jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiawei Tang | Weiwei Sun | Chaofan Chen | Lars J. Bannenberg | Xuehang Wang | Tingwei Zhu | Litao Sun | jinlan wang | Guobing Ying | Yu Xie | Naresh C. Osti | A. I. Kolesnikov | Eugene Mamontov | Madhusudan Tyagi | Jingsong Huang | Paul R. C. Kent
Source: Science Advances (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Altered morphology and diffusivity of water confined in MXenes: Machine learning–accelerated computations combined with experiments Nanoconfined water exhibits unique properties compared to bulk water due to limited quantities, frustrated hydrogen bonding, and surface interactions, which are fundamental for energy storage and transport applications. We integrate machine learning-accelerated ab initio molecular dynamics with x-ray diffraction (XRD) and inelastic neutron scattering (INS) to systematically analyze the thermodynamic and dynamic behavior of water confined between functionalized (-F, -O, and -OH) two-dimensional (2D) Ti<sub>3</sub>C<sub>2</sub>T<i><sub>x</sub></i> MXene layers. As water intercalates between layers, the interlayer spacing exhibits layer-dependent staging characteristics. The water polarization can be flipped by the count and morphology of intercalated molecules interacting with MXene surface groups, resulting in varying electrostatic potential profiles. On the basis of interfacial electrostatic potential, hydrogen bond lifetime, and molecular orientation, we establish a linear combination of exponential model describing water diffusivity. These computational insights align well with experimental x-ray and neutron measurements, suggesting strategies for tuning water morphology and transport by tailoring MXene surface chemistry and water content for electrochemical energy storage and nanofluidic applications. Neutron diffraction ; Thermal diffusivity ; Hydrogen bond ; Molecule ; Molecular dynamics ; Neutron scattering ; Polarization (electrochemistry) ; Intercalation (chemistry) ; Hydrogen MXene and MAX Phase Materials ; Nanopore and Nanochannel Transport Studies ; 2D Materials and Applications
Authors: Xincheng Miao | Roland Mitrić
Source: arXiv (Cornell University) (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

A Hardware-Native Realisation of Semi-Empirical Electronic Structure Theory on Field-Programmable Gate Arrays High-throughput quantum-chemical calculations underpin modern molecular modelling, materials discovery, and machine-learning workflows, yet even semi-empirical methods become restrictive when many molecules must be evaluated. Here we report the first hardware-native realisation of semi-empirical electronic structure theory on a field-programmable gate array (FPGA), implementing as a proof of principle Extended Hückel Theory (EHT) and non-self-consistent Density Functional Tight Binding (DFTB0). Our design performs Hamiltonian construction and diagonalisation on the FPGA device through a streaming dataflow, enabling deterministic execution without host intervention. On a mid-range Artix-7 FPGA, the DFTB0 Hamiltonian generator delivers a throughput over fourfold higher than that of a contemporary server-class CPU. Improvements in eigensolver design, memory capacity, and extensions to nuclear gradients and excited states could further expand capability. Combined with the inherent energy efficiency of FPGA dataflow, this work opens a pathway towards sustainable, hardware-native acceleration of electronic-structure simulation and direct hardware implementations of a broad class of methods. Realisation ; Field-programmable gate array ; Hamiltonian (control theory) ; Class (philosophy) ; Topology (electrical circuits) ; Generator (circuit theory) ; Electronics ; Density functional theory Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum-Dot Cellular Automata
Authors: Chengchi Cao | Qi An
Source: Materials Horizons (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Photoplasticity behavior in inorganic semiconductors: unraveling fundamental mechanisms across ionic and covalent systems Inorganic semiconductors exhibit photoplasticity, where light exposure alters dislocation-mediated plastic flow based on the material's bonding character and carrier-defect interactions. In ionic II-VI compounds (<i>e.g.</i> ZnS and ZnO), above-band-gap illumination generates electron-hole pairs that are readily trapped at dislocation cores. This increases the Peierls stress (the effective barrier to glide), causing photoplastic hardening or a positive photoplastic effect. In contrast, covalent semiconductors (<i>e.g.</i> GaP, GaAs, Ge, and Si) demonstrate softening under illumination (negative photoplasticity) since photoexcited carriers often facilitate dislocation glide and reduce flow stress. This review summarizes recent experimental and theoretical progress on photoplasticity in inorganic semiconductors and integrates these results into a unified microscopic framework. Here, we discuss how modern techniques, density functional theory (DFT), constrained DFT, machine learning interatomic potentials, and large-scale molecular dynamics (MD) directly connect electronic excitation to changes in generalized stacking-fault energy surfaces, dislocation core reconstruction, and mobilities. On the experimental side, we review <i>in situ</i> mechanical tests under controlled illumination-from bulk compression to photo-nanoindentation and transmission electron microscopy-that directly show how light modulates dislocation activity. By systematically comparing ionic II-VI and covalent III-V/group-IV systems, we identify the key mechanisms that control the sign and magnitude of photoplasticity and outline design principles for semiconductors whose mechanical properties can be actively tuned by light illumination. Ionic bonding ; Semiconductor ; Covalent bond ; Dislocation ; Density functional theory ; Softening ; Ionic strength Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Shu-hui Guan | Y. Wei | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Physical Chemistry C (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Scandia as the Oxygen Vacancy Stabilizer to Boost the Ionic Conductivity of Sc–Y-Codoped Zirconia Scandia (Sc) and yttria (Y) codoped zirconia (ScYSZ) emerged as a promising candidate for high-performance solid electrolyte materials utilized in intermediate-temperature solid oxide fuel cells (IT-SOFCs). While it exhibits a record-high ionic conductivity (∼0.10 S/cm at 800 °C), the physical origin of the superior performance remains poorly understood, limiting the further optimization and the application in IT-SOFC. Here, we construct the Sc–Y–Zr–O global neural network potential and explore systematically the thermodynamic landscape of ScYSZ across 65 different compositions (6.7–14.3 mol % dopants). From millions of candidate structures, we identify a thermodynamically stable cubic phase region at Sc/Y < 1 with Y2O3 ≥ 8 mol %. Large-scale molecular dynamics simulations further show that ScYSZ at Sc2O3 = 3 mol % and Y2O3 = 8 mol % yields an exceptional ionic conductivity of 0.13 S/cm at 800 °C, surpassing conventional 8 mol % Y-stabilized zirconia (YSZ) by an order of magnitude. Our analysis reveals that the presence of Sc not only increases the Ov concentration by allowing ⟨111⟩ Ov–Ov pairs but also reduces the oxygen migration barriers markedly. Our results not only pinpoint the optimal ScYSZ composition for IT-SOFC applications theoretically but also establish a general framework for the rational design of advanced solid electrolyte materials. Ionic conductivity ; Yttria-stabilized zirconia ; Cubic zirconia ; Electrolyte ; Conductivity ; Oxide ; Oxygen ; Limiting Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: R. Patrick Xian | Ryan J. Morelock | Ido Hadar | Charles B. Musgrave | Christopher Sutton
Source: Chemistry of Materials (preprint, 2026-04-13)
Field: Materials Science — Materials Chemistry

From Structure Mining to Unsupervised Exploration of Atomic Octahedral Networks Understanding the spatial arrangements of atom-centered coordination octahedra is crucial for relating structures to properties for many materials families. Traditional case-by-case inspection becomes a prohibitive task for discovering trends and similarities in large data sets. Here, we operationalize chemical intuition to automate the geometric parsing, quantification, and classification of coordination octahedral networks using unsupervised machine learning. We apply the workflow to analyze two data sets to demonstrate its effectiveness. For computationally generated single oxide perovskite (ABO3) polymorphs, we uncover axis-dependent tilting trends that assist in detecting oxidation state changes. For hybrid iodoplumbates (AxPbyIz) from measured structures, we taxonomize their octahedral networks, revealing a Pauling-like connectivity rule for the coordination environment and the design principles underpinning their structural diversity. Our results offer a glimpse into the vast design space of atomic octahedral networks in materials chemistry and inform high-throughput, targeted screening of specific structure types. Octahedron ; Intuition ; Computer science ; Parsing ; Artificial intelligence ; Crystal structure ; Crystallography ; Chemistry Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Nikola Vuković | Aleksandar Milićević | Mihailo Stojković | Aleksandar Demić | Dušan Topalović | Novak Stanojević | Milan Ignjatović | Aleksandar Atić | Dragan Indjin | J. Radovanović
Source: VinaR (Institute of Nuclear Sciences "Vinča") (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Determination of transport characteristics of high-temperature THz quantum cascade lasers: numerical simulations and machine learning We present our recent numerical study, in which the transport simulations are performed using the density matrix model presented in [1]. These results served as a basis for applying different machine learning models to determine the material gain, emission frequency, and current density of a two-well THz quantum cascade laser (QCL) designed for hightemperature operation [2]. In particular, we examine Random Forest (RF) [3], Extreme Gradient Boosting (XGBoost) [4], and Artificial Neural Network (ANN) [5]. RF reduces the variance through ensemble averaging, XGBoost improves predictive performance through gradient-boosted regularization, while ANN architectures employ neural representation learning to capture complex nonlinear relationships. The models were trained by varying the layer widths and external electric bias as the input variables, enabling the prediction of the desired outputs. Combining numerical simulations with machine learning models enables rapid and accurate prediction of key device characteristics, bridging detailed physics-based modelling with efficient data-driven approaches. Cascade ; Artificial neural network ; Boosting (machine learning) ; Ensemble learning ; Ensemble forecasting ; Random forest ; Nonlinear system ; Gradient boosting Spectroscopy and Laser Applications ; Heart Rate Variability and Autonomic Control ; Semiconductor Lasers and Optical Devices
Authors: L. Zhao | Shiyin Ma | Z.T. Fan | Qunchao Fan | Chenglong Qin
Source: Inorganic Chemistry (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Accelerating the Design of High-Entropy Alloys for Hydrogen Storage via Generative Adversarial Networks High-entropy alloys (HEAs) have emerged as promising candidates for solid-state hydrogen storage, yet the vast compositional space poses a major challenge for targeted materials discovery. Here, we develop a deep generative framework based on generative adversarial networks (GANs) and conditional GANs (CGANs) to enable the inverse design of HEAs with optimized hydride formation enthalpy and hydrogen storage capacity. The GAN accurately captures the underlying compositional distributions, achieving high novelty and diversity while reproducing known chemical trends and empirical phase-formation rules. By incorporating property labels as conditional inputs, the CGAN allows directional control over the generation process, steering the model toward alloys with targeted enthalpy and capacity ranges. Density functional theory (DFT) calculations are performed to validate a representative GAN-generated composition, Mn<sub>18</sub>V<sub>2</sub>Fe<sub>16</sub>Ti<sub>18</sub>, confirming its structural stability and hydrogen uptake, in good agreement with machine-learning predictions. These results demonstrate that GAN model can autonomously learn distribution characteristics of data set and efficiently explore unexplored regions of the HEAs design space, providing a powerful strategy for the inverse design of advanced hydrogen storage materials. Generative grammar ; Hydrogen storage ; Stability (learning theory) ; Inverse ; Chemical space ; Novelty ; Hydrogen ; Generative Design Machine Learning in Materials Science ; High Entropy Alloys Studies ; Hydrogen Storage and Materials
Authors: Z. H. Ming | Xuefeng Bai | Yu Li | Kexin Guan | Xinyu Zhang | Huisheng Yao | Min Zhang | Shouxin Zhang | Xiaoshan Yan | Heguo Li | Xiaopeng Li | Yufang Wu | Yue Zhao | Zhiwei Qiao
Source: Engineered Science (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Integrating Molecular Simulations, Interpretable Machine Learning and Metal-Organic Framework Identifier Analysis for the Discovery of Hydrophobic Metal-Organic Frameworks with High Adsorption Capacity for Sulfur Mustard Simulants Adsorption ; Sulfur ; Identifier ; Identification (biology) Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Covalent Organic Framework Applications
Authors: Gezhao Sang | Zhengyi Xu | Jianming Wei | Sheng Zhang | Jiawei Li
Source: The Journal of Physical Chemistry A (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Multi-Task Feedforward Neural Networks for Thermodynamic Property Prediction under Small Sample Sizes Reliable explosion modeling requires accurate thermodynamic properties of hazardous chemicals, yet the scarcity of experimental data highlights the urgent need for predictive approaches to bridge this critical gap. In recent years, machine learning-assisted quantitative structure-property relationship (ML-QSPR) approaches have been widely adopted for thermodynamic property prediction due to their high computational efficiency and strong interpretability. Nevertheless, current methods still suffer from limited multitask prediction accuracy, redundant features, and poor generalization under small-sample conditions and descriptor calculation constraints. In this work, we propose a thermodynamically constrained multitask learning network designed for small-sample thermodynamic property prediction. The model leverages multitask learning to exploit inherent correlations among thermodynamic properties, integrates ensemble learning strategies into the feature engineering process to alleviate overfitting, and incorporates physicochemical constraints into the loss function by constructing a physics-informed neural network (PINN) to enhance structure-property relationship modeling. Comparative experiments demonstrate that ThermoMTLnet outperforms traditional machine learning models and single-task neural networks across multiple metrics, including Pearson correlation coefficient (PCC) and mean absolute error (MAE). Furthermore, ThermoMTLnet achieved a 0.23% higher PCC and 2.44% lower MAE than the best baseline on 300-sample data sets, while maintaining consistent superiority on larger data sets. This demonstrates its robust generalization capability for thermodynamic property prediction with limited samples. Artificial neural network ; Property (philosophy) ; Generalization ; Feature (linguistics) ; Exploit ; Function (biology) ; Multi-task learning ; Correlation ; Process (computing) Machine Learning in Materials Science ; Advanced Combustion Engine Technologies ; Model Reduction and Neural Networks
Authors: Shubha S. Gunaga | Robert W. Schurko | Sean T Holmes | Frederic Mentink-Vigier
Source: ChemRxiv (article, 2026-03-17)
Field: Chemistry — Spectroscopy

Performance of machine learning tools for NMR crystallography Nuclear magnetic resonance (NMR) crystallography is a robust method for structure determination, but its reliance on density functional theory (DFT) for geometry refinement and tensor prediction limits its speed. While Machine Learning (ML) tools like ShiftML3 can predict magnetic shielding in seconds, they still require high-quality geometries typically produced by slow DFT optimizations. This study demonstrates that Machine Learning Interatomic Potentials (MLIPs) eliminate this bottleneck for organic crystals by generating accurate structures without DFT calculations. Using the Universal Model for Atoms (UMA), we produced structural models that rival hybrid-DFT accuracy. Magnetic shielding predictions derived from these models are statistically indistinguishable from DFT benchmarks. The UMA-ShiftML3 workflow therefore serves as a proper "drop-in" replacement for conventional DFT-based refinement, enabling high-throughput NMR crystallography at dramatically reduced computational cost. Furthermore, by combining NMR sensitivity enhancement via Dynamic Nuclear Polarization (DNP) with these ML-based methods, we used extracted chemical shift tensors for L-histidine without isotopic enrichment that aid rapid discrimination between two polymorphs. While 13C/15N tensors alone are similar for the two polymorphs, incorporating 1H chemical shift analysis enables discrimination between the monoclinic and orthorhombic forms. These results show how data-driven modeling and DNP can enable fast, accurate NMR crystallography at scale. Density functional theory ; Orthorhombic crystal system ; Bottleneck ; Monoclinic crystal system ; Tensor (intrinsic definition) ; Workflow ; Nuclear magnetic resonance spectroscopy Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Protein Structure and Dynamics
Authors: Zhongjie Meng | Chongyan Hao | Xiaoning Li
Source: Materials Today Energy (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Bridging machine learning and water electrolysis: Concepts, methods, and perspectives Machine learning (ML) is increasingly applied in water electrolysis research for analysing complex datasets and accelerating catalyst and device development. However, the lack of familiarity with ML concepts and practical workflows among catalysis researchers has limited its effective use. This review provides a concise and practical introduction to ML from the perspective of catalysis researchers. We first outline the basic working principles of ML, including data representation, learning and generalisation. Their working principles are demonstrated in a way accessible to readers without coding experience. Following this, we introduce commonly used software environments and accessible tools, ranging from Python-based workflows to no-code and low-code platforms. Building on this methodological foundation, we systematically review representative ML applications in water electrolysis, organised by key research tasks, including catalyst screening, data-driven analysis of electrochemical and operando measurements, and device-level prediction. Key challenges, such as data consistency, model reliability and physical interpretability, are discussed together with emerging opportunities. In contrast to existing reviews that primarily emphasise algorithmic advances or application case studies, this review seeks to serve as a bridge between ML methodologies and the questions and practices encountered in water electrolysis research. • Provides original schematic illustrations of the principles of key ML algorithms • Introduces ML concepts and workflows in a non-coding manner • Compares Python-based, low-code and no-code ML tools • Discusses catalyst screening, spectroscopic analysis, and device monitoring • Bridges conceptual understanding and practical ML workflows for chemists Workflow ; Bridging (networking) ; Schematic ; Bridge (graph theory) ; Key (lock) ; Software Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; CO2 Reduction Techniques and Catalysts
Authors: Jakub Grudzien Kuba | Benjamin Kurt Miller | Sergey Levine | Pieter Abbeel
Source: Open MIND (preprint, 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: Que Ngo | Anh D. Phan | T. Nga Do | Trần Văn Huỳnh
Source: Communications in Physics (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Machine learning–assisted ECNLE theory for predicting dynamical properties of FeCoCrMoCBTm metallic glass In this work, we present an integrated modeling framework that combines machine learning (ML) and the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to investigate the glass transition dynamics of metallic glass systems. We focus on the Fe42Co6Cr15Mo14CxB21-xTm2 alloy family. The glass transition temperatures Tg of this alloy family are predicted using machine learning models trained on experimental datasets. These ML-predicted-Tg values are then used as input to the ECNLE theory to compute the temperature dependence of structural relaxation times, dynamic fragility, and diffusion coefficients. Our predictions of Tg and dynamic fragility quantitatively agree with the experimental data. This shows that the combined ML-ECNLE approach provides a practical and scalable tool for characterizing the relaxation behavior and diffusion dynamics metallic glasses when experimental data remain limited. Fragility ; Amorphous metal ; Glass transition ; Relaxation (psychology) ; Diffusion ; Alloy ; Nonlinear system Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Thermodynamic and Structural Properties of Metals and Alloys