jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sissi Feng | Brandon R. Sutherland | Ali Shayesteh Zeraati | Yang Bai
Source: Matter (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Purposeful imperfection in materials representation Representation (politics) ; Feature (linguistics) ; Face (sociological concept) ; Context (archaeology) ; Key (lock) Material Selection and Properties ; Machine Learning in Materials Science ; Quasicrystal Structures and Properties
Authors: Jie Jack Li | Bin Zou | Xingna Zheng | Yongxin Li | Hui Huang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Novel Strategy for Accurately Distinguishing Chinese Tea by Simultaneously Sensing of Tea Infusions and Tea Aroma Based on Multi-metal Nanozymes Workflow ; Chinese tea ; Signal processing ; Focus (optics) ; SIGNAL (programming language) ; Pattern recognition (psychology) Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Nanocluster Synthesis and Applications
Authors: Yury  D. Fomin
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

The origin of complex behavior of liquid carbon: an insight from computer simulation Molecular dynamics ; Point (geometry) ; Carbon fibers ; Experimental data ; Liquid state ; Liquid water ; Melting point Advanced Physical and Chemical Molecular Interactions ; Material Dynamics and Properties ; Fullerene Chemistry and Applications
Authors: Qing Tang | Yuping Chen | Xiaoyan Wu | Jingjing Liu | Zhou Huang | Qiong Luo | Luming Meng
Source: Angewandte Chemie International Edition (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

HALOM Method: An Efficient and Accurate Neutron Diffraction‐Independent Approach for Determining Hydrogen Locations in Metal Hydride Nanoclusters Metal hydride nanoclusters play important roles in catalytic transformations and energy conversion. However, accurate localization of hydrogen atoms in these nanoclusters remains a grand challenge. Traditional techniques face inherent limitations that single-crystal X-ray diffraction cannot localize hydrides effectively, while neutron diffraction requires large single crystals and has limited accessibility. To address this challenge, we developed an innovative machine-learning-assisted hydrogen atom locator for metal hydrides (HALOM) algorithm for rapid and accurate hydrogen position prediction. This approach systematically searches potential hydrogen atom locations using a predefined potential function and refining these locations to minimize the total energy, which operates independently of neutron diffraction data and avoids exhaustive exploration of the potential energy surface (PES). In terms of prediction accuracy, HALOM fully reproduces neutron diffraction (ND)-identified hydride sites and aligns perfectly with density functional theory (DFT) models, and even yields lower-energy structures than literature DFT placements in some key systems (e.g., 2.86 eV lower for Cu<sub>81</sub>H<sub>32</sub>). HALOM also demonstrates superior computational efficiency, which generates low-energy hydrogen configurations in only seconds, compared to the expensive PES exploration for hours. Overall, HALOM offers an efficient and accurate tool for hydrogen localization in metal hydride nanoclusters to advance their structure-property research. Nanoclusters ; Neutron diffraction ; Hydride ; Hydrogen ; Diffraction ; Hydrogen storage ; Atom (system on chip) Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Justin Airas | Bin Zhang
Source: PubMed Central (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long-timescale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free-energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that is transferable across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational implicit solvent model poised to accelerate the development of predictive, large-scale simulation tools. Protein folding ; Folding (DSP implementation) ; Solvent models ; Process (computing) ; Computational model ; Artificial neural network ; Protein structure prediction ; Unified Model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Norhazwani Md Yunos | Camellia Doroody | Ong Ke Sheng | Hasrul Nisham Rosly | Chenyoushi Xu | Zheng-Jie Feng | Zuraini Othman | Yuan Dong
Source: Journal of Electronic Materials (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

High-Precision Classification of Transparent Conductive Oxides with Optical Signatures using XGBoost Algorithm for Accelerated Material Screening Abstract This study introduces a machine learning (ML) adaptive interface for classifying transparent conductive oxide (TCO) materials by examining their optical characteristics, such as their wavelength, optical density, absorption, and transmission. Six supervised ML models were considered in this study, namely decision tree, random forest, extreme gradient boosting (XGBoost), support vector classifier (SVC), K -nearest neighbors (KNN), and multilayer perceptron (MLP). These models were evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrices, receiver operating characteristic (ROC) curves, and learning curves. The comparison findings revealed that XGBoost delivered the best performance, achieving an accuracy of $$96.21\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>96.21</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and a perfect area under the curve (AUC) score of 1.00, effectively distinguishing all five sampled TCO classes: aluminum-doped zinc oxide (AZO), fluorine-doped tin oxide (FTO), indium tin oxide (ITO), magnesium-doped zinc oxide (MZO), and zinc oxide (ZnO). These findings reveal ML’s potential for both scientific and industrial integration in material science research. XGBoost, as a high-precision model, may significantly reduce process time and equipment-intensive laboratory characterizations by allowing for the rapid and precise classification of data from optical measurements. This expedites the search for materials suitable for use in photovoltaics, optoelectronics, and display technologies, all of which require high transparency and conductivity. Overall, this work expands the potential for using ML to categorize materials, particularly XGBoost. It provides both scientific understanding and practical applications in innovative substance informatics and industrial quality control. Support vector machine ; Indium tin oxide ; Transparent conducting film ; Tin oxide ; Perceptron ; Oxide ; Classifier (UML) ; Multilayer perceptron ; Zinc ZnO doping and properties ; Nanomaterials and Printing Technologies ; Biosensors and Analytical Detection
Authors: Jackson Geary | Dayton Jonathan Vogel | Melissa L. Meyerson | Paul G. Kotula | Caith E Mckeown | Madeline Steinberg | Dorina F. Sava Gallis
Source: Angewandte Chemie (article, 2026-01-10)
Field: Chemistry — Inorganic Chemistry

Realizing Cocktail Effects in Catalytic High‐Entropy Metal–Organic Frameworks (HEMOFs) via Predictive Density of State Calculations Abstract High‐entropy materials, particularly high‐entropy metal–organic frameworks (HEMOFs), represent a promising class of catalysts amenable to imparting superior activity via harnessing cocktail effects. However, optimal leveraging of these effects remains an outstanding challenge. Here, we introduce a promising, computationally driven roadmap for the rational selection of metal compositions in catalytic HEMOFs. Density functional theory (DFT) was first used to probe a key catalytic intermediate for CO 2 epoxidation in a series of compositionally related high‐entropy polynuclear clusters. A direct correlation between composition and predicted catalytic activity trends was established, capitalizing on clear differences in a new valence edge peak in the band gap as function of active metal site. Following that, a series of HEMOFs with DFT‐predetermined compositions were successfully synthesized. Remarkably, catalytic tests demonstrated the trends predicted by DFT, emphasizing the direct correlation between electronic structure and activity. The DFT‐predicted optimal HEMOF composition was validated experimentally and shown to exhibit over 40% greater activity than the least active variant. The strategy described herein demonstrates the immense promise of harnessing cocktail effects in HEMOFs, wherein synergistic intermetallic effects impart superior catalytic activity. Finally, this work serves as a first step toward enabling machine learning‐driven approaches to optimize catalytic performance through tailored metal compositions. Catalysis ; Valence (chemistry) ; Density functional theory ; Intermetallic ; Rational design ; Metal ; Work (physics) ; Band gap ; Characterization (materials science) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Kang Li | Jing Li | Pu Wang | Yong Pei
Source: Accounts of Chemical Research (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Theoretical Insights on the Regulatory Mechanisms of Structure and Doping on the Photoluminescence of Ligand Protected Gold Nanoclusters ConspectusLigand-protected gold nanoclusters (Au NCs) occupy a unique region between molecules and bulk metals, garnering significant attention due to their atomically precise structure and size- and structure-dependent optical properties. Notably, their tunable emission characteristics, excellent photostability, and biocompatibility make them promising candidates for bioimaging, sensing, and optoelectronic applications. However, the relatively low photoluminescence quantum yield (PLQY) of most Au NCs hinders their practical application. Furthermore, the low PLQY of Au NCs is closely associated with their complex excited-state dynamics: the interactions between the metal core and ligand shell, coupled with the strong spin-orbit coupling (SOC) effect, collectively induce diverse excited-state relaxation pathways, which render the regulatory mechanism of photoluminescence (PL) difficult to decipher precisely.This Account summarizes recent theoretical insights into the PL mechanisms of ligand-protected gold and other coinage metal (Ag and Cu) nanoclusters. We systematically examine how fundamental structural factors, such as core-shell structure, isomer effects, size, and heteroatom doping, modulate the respective radiative and nonradiative decay channels that determine the clusters' PL properties. In addition, nonadiabatic molecular dynamics (NA-MD) simulations incorporating many-body (MB) treatment of electronic excited states yields relaxation time scales in better agreement with experiments. Finally, we introduce application of a graph convolutional neural network (GCNN) for the rapid and accurate prediction of UV-vis absorption spectra, demonstrating the potential of machine learning in this field. Collectively, these insights establish key principles: size and symmetry dictate radiative rates, ligand rigidity suppresses nonradiative decay, and heteroatom doping provides a flexible handle to tune excited-state pathways. Moving forward, integrating NA-MD with data-driven approaches will be critical for bridging atomic-scale structures with macroscopic optical performance. These advances are essential for the rational design of highly efficient luminescent clusters in light harvesting, optoelectronics, and biomedical applications. Nanoclusters ; Photoluminescence ; Heteroatom ; Photoexcitation ; Relaxation (psychology) ; Quantum yield ; Excited state ; Doping Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Mingyuan Wang | Yaqi Zhang | Bowen Xiong | Ke Wang | Xiangzhao Zhang | Jian Yang | Lin Xu | Guanjun Qiao | Guiwu Liu
Source: Journal of Advanced Ceramics (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

First-principles calculations informing machine learning framework and visualization system for rapid and generalized gas response prediction in black phosphorus sensors Gas sensors are vital in practical applications, yet the efficient screening of sensing materials remains a formidable challenge. Conventional trial-and-error approaches are costly, single descriptors fail to capture complex interactions, and multi-parameter combinations introduce nonlinearities. To overcome these limitations, we propose a synergistic strategy that integrates first-principles calculations with machine learning (ML) for rapid prediction of gas sensitivity. Using black phosphorus (BP) as a model system, we evaluated its responsiveness to 21 gases by analyzing adsorption-induced electronic and structural changes. Key descriptors extracted from these calculations were used to train six ML models. The extra trees (ET) model demonstrated exceptional robustness, achieving 96% accuracy with minimal deviation in five-fold cross-validation and top-tier performance in F1-score evaluation. Furthermore, analyses of feature importance and SHapley Additive exPlanations (SHAP) identified adsorption energy, p-orbital center, valence band maximum, conduction band minimum, and Fermi level as the dominant descriptors. We also developed a lightweight, Python-based prediction and visualization system. By inputting only these five key features obtained from first-principles calculations, this tool enables real-time assessment of BP's response to various gas molecules. This integrated approach showcases significant potential for predicting material sensing properties and offers valuable theoretical guidance for gas sensor design. Visualization ; Black phosphorus ; Key (lock) ; Feature (linguistics) ; Thermal conduction ; Valence (chemistry) 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Machine Learning in Materials Science
Authors: Shuya Guo | Kexin Guan | Yizhen Situ | Xueying Yuan | Yujuan Yang | Shuhua Li | Z. H. Ming | Min Zhang | Shouxin Zhang | Heguo Li | Zili Liu | QingYuan YANG | Kaikai Ma | Hong Liang | Yue Zhao | Yufang Wu | Zhiwei Qiao
Source: Engineered Science (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Big Data Mining and Transferable Machine Learning for the High Accurate Prediction of Gas Separation Metal-Organic Framework Membranes In the paper, the physical properties of 21 gas binary mixtures were combined with the basic physical descriptors of metal-organic framework membranes (MOFMs) as descriptors to train the machine learning model for evaluating the gas permeation and separation performance of MOFMs.The highly accurate (R 2 = 0.935) extreme gradient boosting (XGBoost) model demonstrated outstanding generalization capabilities, effectively predicting the permeation performance of different gas binary mixtures in both the Computation-Ready Experimental MOFMs database and the hypothetical MOFMs database.Furthermore, the trained XGBoost model has been encapsulated to provide a user-friendly interface and allow other researchers to utilize the model for gas permeation predictions efficiently.The Shapley Additive Explanations analysis based on the trained XGBoost model was employed, revealing that gas polarizability is a key factor governing gas permeation performance.Additionally, two distinct membrane separation mechanisms dominated by adsorption and diffusion effects were identified for the CO2/M, He/X and H2/Y (M = CH4, H2S, N2, O2; X = CH4, N2, O2, CO2, H2S, H2; Y = CH4, N2, O2, CO2, H2S) binary mixtures through big data mining.The datadriven approach enhances molecular permeation prediction, aiding the design of high-performance membranes for complex gas mixtures. Big data ; Separation (statistics) ; Pattern recognition (psychology) ; Artificial neural network ; Support vector machine Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Carbon Dioxide Capture Technologies
Authors: Penghua Ying | Ting Liang | Yun Chen | Yan Chen | Shiyun Xiong | Zheyong Fan | Jianbin Xu | Yilun Liu
Source: arXiv (Cornell University) (preprint, 2026-04-04)
Field: Materials Science — Materials Chemistry

Structurally Triggered Breakdown of the Phonon Gas Model in Crystalline Metal-Organic Frameworks While crystalline materials with glass-like thermal conductivity are fundamentally intriguing, structurally triggering the transition from propagating to diffusive heat transport within a single framework remains a formidable challenge. Here, using extensive machine learning molecular dynamics, we demonstrate a fundamental thermal transport crossover in metal-organic frameworks. We reveal that grafting flexible side chains onto a pristine MOF backbone acts as a structural switch, strongly reducing the thermal conductivity by $\sim$70% (from $\sim 0.7$ to $\sim 0.2\ \text{W m}^{-1}\text{K}^{-1}$ at 300 K). Crucially, the functionalized derivatives exhibit a drastic transition from a classical Peierls $\sim 1/T$ decay to an anomalous, temperature-independent glass-like plateau. Reciprocal- and real-space analyses reveal the microscopic origins: the side chains act as built-in local resonators that trap acoustic energy via strong low-frequency resonant hybridization, while simultaneously inducing extreme steric crowding. Consequently, the heat-carrying phonon modes become critically damped, with their mean free paths strictly confined to the nanometer scale and their lifetimes collapsing to the Ioffe-Regel limit. This work establishes a highly programmable molecular engineering strategy to dismantle the phonon gas model, forcing crystalline frameworks into an extreme diffusive transport regime. Phonon ; Thermal conductivity ; Work (physics) ; Thermal ; Molecular dynamics ; Nanometre ; Steric effects ; Resonator Thermal properties of materials ; Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science
Authors: Ateeq Ur Rehman | Sandra Corasaniti | Ivano Petracci | Paolo Coppa | Dario Atzori
Source: International Journal of Thermophysics (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Numerical and Machine Learning Methods to Predict the Thermal Behaviour of New Photovoltaic Cells in Order to Increase Their Efficiency Photovoltaic system ; Thermal ; Order (exchange) ; Support vector machine ; Numerical analysis Chemical and Physical Properties of Materials ; solar cell performance optimization ; Silicon and Solar Cell Technologies
Authors: Jackson Geary | Dayton Jonathan Vogel | Melissa L. Meyerson | Paul G. Kotula | Caith E Mckeown | Madeline Steinberg | Dorina F. Sava Gallis
Source: Angewandte Chemie International Edition (article, 2026-01-10)
Field: Chemistry — Inorganic Chemistry

Realizing Cocktail Effects in Catalytic High‐Entropy Metal–Organic Frameworks (HEMOFs) via Predictive Density of State Calculations High-entropy materials, particularly high-entropy metal-organic frameworks (HEMOFs), represent a promising class of catalysts amenable to imparting superior activity via harnessing cocktail effects. However, optimal leveraging of these effects remains an outstanding challenge. Here, we introduce a promising, computationally driven roadmap for the rational selection of metal compositions in catalytic HEMOFs. Density functional theory (DFT) was first used to probe a key catalytic intermediate for CO<sub>2</sub> epoxidation in a series of compositionally related high-entropy polynuclear clusters. A direct correlation between composition and predicted catalytic activity trends was established, capitalizing on clear differences in a new valence edge peak in the band gap as function of active metal site. Following that, a series of HEMOFs with DFT-predetermined compositions were successfully synthesized. Remarkably, catalytic tests demonstrated the trends predicted by DFT, emphasizing the direct correlation between electronic structure and activity. The DFT-predicted optimal HEMOF composition was validated experimentally and shown to exhibit over 40% greater activity than the least active variant. The strategy described herein demonstrates the immense promise of harnessing cocktail effects in HEMOFs, wherein synergistic intermetallic effects impart superior catalytic activity. Finally, this work serves as a first step toward enabling machine learning-driven approaches to optimize catalytic performance through tailored metal compositions. Catalysis ; Valence (chemistry) ; Density functional theory ; Intermetallic ; Metal ; Work (physics) ; Rational design ; Function (biology) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Pei Zhou | Yuhang Li | Junjie Wang | Qing Lu | Yu Han | C. Y. Ding | Yang Ni | Xiaomeng Wang | Jian Sun
Source: Chinese Physics B (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Coexistence of Superconducting and Superionic States in Lithium Boron Compounds Under High Pressure Abstract Lithium-based compounds with interstitial anionic electrons (IAEs) exhibit unique electronic properties, including superconductivity, and superionic behavior. The intrinsic connection between these properties offers valuable insights and potential applications in the materials science. In this study, we employed machine learning accelerated crystal structure prediction and first-principles calculations to investigate the phase stability of various Li−B systems under high pressures. Our results indicate that the known R-3m Li 6 B compound is an electride. At 150 GPa, R-3m Li6B exhibits a superconducting transition temperature of around 51 K and enters a superionic state at high temperature. Additionally, a monoclinic compound, C2/m LiB 6 , which is metastable at ambient pressure was found. More interestingly, an unpredicted caged-like metallic boron allotropes termed C2/m -B 12 can be obtained by removing Li from LiB 6 . These findings open avenues for interdisciplinary research and highlight the potential of exotic boron allotropes in advanced device applications. Metastability ; Boron ; Monoclinic crystal system ; Superconductivity ; Lithium (medication) ; High pressure ; Phase (matter) ; Ambient pressure Boron and Carbon Nanomaterials Research ; Superconductivity in MgB2 and Alloys ; Rare-earth and actinide compounds