jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Lebeda | Jan Drahokoupil | Petr Vlčák | Šimon Svoboda | Axel van de Walle
Source: Journal of Computational Science (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

SimplySQS: An automated and reproducible workflow for special quasirandom structure generation with ATAT The special quasirandom structure (SQS) method is widely used for modeling disordered materials under periodic boundary conditions, with the ATAT mcsqs module being one of the most established implementations. However, SQS generation with mcsqs typically relies on manual preparation of input files, ad hoc execution scripts, and post-processing steps, which introduces user-dependent errors and limits reproducibility. Here, we present SimplySQS ( https://simplysqs.com ), an automated and reproducible workflow for SQS generation that is delivered through an online, interactive interface. SimplySQS guides users through structure import, compositional and supercell definition, and cluster parameter selection, while automatically generating all required ATAT input files and a single all-in-one execution script that encapsulates the complete search process. By standardizing input preparation, execution, and output analysis, the framework minimizes errors associated with manual file handling and enables consistent reproducibility of SQS searches. The workflow is demonstrated on the Pb 1- x Sr x TiO 3 (PSTO, including PbTiO 3 (PTO) and SrTiO 3 (STO)) perovskite system. SQSs spanning the entire concentration range were generated using a single automated bash script produced by SimplySQS , after which all resulting structures were subjected to geometry optimization using a universal machine-learning interatomic potential (MACE MATPES-r²SCAN-0). This approach reliably reproduced the experimentally observed cubic-to-tetragonal transition near x ≈ 0.5, with lattice parameters deviating by less than 1% in the cubic region ( x > 0.5) and less than 4% in the tetragonal region ( x ≤ 0.5). Overall, SimplySQS transforms SQS generation with ATAT into an intuitive, reproducible, and systematic framework for modeling disordered materials. Workflow ; Data structure ; Range (aeronautics) ; Lattice (music) ; Tetragonal crystal system ; Cluster (spacecraft) ; Set (abstract data type) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials
Authors: Mingxing Zhang | Nicola Rossberg | Simone Innocente | Katarzyna Komolibus | Rekha Gautam | Barry O'Sullivan | Luca Longo | Andrea Visentin
Source: arXiv (Cornell University) (preprint, 2026-03-19)
Field: Chemistry — Analytical Chemistry

SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data In recent years, machine learning models have been increasingly applied to spectroscopic datasets for chemical and biomedical analysis. For their successful adoption, particularly in clinical and safety-critical settings, professionals and researchers must be able to understand and trust the reasoning behind model predictions. However, the inherently high dimensionality and strong collinearity of spectroscopy data pose a fundamental challenge to model explainability. These properties not only complicate model training but also undermine the stability and consistency of explanations, leading to fluctuations in feature importance across repeated training runs. Feature extraction techniques have been used to reduce the input dimensionality; these new features hinder the connection between the prediction and the original signal. This study proposes SHAPCA, an explainable machine learning pipeline that combines Principal Component Analysis (for dimensionality reduction) and Shapely Additive exPlanations (for post hoc explanation) to provide explanations in the original input space, which a practitioner can interpret and link back to the biological components. The proposed framework enables analysis from both global and local perspectives, revealing the spectral bands that drive overall model behaviour as well as the instance-specific features that influence individual predictions. Numerical analysis demonstrated the interpretability of the results and greater consistency across different runs. Interpretability ; Consistency (knowledge bases) ; Curse of dimensionality ; Stability (learning theory) ; Collinearity ; Pipeline (software) ; Feature (linguistics) ; Principal component analysis ; Exploratory data analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science
Authors: Novianto Nur Hidayat | Usman Sudibyo | Achmad Wahid Kurniawan | Fariz Hasim Arvianto | Muhammad Naufal | Wahyu Aji Eko Prabowo | Harun Al Azies | Muhamad Akrom
Source: Engineering chemistry (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Effect of Feature Selection on Data-Driven Prediction of Catalyst Performance: A Case Study on Methanol Formation from Thermocatalytic CO&lt;sub&gt;2&lt;/sub&gt; Hydrogenation on Cu-Based Catalysts CO 2 conversion to methanol via thermocatalytic hydrogenation is one of the viable alternatives to address climate change problem while producing a valuable industrial product. However, this comes with a challenge, i.e., predicting the performance of catalytic systems. In this work, we present a data-driven study to predict the performance of Cu-based catalyst based on a compiled dataset consisting of 15 features obtained from experiment data. Furthermore, we implement feature selection techniques such as univariate, RFE, and XGBoost to investigate how the performance of the prediction model changes with varied number of features. The results show that features selected by RFE method yields the best performance with 7 number of features, capable of even outperforms the baseline model in terms of accuracy and feasibilty. This suggests that feature selection technique is relevant in terms of constructing a machine learning model for predicting methanol production via CO 2 thermocatalytic hydrogenation. Methanol ; Catalysis ; Feature selection ; Selection (genetic algorithm) ; Feature (linguistics) ; Production (economics) ; Support vector machine Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysts for Methane Reforming
Authors: Amirhossein F. Firooz | Christophe A. N. Biscio | Anders K. R. Christensen | Søren S. Sørensen | N. M. Anoop Krishnan | Morten M. Smedskjaer
Source: Physical Review Materials (article, 2026-03-02)
Field: Materials Science — Ceramics and Composites

Ring structure analysis in calcium aluminophosphate glasses While the short-range order structure and properties of phosphate-based glasses have been extensively studied, limited studies have focused on their medium-range order (MRO) structure, including the structural origin of the first sharp diffraction peak (FSDP) in the structure factor. In this work we perform molecular dynamics simulations, based on a machine learning interatomic potential, of calcium phosphate and calcium aluminophosphate glasses to investigate their MRO structures. We do so based on both classical ring analyses and persistent homology, clarifying the relation between different MRO features and the position of the FSDP. Furthermore, by classifying loops based on geometric descriptors (roundness and eccentricity), we show that the density distribution of loops with an eccentricity between 0.6 and 0.8 and those exhibiting high roundness are found at similar $Q$ values as the FSDP. Compositional variations, especially with increasing Ca/P ratio, systematically leads to larger loops, including more loops with high eccentricity. Overall, our work provides new insights into the geometry of MRO structures identified by persistent homology and classical ring analysis in phosphate glass networks. Ring (chemistry) ; Diffraction ; Molecular dynamics ; Threading (protein sequence) ; Work (physics) ; Calcium ; Position (finance) Glass properties and applications ; Material Dynamics and Properties ; Clay minerals and soil interactions
Authors: Moritz Schäfer | Matthias Kellner | Johannes Kästner | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

How to Train a Shallow Ensemble Shallow ensembles provide a convenient strategy for uncertainty quantification in machine learning interatomic potentials, that is computationally efficient because the different ensemble members share a large part of the model weights. In this work, we systematically investigate training strategies for shallow ensembles to balance calibration performance with computational cost. We first demonstrate that explicit optimization of a negative log-likelihood (NLL) loss improves calibration with respect to approaches based on ensembles of randomly initialized models, or on a last-layer Laplace approximation. However, models trained solely on energy objectives yield miscalibrated force estimates. We show that explicitly modeling force uncertainties via an NLL objective is essential for reliable calibration, though it typically incurs a significant computational overhead. To address this, we validate an efficient protocol: full-model fine-tuning of a shallow ensemble originally trained with a probabilistic energy loss, or one sampled from the Laplace posterior. This approach results in negligible reduction in calibration quality compared to training from scratch, while reducing training time by up to 96%. We evaluate this protocol across a diverse range of materials, including amorphous carbon, ionic liquids (BMIM), liquid water (H$_2$O), barium titanate (BaTiO$_3$), and a model tetrapeptide (Ac-Ala3-NHMe), establishing practical guidelines for reliable uncertainty quantification in atomistic machine learning. Calibration ; Range (aeronautics) ; Energy (signal processing) ; Training (meteorology) ; Probabilistic logic ; Protocol (science) ; Laplace transform ; Uncertainty quantification ; Ensemble forecasting Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Block Copolymer Self-Assembly
Authors: Ke Li (106849) | Xuesong Wang | Xixingchi Chen | Qing Han | Hui Huang | Yongxin Li
Source: Journal of Hazardous Materials (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Construction of a colorimetric sensor array based on two novel carboxylated nitrogen heterocycle-copper coordinated laccase-like nanozymes for identifying phenolic endocrine disruptors Catalysis ; Nitrogen ; Tap water ; Environmentally friendly ; Linear range ; Analyte Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Andre Bohn | Adie Alwen | Andrèa M. Hodge
Source: Advanced Engineering Materials (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Nanoindentation Criteria for Combinatorial Thin Film Libraries Employing high‐throughput characterization techniques on combinatorially synthesized thin film material libraries offers a pathway for accelerating the discovery and development of novel materials. In particular, nanoindentation is useful for rapidly screening composition‐property relationships. However, despite its widespread use, current literature reveals methodological inconsistencies regarding the number of indents performed per composition and the total number of compositions sampled. This work utilizes a Cu x Ni (1− x ) library as a model system to investigate nanoindentation data collection and optimize experimental throughput. Interpolation methods are applied to data subsets to identify critical regions of interest and capture material trends. The employed framework indicates that characterizing as few as 15% of the total compositions is sufficient for predicting both hardness and modulus trends across the Cu x Ni (1− x ) library. This approach can be used as a baseline for investigating more compositionally complex systems, enabling faster generation of datasets, materials discovery, and training sets for machine learning models. Nanoindentation ; Characterization (materials science) ; Thin film ; Work (physics) ; Interpolation (computer graphics) ; Elastic modulus Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Leng Ze Tang | Layla Purdy | Trupti Mohanty | Leonard W. T. Ng | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Systematically Verified Experimental Thermoelectric Dataset For Data-driven Approaches Thermoelectric effect ; Work (physics) ; Field (mathematics) ; Noise (video) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Tan Le Dinh
Source: JuSER Publikationsportal (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Guidance of Manufacturing Process of Solid Oxide Cells Solid Oxide Cells (SOCs) are promising energy conversion devices with applications in both electricity generation and chemical fuel production. Fuel Electrode-Supported Cells (FESCs) particularly offer a robust architecture, but their performance and reliability are heavily dependent on the manufacturing process of a porous substrate material. Traditional manufacturing parameter optimisation primarily relies on trialand- error experimental methods, which are resource-intensive, time-consuming, and difficult to scale for industrial production. This PhD dissertation addresses these challenges by combining physics-based and data-driven modelling to link manufacturing parameters, microstructure, and physical properties of the fuel-electrode substrate. These novel approaches are developed to investigate how the manufacturing parameters affect the substrate microstructure, offering the digital tools to optimise the fabrication process of SOCs. Specifically, a coarse-grained physics-based modelling framework was developed to simulate the slurry and dried microstructures of fuel-electrode substrates for the tape casting and drying stages of fuel cell substrate materials. The microstructures generated by this model provide digital representations of the coarse substrate material, which can be used as input for multi-scale simulation methods. Concurrently, the thesis focuses on applying Machine Learning (ML) techniques to optimise SOC manufacturing processes. By integrating advanced data collection through electronic laboratory notebooks and on-site characterisation, ML models have been trained to predict the substrate properties at each manufacturing stage. This data-driven approach helps to identify fundamental relationships between key manufacturing parameters and substrate properties, enabling the optimisation of the manufacturing routes for achieving target characteristics of the fuel-electrode substrate. These physics-based and data-driven strategies form a complementary framework, bridging experimental production and predictive simulation. The contribution of this research lies in providing novel modelling tools, integrating experimental, numerical and data-driven methods, and supporting the systematic, reproducible, and energyefficient scaling up of SOC production. Digital manufacturing ; Solid oxide fuel cell ; Process (computing) ; Substrate (aquarium) ; Bridging (networking) ; Computer-integrated manufacturing ; Reliability (semiconductor) ; Manufacturing execution system ; Fuel cells Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Big Data and Digital Economy
Authors: Bo Shan | Ruochu Lei | Yali Yan | Yihao Luan | Bowen Cheng | Yan Huang
Source: Journal of Material Science and Technology (article, 2026-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sustainable electromagnetic wave management: Structural engineering based on bio-derived graphene hybrid carbon Graphene ; Electronics ; Dissipation ; Carbon fibers ; Microwave ; Electromagnetic radiation ; Transformative learning ; Metamaterial Electromagnetic wave absorption materials ; Graphene research and applications ; Metamaterials and Metasurfaces Applications
Authors: Yoshiyasu Takefuji
Source: Computational Materials Science (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Beyond prediction: Assessing stability in feature selection methods for materials science applications Interpretability ; Feature selection ; Stability (learning theory) ; Feature (linguistics) ; Reliability (semiconductor) ; Ranking (information retrieval) ; Supervised learning ; Unsupervised learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metallurgical and Alloy Processes
Authors: Yuji Ikeda | Axel Forslund | Pravir Kumar | Yongliang Ou | Jong Hyun Jung | Andreas Köhn | Blazej Grabowski
Source: Journal of Chemical Theory and Computation (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Machine-Learning Interatomic Potentials Achieving CCSD(T) Accuracy for Systems with Extended Covalent Networks and van der Waals Interactions Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining <i>ab initio</i> accuracy. In recent years, MLIPs trained on coupled-cluster data─particularly CCSD(T), which includes single, double, and perturbative triple excitations─have emerged as a promising route to achieve chemical accuracy (1 kcal/mol) beyond the limits of density functional theory (DFT) and to incorporate nonempirical van der Waals (vdW) interactions. Most existing approaches are, however, still not straightforwardly applicable for systems with extended covalent networks such as covalent organic frameworks (COFs) due to the limited availability of CCSD(T) under periodic boundary conditions. Here we present a methodology to train MLIPs with CCSD(T) accuracy for systems with extended covalent networks. The approach is based on the Δ-learning method with a dispersion-corrected tight-binding baseline and an MLIP trained on the differences of the target CCSD(T) energies from the baseline. This Δ-learning strategy enables training on compact molecular fragments while preserving transferability toward the periodic systems. Dispersion interactions are accounted for by including vdW-bound multimers in the training set, and the combination with a vdW-aware tight-binding baseline allows the formally local MLIP to attain CCSD(T)-level accuracy even for systems dominated by long-range vdW forces. The resulting potential yields root-mean-square energy errors below 0.4 meV/atom on both training and test sets and reproduces electronic total atomization energies, bond lengths, harmonic vibrational frequencies, and intermolecular interaction energies for benchmark molecular systems. We apply the method to a prototypical quasi-two-dimensional covalent organic framework (COF) composed of carbon and hydrogen. The COF structure, interlayer binding energies, and hydrogen absorption are analyzed at CCSD(T) accuracy. Overall, the developed methodology opens a practical route to large-scale atomistic simulations for systems with extended covalent networks and vdW interactions with chemical accuracy. van der Waals force ; Covalent bond ; Periodic boundary conditions ; Benchmark (surveying) ; Density functional theory ; Intermolecular force ; Non-covalent interactions ; Transferability ; Hydrogen bond Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum many-body systems
Authors: Le Nhat Thanh | Duy Khanh Nguyen | Phạm Thị Bích Thảo | Quoc Duy Ho | Tuoc Ngoc Vu | Nguyen Thanh Tien | Vo Khuong Dien
Source: Journal of Applied Physics (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Tunable thermal transport in hole-doped monolayer penta-graphene: A first-principles and machine-learning study In conventional semiconductors, electron–phonon coupling (EPC) is weaker than phonon–phonon (ph–ph) scattering and plays little role in lattice thermal conductivity (κL). We show, using first-principles calculations combined with machine-learning force fields (MLFF), that EPC can instead dominate phonon transport in monolayer penta-graphene under carrier doping. The absence of mirror symmetry allows direct coupling of flexural (ZA) phonons to carriers, and tuning the Fermi level into the Van Hove singularity strongly enhances the electronic density of states and amplifies EPC. Consequently, κL is suppressed by nearly 64% (from 266 to ∼96 W m−1 K−1 at room temperature) and exhibits a weakened temperature dependence (from T−1.4 to T−1.0). This EPC-driven suppression of heat transport enhances the thermoelectric figure of merit by more than threefold. These findings open a pathway to engineer heat conduction in low-symmetry carbon allotropes via controlled doping. Phonon ; Monolayer ; Thermal conductivity ; Thermal conduction ; Van Hove singularity ; Fermi level ; Density of states ; Scattering ; Coupling (piping) Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Weifeng Wan | Junlong He | Xiuqi Xu | Dawei Huang | Xiaojun Yan
Source: Materials & Design (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Physics-informed LSTM framework for predicting anisotropic room-temperature creep in textured Zircaloy-4 • Physics-informed LSTM predicts anisotropic creep from mesoscale DIC data. • Creep anisotropy arises from texture-dependent slip activation in HCP Zircaloy-4. • Local-SRS and strain-gradient features enhance predictive accuracy. This study investigates the mesoscale creep behaviour of textured Zircaloy-4 at room temperature by integrating in situ digital image correlation (DIC), crystal plasticity finite element modelling, and a physics-informed recurrent neural-network framework. Pronounced creep anisotropy is observed, with specimens whose c-axes are aligned out of plane exhibiting larger creep strain accumulation than those oriented in plane. Resolved shear stress analysis reveals that this contrast originates from texture-dependent slip activity, where the Y-textured specimen activate both prismatic-〈a〉 and pyramidal-〈c + a〉 systems, while the Z-textured specimen primarily deforms through prismatic-〈a〉 slip. To capture these mechanisms, a dual-stage long short-term memory network is developed that embeds physics-informed descriptors, including local strain-rate sensitivity and strain-gradient features, into both texture classification and strain-field prediction. The creep strain-field problem is reformulated as a long-horizon forecasting task, in which early-stage DIC sequences are used to predict far-future strain evolution. Strain-gradient descriptors act as experimentally accessible proxies for evolving microstructural constraints, enabling physical insight to be incorporated into the learning process. The framework enhances interpretability and long-horizon predictive stability, enabling accurate reconstruction of spatiotemporal creep-strain evolution. This study demonstrates that coupling micro-mechanical experiments with physics-informed learning provides a transferable strategy for predicting texture-dependent creep deformation in structural alloys. Creep ; Anisotropy ; Slip (aerodynamics) ; Mesoscale meteorology ; Digital image correlation ; Interpretability ; Deformation mechanism ; Shear (geology) Machine Learning in Materials Science ; Nuclear Materials and Properties ; Block Copolymer Self-Assembly