jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fatemeh Keshavarz | Stefanie Kröll | Jakob Gamper | Josef M. Gallmetzer | Thomas S. Hofer | Nima Rezaei | Bernardo Barbiellini
Source: Chemistry of Materials (article, 2026-02-17)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Framework Flexibility Boosting Atmospheric Water Harvesting by a Zinc-Based Glycine–Glyoxime–Histidine Framework Structural flexibility of metal–organic frameworks (MOFs) is both a challenge and an opportunity, depending on the MOF’s properties and the proposed applications. Here, we focus on ZnGGH, a peptide-based flexible MOF that switches from its “gate-closed” ZnGGH-9 conformer to “gate-open” ZnGGH-4 upon water adsorption. We use periodic density functional theory (DFT), semiempirical self-consistent charge density functional tight binding third order (DFTB3) and neural network-based MACE-MP simulations to understand the ZnGGH’s behavior and assess its potential for atmospheric water harvesting (AWH). Our results show that ZnGGH selectively adsorbs water over CO2, O2, and N2. As water loading increases, water-linker interactions trigger the gate opening and affect temperature-dependent water diffusion within the pores. Based on our analyses and comparison with benchmark AWH MOFs (MOF-303, MOF-333 and MOF-808), the gate opening facilitates AWH by weakening of the water interactions during water adsorption/desorption cycles while maintaining water selectivity. The MOF is thermally and hydrolytically stable under AWH conditions. To enable grid-scale applications, future work should focus on reducing synthesis costs to improve its economic viability. Flexibility (engineering) ; Benchmark (surveying) ; Boosting (machine learning) ; Density functional theory ; Work (physics) ; Focus (optics) Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Self-Assembly in Materials ; Carbon Dioxide Capture Technologies
Authors: Georgios Kamaris | Kyriakos Kachrimanis | Catherine K. Markopoulou
Source: Journal of Chromatography Open (article, 2026-03-14)
Field: Chemistry — Analytical Chemistry

Experimental multivariate modeling of structure-fluorescence relationships in organic compounds under chromatographic conditions: A proof-of-concept study • Computational fluorescence study using 117 observations and 74 descriptors • PLS and ANN multivariate modeling for the elucidation of fluorescence • In silico models hierarchically classified key fluorescence factors. Although fluorescence provides valuable analytical information, its theoretical interpretation remains challenging. In silico methodologies offer a systematic framework to explore multifactorial mechanisms and enhance fluorometric predictions. The present study investigates theoretical aspects of fluorimetry by interpreting the behavior of 117 substances analyzed by RP‑HPLC‑FLD (Reverse Phase High Pressure Liquid Chromatography with Fluorescence Detection), in methanol both as diluent and mobile phase, using Partial Least Squares (PLS) and Artificial Neural Network (ANN) models. The dataset used for model development consisted of the logarithmically transformed fluorescence responses of 117 observations (Y variable), while 36 physicochemical properties and 38 structural descriptors were included as predictor variables (X). PLS analysis objectively quantified the relative importance of the factors governing fluorescence, revealing that dominant contributors were primarily structural features such as fused or aromatic ring systems (+), conjugated single-double bonds (+), carbonyl groups (-), carboxylate groups (-), hydroxyl groups (+), amino groups (+), and sulfur-containing moieties (-). Several physicochemical properties also played a significant role, including lipophilicity (+), polar surface area (-), molecular flatness (+), hydrogen‑bond donor/acceptor capacity (-), and molecular flexibility (-). Accordingly, the application of ANN enabled the prediction of fluorescence intensity for unknown molecules with high reliability (r > 0.85). It was also noteworthy that the signal intensity of an analyte was influenced by the mobile phase flow rate as a function of the slope of its calibration curve. Overall, this multivariate approach provides, for the first time, a clear and objective prioritization of the dominant descriptors shaping fluorometric behavior. Multivariate statistics ; Analyte ; Chemometrics ; Identification (biology) ; Calibration Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Ming Dong | Yansong Li | Subash Rai | Han Zhang | Emiliano Bilotti | Dimitrios G. Papageorgiou
Source: Chemical Engineering Journal (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Machine-learning-enabled starch/MXene moisture sensing bioelectronics for human monitoring and interaction The drive toward sustainable sensors demands materials and manufacturing routes that deliver high sensing performance without generating persistent waste. We report a water-based, blade-coating process to fabricate starch/Ti 3 C 2 Tₓ MXene nanocomposite films that operate as multifunctional, moisture-responsive bioelectronics. The films form a layered conductive architecture with a Young's modulus of 6.4 GPa and tensile strength of 82.3 MPa at only 1.36 vol% MXene, exceeding the mechanical performance of previously reported biodegradable conductive films. The composite films display a low percolation threshold of 0.48 vol% and offer tuneable conductivity over 9.6 × 10 −4 to 0.48 S/m. In combination with exceptional humidity sensitivity (>700% ΔR/R 0 at 95% RH), these features enable high-performance multimodal moisture sensing, including respiration analysis, speech detection, skin hydration monitoring, and non-contact human–machine interaction (HMI). When integrated with machine-learning classification, a single transient sensor can accurately distinguish breathing patterns and recognise spoken words with 97% accuracy. In addition, the films exhibit moisture-driven actuation, excellent biocompatibility (cell viability >97%), and fully biodegrade in soil within 30 days. By combining scalable processing, intelligent sensing and transient functionality, this work positions biodegradable bioelectronics as a credible alternative to conventional wearable sensor technologies. • We demonstrate, for the first time, that humidity-induced resistance signals from natural-biopolymer based, transient films can be processed using machine learning to accurately classify breathing modes and spoken words, enabling AI-assisted physiological monitoring and HMI. • The biobased nanocomposite films exhibit exceptional humidity sensitivity (>700% ΔR/R₀ at 95% RH), enabling precise detection of moisture variations for respiration monitoring, speech recognition, skin-hydration assessment, and contactless control of a smart-car. • In addition, the films combine a low percolation threshold (0.48 vol%), tuneable conductivity, and high mechanical strength (82.3 MPa), ensuring stable electrical performance and mechanical integrity during repeated operation. • The devices fully biodegrade in soil within a month, addressing the rapidly growing challenge of electronic waste from disposable sensors and short-lifetime wearables. Bioelectronics ; Moisture ; In situ ; Human health MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Nanomaterials and Printing Technologies
Authors: Imam Hossain Shibly | Md.Asif Mahmud | Zahid Kaisar | Md Selim Hossain | Anirban Sarkar | Sadia Sultana | Nazrul Islam
Source: Optik (article, 2026-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-core D-shaped plasmonic sensor for kerosene adulteration detection using machine learning approach Kerosene ; Artificial neural network ; Sensitivity (control systems) ; Classifier (UML) ; Plasmon ; Ranging ; Photonics ; Pattern recognition (psychology) ; Wavelength Gold and Silver Nanoparticles Synthesis and Applications ; Dye analysis and toxicity ; Plasmonic and Surface Plasmon Research
Authors: José Cifuentes | Cristopher Oñate | Matías Espinoza | Hugo O. Garcés | Nikolaos Salaris | Rosario Castillo | Fernando  Antonio Castillo
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Organic Chemistry

Explainable wood origin discrimination based on flame emission spectroscopy dynamics and a Transformer model Overfitting ; Traceability ; Combustion ; Random forest ; Transformer ; Pattern recognition (psychology) ; Support vector machine ; Tracing Wood and Agarwood Research ; Forensic Fingerprint Detection Methods ; Isotope Analysis in Ecology
Authors: Rohit Goswami
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Reproducible Orchestration of Best Practices for Reaction Path Optimization with the Nudged Elastic Band The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention. Workflow ; Path (computing) ; Initialization ; Energy (signal processing) ; Orchestration ; Pipeline (software) ; Point (geometry) ; Saddle point ; Software ; Energy minimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Niamh O'Neill | Benjamin X. Shi | William C. Witt | Blake Armstrong | William J. Baldwin | Paolo Raiteri | Christoph Schran | Angelos Michaelides | Julian D. Gale
Source: Open MIND (preprint, 2026-03-06)
Field: Materials Science — Materials Chemistry

From Accurate Quantum Chemistry to Converged Thermodynamics for Ion Pairing in Solution Quantitative prediction of thermodynamic properties in solution is essential for translating atomistic simulations into reliable chemical insight. As an exemplar system, the behaviour of CaCO$_3$ in water has been widely studied to understand its mineralization in seawater, with potential implications for carbon-capture strategies. However, making accurate computational predictions has been a long-standing challenge, requiring both highly accurate electronic structure methods and extensive statistical sampling. Here, we combine advances in machine learning and electronic structure theory to fully resolve the ion pairing free energy of CaCO$_3$ with explicit solvation. We show that achieving quantitative agreement with experiment requires going beyond the standard density functional theory up to the "gold-standard" coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We generate a set of systematically improvable models, enabling reliable insights into the initial association mechanism of Ca and CO$_3$ ions prior to nucleation while fully quantifying enthalpic and entropic effects. Our results demonstrate that CCSD(T)-level thermodynamic predictions of complex aqueous systems can now be routinely achieved. Density functional theory ; Ion ; Pairing ; Quantum chemistry ; Electronic structure ; Nucleation ; Molecular dynamics ; Coupled cluster ; Cluster (spacecraft) Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Spectroscopy and Quantum Chemical Studies
Authors: Djamel Ghernaout Ghernaout | Messaouda Boumaaza | Ahmed Belaadi | Noureddine Elboughdiri | Mhamed Benaissa | Ibrahim M. H. Alshaikh
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Determining Water Absorption in Hybrid Biocomposites Made from <i>Syagrus romanzoffiana</i> Palm Waste Biomass and Fibers: Optimizing with ANN and RSM Methods The water absorption (WA) behavior of hybrid biocomposites reinforced with <i>Syagrus romanzoffiana</i> palm fibers (<i>Sr</i>PFs) and <i>Syagrus romanzoffiana</i> palm waste (biochar, <i>Sr</i>PW) shows a nonlinear relationship with immersion time and biochar content. This study explores the use of response surface methodology (RSM) and artificial neural networks (ANN) for multi-objective optimization and predictive modeling of this behavior. A hybrid model combining ANN with a genetic algorithm (GA) and RSM was developed to predict WA over immersion periods from 24 to 720 hours and <i>Sr</i>PW contents from 0.5% to 2%. The model was further enhanced using a Multi-Criteria Decision-Making (MCDM) approach with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The ANN-GA model predicted WA with reasonable accuracy (2.72% at 672 hours), closely matching experimental results, as confirmed by validation tests. At 615 hours, RSM optimization resulted in a slightly lower WA (2.68%). These findings demonstrate the reliability of the proposed modeling framework in reducing experimental work and guiding material design. The biocomposite exhibits potential for eco-friendly applications, particularly in the automotive sector. Response surface methodology ; Biocomposite ; Absorption of water ; Artificial neural network ; Palm ; Multi-objective optimization ; Biomass (ecology) ; Design of experiments Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Hygrothermal properties of building materials
Authors: Rui Yu | Junwei Sun | Dominik Legut | Ruifeng Zhang
Source: The Journal of Physical Chemistry C (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Rational Design of Cluster-Modified MXene for Electrocatalytic High-Efficiency Hydrogen Evolution Reaction To enable efficient and cost-effective hydrogen production from water splitting, transition-metal clusters (TMCs) loaded on various functionalized Ti2CT2 MXenes (TMCs/Ti2CT2) are systematically investigated as potential electrocatalysts for the hydrogen evolution reaction (HER) using first-principles calculations. The results indicate that 24 optimal combinations of functional groups and TMCs (e.g., Te–Co, Te–Rh, and Se–Ir) are screened from 223 candidates, which exhibit favorable stability and even lower |ΔGH| than that of the noble-metal Pt benchmark (ΔGH = −0.09 eV). Crucially, the enhanced activity originates from the electronegativity match between functional groups and TMCs, which induces uniform valence states across the cluster sites and thereby optimizes the hydrogen binding strength. Furthermore, a machine-learning framework with high predictive accuracy (R2 = 0.89) is established to enable the rapid screening of TMCs/Ti2CT2 and reveal the correlation between their structural properties and HER catalytic activity. These findings not only provide promising HER electrocatalyst candidates but also elucidate the electronic origins of catalytic activity, offering a rational design strategy for TMCs/Ti2CT2. Rational design ; Electrocatalyst ; Catalysis ; Electronegativity ; MXenes ; Density functional theory ; Valence (chemistry) ; Hydrogen MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Shumin Yan | Ruiling Gao | Shunbo Hu | Runhai Ouyang | Yin Wang
Source: Computational Materials Science (article, 2026-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The role of electron-phonon interactions on the electrical transport properties of metal interconnects Conductance ; Coupling (piping) ; Metal ; Electrical resistivity and conductivity ; Scattering ; Work (physics) ; Interconnection ; Vacancy defect ; Electrical resistance and conductance Copper Interconnects and Reliability ; Semiconductor materials and interfaces ; Electronic Packaging and Soldering Technologies
Authors: Jiacao Wu | Yi Kuang | Shengxiang Yang | Xu Han | Yiyu Qi
Source: Separation and Purification Technology (article, 2026-02-11)
Field: Chemistry — Analytical Chemistry

Sulfonamides extraction from environmental water using natural hydrophobic deep eutectic solvent: COSMO-RS calculations and ANN-GA optimization Extraction (chemistry) ; Deep eutectic solvent ; Eutectic system ; Matrix (chemical analysis) ; Surface water ; Environmental analysis ; Water extraction Analytical chemistry methods development ; Ionic liquids properties and applications ; Water Treatment and Disinfection
Authors: Ali Attariyan | Kerem Peker | Kivanc Taskin | Beyza Taskin
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Experimental and analytical investigation of the joining technique of NiTi SMA in a reusable seismic device for coupling beams Nickel titanium ; Shape-memory alloy ; Welding ; SMA* ; Pseudoelasticity ; Brittleness ; Coupling (piping) ; Slip (aerodynamics) ; Martensite Shape Memory Alloy Transformations ; Seismic Performance and Analysis ; Mechanical stress and fatigue analysis
Authors: Nikhil Chaurasia | S. Sangal | Shikhar Krishn Jha
Source: Materialia (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Noise removal and grain-boundary reconstruction in single-phase polycrystalline microstructures using deep learning Crystallite ; Deep learning ; Noise (video) ; Microstructure ; Context (archaeology) Machine Learning in Materials Science ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Xurui Li | Zhiguo Gan | Jiaming Zhang | Hongxi Zeng | Zheng Liu | Diannan Lu
Source: ChemRxiv (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Prediction of Molecular Critical Properties Based on Boruta Feature Selection: Balancing Accuracy, Applicability and Explainability The critical point as the endpoint of vapor-liquid equilibrium for a pure substance and play a pivotal role in the calculation of thermophysical properties. For Quantitative Structure-Property Relationship modeling, we used the Boruta algorithm to extract the optimal feature subset from 2705 initial features and applied regression algorithms including Gaussian Processes Regression, Random Forest and Support Vector Regression. Comparisons with GNN and other models demonstrate the good applicability of the current model. All models were tested on experimental data of 148 new molecules. The AARD for the predictions of Tc and Pc were below 2.5% and 5.7%, respectively, surpassing traditional group contribution methods and recent machine learning models. Furthermore, SHAP was employed to conduct an interpretability analysis of feature importance, and the top k features were selected for modeling. The results indicate that the features screened by Boruta yield superior modeling performance. This validates the compatibility of the Boruta feature selection algorithm with SHAP-derived feature importance. Finally, the UMAP-based visualization analysis indicates that the selected feature set can effectively distinguish high and low Critical Points, validating the effectiveness of the Boruta algorithm in feature selection. Interpretability ; Feature selection ; Feature (linguistics) ; Pattern recognition (psychology) ; Support vector machine ; Set (abstract data type) ; Feature extraction ; Random forest Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Lewis Conway | Christopher Pickard
Source: Apollo (University of Cambridge) (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Accelerating Crystal Structure Prediction Using Data‐Derived Potentials: High‐Pressure Binary Hydrides ABSTRACT Crystal structures can be predicted from first‐principles using ab initio random structure searching ( AIRSS ) to sample the potential energy landscape, and density functional theory (DFT) to robustly and accurately describe it. While classical interatomic potentials lower computational costs at the expense of robustness and accuracy, modern machine‐learning potentials offer a compromise, providing both at a fraction of the DFT cost. In this work, we use Ephemeral Data‐Derived Potentials ( EDDP s) to accelerate AIRSS calculations for binary hydrides at 100 GPa. Since the training data is generated iteratively using AIRSS , the searches require no prior knowledge of hydrides. These potentials allow more diverse searches, sampling diverse compositions, larger unit cells, and many more structures. Besides recovering known structures, the searches reveal hydrogen‐rich phases of H 22 (BrH), H 23 Pb, and H 32 Mg, supermolecular phases of H 25 Cs and H 26 Rn, and many ‘substoichiometric’ variants of known hydrides. Our results indicate that using the current generation of universal MLIPs to search for novel high‐pressure hydrides is less effective due to model instabilities or markedly slower inference speeds and highlight the necessity of generating new, targeted data. Binary number ; Robustness (evolution) ; Crystal structure prediction ; Density functional theory ; Ab initio ; Inference ; Crystal structure ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Hydrogen Storage and Materials ; High-pressure geophysics and materials
Authors: Mingliang Li | Chuchuan Peng | Rui Long | Zhichun Liu | Wei Liu
Source: International Journal of Hydrogen Energy (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Deep learning-assisted design of porosity distribution for enhanced adsorption hydrogen storage Porosity ; Adsorption ; Hydrogen ; Hydride ; Mass transfer ; Hydrogen storage ; Heat transfer ; Permeability (electromagnetism) Hydrogen Storage and Materials ; Adsorption and Cooling Systems ; Catalysts for Methane Reforming
Authors: Xiongwei He | Fan-Shun Meng | Yanjing Su | Lijie Qiao | Shigenobu Ogata | Lei Gao
Source: Computational Materials Science (article, 2026-02-16)
Field: Materials Science — Metals and Alloys

Unravelling the interplay between hydrogen and grain boundary of α-Fe under different concentration and strain rate via neural network interatomic potential Grain boundary ; Nucleation ; Dislocation ; Plasticity ; Hydrogen ; Interatomic potential ; Molecular dynamics ; Intergranular corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Cheng Cheng | Zhiwei Chen | Junhua Li | Aiwu Wang | Muhammad Humayun
Source: Food Chemistry (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Multicolor colorimetric tannic acid detection enabled by coordination-mediated oxygen vacancies modulation in MoOx nanosheets Tannic acid ; Oxygen ; Modulation (music) ; Colorimetry ; Selectivity Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors ; Carbon and Quantum Dots Applications