Thermal expansion ; Differential scanning calorimetry ; Vickers hardness test ; Ceramic ; Tetragonal crystal system ; Interatomic potential Thermal Expansion and Ionic Conductivity ; Microwave Dielectric Ceramics Synthesis ; Ferroelectric and Piezoelectric Materials
Authors: Shiwei Fan | Hongbin Yin | Xu Tang | De Lv | Kun Peng | Hongrui Zhang | Xiao Li | Jing Lv | Baoying Lv | Ming Zhou
Source: Chemical Engineering Science (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Synergistic optimization for enhancing electrical conductivity of Cu@Ag powder by RSM-ANN-PSO: An efficient and economical approach Response surface methodology ; Particle swarm optimization ; Electrical resistivity and conductivity ; Electrical conductor ; Artificial neural network ; Process (computing) ; Particle size ; Design of experiments ; Process optimization Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Nadeem Hussain Solangi | Rana R. Neiber | Bharat Prasad Sharma | Jai Kumar | Jingmeng Jiao | Mazhar Ali | Maokuan Guo | Jun Lu
Source: Small (article, 2026-01-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tuning Charge Storage in Bimetallic CoV–LDH for High‐Performance Supercapacitor: A Synergistic Experimental and Machine Learning Approach The partial reduction of layered double hydroxides (LDHs) is becoming a vital approach to harness their electrochemical capabilities for high-performance supercapacitors (SCs). This paper provides a synergistic experimental and theoretical data study of controlled partial reduction, density functional theory (DFT), and machine learning (ML) to design the oxygen vacancy (Vo) chemistry of cobalt vanadium layered double hydroxides (CoV-LDHs). A solution-based partial-reduction protocol introduces Vo and provides the opportunity to precisely modulate the LDH lattice with a significant enhancement of charge-storage performance. The Vo-CoV-LDH electrode exhibits a specific capacitance of 2437 F g<sup>-</sup> <sup>1</sup> at 2 A g<sup>-</sup> <sup>1</sup>, significantly surpassing its unmodified CoV-LDH (1371 F g<sup>-</sup> <sup>1</sup>). Moreover, it delivers 78.4% capacitance retention at escalating current densities (2-10 A g<sup>-</sup> <sup>1</sup>), in contrast to 55% for the untreated LDH. Incorporated into an asymmetric supercapacitor (ASC) device, Vo-CoV-LDH attained a remarkable energy density of 47.1 W h kg<sup>-</sup> <sup>1</sup> at a power density of 468.1 W kg<sup>-</sup> <sup>1</sup>. DFT simulations reveal that the availability of Vo causes the bandgap to be narrower and the number of states near the Fermi level to be higher to accelerate electronic conductivity and redox dynamics. Simultaneously, machine-learning models are used to explain quantitative relationships between the parameters of synthesis, concentration of vacancies, and electrochemical performance, with coefficients of determination of more than 0.98. The findings support experimental reproducibility and predictive accuracy. The work demonstrates the synergistic efforts of partial reduction, DFT knowledge, and ML modeling to design Vo-engineered LDHs and, thus, a generalizable approach to the creation of an advanced energy storage material is demonstrated. Bimetallic strip ; Supercapacitor ; Capacitance ; Density functional theory ; Energy storage ; Electrochemistry ; Partial charge ; Electrode ; Vanadium Supercapacitor Materials and Fabrication ; Layered Double Hydroxides Synthesis and Applications ; Catalysis for Biomass Conversion
Authors: José Roberto Vega Baudrit | Yendry Regina Corrales-Ureña | Karla J. Merazzo | Javier Stuardo Chinchilla Orrego | Mary Lopretti
Source: Foods (article, 2026-04-08)
Field: Chemistry — Organic Chemistry

Advances in Nanotechnological Strategies for Preserving and Authenticating Bioactive Compounds in Extra Virgin Olive Oil: Nano-Enabled Stabilization, Sensing, and Circular Valorization Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Traceability ; Olive oil ; Shelf life ; Active packaging ; Sustainability ; Biomass (ecology) ; Quality (philosophy) ; Health benefits Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis ; Essential Oils and Antimicrobial Activity