jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zizheng Feng
Source: Journal of Physics Conference Series (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Data-driven discovery of silicon-based electrode materials Abstract With the advancement of energy transition and electric vehicles, the exploration of new electrode materials has become urgent. Herein, data-driven techniques centered on machine learning were used to develop models based on three predictive algorithms for the screening of silicon-based materials. Multidimensional features—including crystal structures, electronic properties, and thermodynamic parameters—of 890 silicon-containing materials from the Materials Project database were integrated. Then, performance prediction models were constructed using the Random Forest (RF), Gradient Boosting Regression Tree (GBRT), and Adaboost (AD) algorithms. The results showed that the RF model exhibited the best performance in predicting specific capacity (R 2 =0.75). SHAP interpretability analysis revealed a significant negative correlation between atomic density and capacity (R=-0.46), while there was a weak positive correlation with uncorrected energy per atom. Based on these findings, 10 lithium-silicon-transition metal oxide candidate materials with an atomic density below 8.53 were screened out, covering three structure types: Li 3 MSiO 5 , Li 7 Si 2 (NiO 4 ) 3 , and Li 9 Si 2 Ni 5 O 16 . This study provides new insights for the rational design of high-capacity silicon-based electrodes. Interpretability ; Random forest ; Boosting (machine learning) ; Gradient boosting ; Crystal structure prediction ; Decision tree ; Ensemble learning ; AdaBoost ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Chengchun Liu | Wendi Cai | Boxuan Zhao | Fanyang Mo
Source: ArXiv.org (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

A Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement Accurate molecular geometries are a prerequisite for reliable quantum-chemical predictions, yet density functional theory (DFT) optimization remains a major bottleneck for high-throughput molecular screening. Here we present GeoOpt-Net, a multi-branch SE(3)-equivariant geometry refinement network that predicts DFT-quality structures at the B3LYP/TZVP level of theory in a single forward pass starting from inexpensive initial conformers generated at a low-cost force-field level. GeoOpt-Net is trained using a two-stage strategy in which a broadly pretrained geometric representation is subsequently fine-tuned to approach B3LYP/TZVP-level accuracy, with theory- and basis-set-aware calibration enabled by a fidelity-aware feature modulation (FAFM) mechanism. Benchmarking against representative approaches spanning classical conformer generation (RDKit), semiempirical quantum methods (xTB), data-driven geometry refinement pipelines (Auto3D), and machine-learning interatomic potentials (UMA) on external drug-like molecules demonstrates that GeoOpt-Net achieves sub-milli-Å all-atom RMSD with near-zero B3LYP/TZVP single-point energy deviations, indicating DFT-ready geometries that closely reproduce both structural and energetic references. Beyond geometric metrics, GeoOpt-Net generates initial guesses intrinsically compatible with DFT convergence criteria, yielding nonzero ``All-YES'' convergence rates (65.0\% under loose and 33.4\% under default thresholds), and substantially reducing re-optimization steps and wall-clock time. GeoOpt-Net further exhibits smooth and predictable energy scaling with molecular complexity while preserving key electronic observables such as dipole moments. Collectively, these results establish GeoOpt-Net as a scalable, physically consistent geometry refinement framework that enables efficient acceleration of DFT-based quantum-chemical workflows. Energy minimization ; Topology (electrical circuits) ; Bottleneck ; Representation (politics) ; Feature (linguistics) ; Convergence (economics) ; Density functional theory ; Quantum ; Potential energy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yuta Otsuka | Tomohiro Masuzaki | Hiroyuki Takase | Toru Naito
Source: Bio-Medical Materials and Engineering (article, 2026-02-02)
Field: Materials Science — Biomaterials

Near-infrared spectroscopy and machine-learning evaluation of PVA/β-TCP biomaterial composites A composite of polyvinyl alcohol (PVA) and beta-tricalcium phosphate (β-TCP) was synthesized as a biomaterial filament for 3D printers and its analytical and chemical evaluation was performed. PVA powder and β-TCP were mixed in the range of 0-20 wt% and hot-melt extruded at 200 °C using a single-screw extruder. Comprehensive material characterization of the synthesized filament was performed by powder X-ray diffraction (XRD), near-infrared spectroscopy (NIR), and scanning electron microscopy (SEM). XRD analysis confirmed that the amorphous nature of PVA and the crystalline nature of β-TCP coexisted and the physical mixture state was well maintained. In near-infrared spectroscopy, concentration-dependent spectral changes were observed by normalization, and principal component analysis showed that the first principal component explained 85.6% of the variance. In machine learning regression analysis, partial least squares regression (PLS), random forest (RF), and support vector machine (SVM) were compared, and SVM achieved the best prediction accuracy (R<sup>2</sup> = 0.910). SEM observations confirmed streaky structures along the extrusion direction and uniform dispersion of β-TCP particles. This study demonstrated that a combined NIR spectroscopy and machine learning approach is effective as a non-destructive quality evaluation technique for composite filaments for 3D printing. This technique enables real-time composition monitoring and quality control of biomaterial filaments, and is expected to be applied to the manufacturing of patient-specific biomedical devices. Biomaterial ; Partial least squares regression ; Spectroscopy ; Extrusion ; Composite number ; Scanning electron microscope ; Polyvinyl alcohol ; Principal component analysis ; Dispersion (optics) Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Bone Tissue Engineering Materials
Authors: Dongming Huan | Chenkai Gu | Yawei Gu | Bo Shen | Rujing Hou | Jing Zhong | Yichang Pan | Weihong Xing
Source: ChemRxiv (article, 2026-03-04)
Field: Chemistry — Inorganic Chemistry

Physics-Constrained Artificial Intelligence for Accelerated Experimental Discovery of Metal-Organic Frameworks in Gas Separation The development of efficient adsorbents is significant but challenging for gas separation. Metal-organic frameworks (MOFs) show great potentials in cutting separation energy consumption, while suffering from the problems caused by its vast diversity. Artificial intelligence (AI) offers effective solutions, but still faces high data consumption and poor extrapolation. To address this issue, taking C 3 H 6 /C 3 H 8 as an example, we proposed a physics constrained neural network (PCNN) which integrates physical knowledge into the framework and reach a great balance between prediction accuracy and physical rationality. Meanwhile, PCNN can be well trained using much less data (< 10%) than that required by conventional models, significantly cutting the training cost. Moreover, our model can provide accurate assessments of adsorption isotherms across the full pressure range even after being trained on data from only a single pressure point, demonstrating excellent extrapolation. Finally, a series of experiments were carried out to confirm the precision of PCNN in MOF screening. MOF (CCDC ID: CUIMDZ01) with ultrahigh C 3 H 6 /C 3 H 8 selectivity (IAST selectivity of 3.5×10 5) was successfully discovered, which demonstrated the potential of our model in material discovery. This work presents an efficient workflow with broad applicability for material discovery across diverse fields. Artificial neural network ; Workflow ; Range (aeronautics) ; Work (physics) ; Energy consumption ; Energy (signal processing) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Machine Learning in Materials Science
Authors: Meijie Wang | Yuxing Lin | Yaowei Xiang | Yang Sun | Zi-zhong Zhu | Shunqing Wu | Xinrui Cao
Source: The Journal of Physical Chemistry Letters (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Curvature Engineering of SiFe Dual-Atom Catalysts for Enhanced CO <sub>2</sub> Electroreduction Geometric tuning of supports is an emerging strategy to optimize catalysts, yet its role in governing the synergy of heteronuclear p-d dual-atom catalysts (DACs) is unexplored. Using carbon nanotubes (CNTs) as tunable curvature substrates, we investigated their influence on SiFeN<sub>6</sub> DACs via first-principles calculations. We reveal an inverted-volcano-type relationship between curvature and activity, originating from the nonlinear differential response of key intermediates. This curvature-driven trend is a general principle applicable to other 3d transition metals (TM = Mn, Co, Ni). To rationalize this complex relationship, we integrated a machine learning (SISSO) approach, which yielded a robust multidimensional descriptor (<i>R</i><sup>2</sup> = 0.92). By quantitatively revealing the dominant role of the p-block Si site, our data-driven model establishes substrate geometry as a primary and effective design strategy for optimizing these complex dual-atom catalysts. Curvature ; Catalysis ; Substrate (aquarium) ; Nonlinear system ; Transition metal ; Heteronuclear molecule ; Carbon nanotube ; Process (computing) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Amir Hossein Navidpour | Amir Dashti | Ebrahim Alipanahpour Dil | Peyman Asadi | John L. Zhou | Alireza Moshfegh | Ali Altaee
Source: Advanced Composites and Hybrid Materials (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Zinc oxide photocatalysis for pollutant degradation: a review of elemental doping, morphology, and microstructure, integrated with machine learning-based performance modelling ZnO nanoparticles are effective photocatalysts for the degradation of organic pollutants; however, there are gaps of knowledge about the effects of their morphology, microstructure, doping elements, and estimation of photocatalytic performance. Accordingly, the effects of morphology, microstructure, and elemental doping are reviewed first. Notably, ZnO microstructure is an important parameter influencing its photocatalytic performance. Specifically, the wurtzite phase, which is the most stable phase of ZnO, has been widely utilized in photocatalytic applications. However, the rocksalt phase of ZnO, a metastable phase, could exhibit higher photocatalytic activity than both wurtzite ZnO and anatase TiO2. In addition to the review study, robust machine learning algorithms, including coupled simulated annealing-least square support vector machine, decision tree-particle swarm optimization, random forest- particle swarm optimization, and extreme gradient boosting-particle swarm optimization, were used to predict the photodegradation yield of various pollutants. The prediction is based on various parameters, including the molecular weight of the pollutants and dopants, topological polar surface area, hydrogen bond donor count, hydrogen bond acceptor count of the pollutants, initial concentration of pollutant, solution pH, light source, weight ratio of doping element to Zn, dosage of catalyst, and reaction time, using ZnO-based photocatalysts. A comprehensive dataset of 1176 entries was gathered from 22 different sources. To the best of our knowledge, for the first time in the field of photocatalysis, a web-based model using extreme gradient boosting-particle swarm optimization method has been developed in Python. This model can be easily accessed online to predict the photodegradation efficiency of various pollutants using ZnO-based photocatalysts. Photocatalysis ; Photodegradation ; Wurtzite crystal structure ; Anatase ; Pollutant ; Zinc ; Phase (matter) ; Hydrogen production ZnO doping and properties ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Mei Tian | Lanzhi Wang | Turghun Muhammad | Tajiguli Yimamumaimaiti | Dengbin Yu | Yan He
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

A machine learning algorithm based on three-dimensional excitation-emission matrix fluorescence spectroscopy for qualitative and quantitative analysis of polycyclic aromatic hydrocarbons in coal tar Coal tar ; Quantitative analysis (chemistry) ; Matrix (chemical analysis) ; Convolutional neural network ; Fluorescence spectroscopy ; Mass spectrometry ; Fluorescence ; tar (computing) ; Spectroscopy Spectroscopy and Chemometric Analyses ; Toxic Organic Pollutants Impact ; Vehicle emissions and performance
Authors: Leonilla Zaitseva | Iuliia Zaitceva
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A variational approach for constitutive modeling of nonlinear materials based on Bregman divergence Nonlinear system ; Formalism (music) ; Continuum mechanics ; Monotonic function ; Uniqueness ; Constitutive equation ; Solid mechanics ; Bregman divergence ; Inverse problem Nonlocal and gradient elasticity in micro/nano structures ; Model Reduction and Neural Networks ; Statistical Mechanics and Entropy
Authors: Xin Chen | Jian Chen | G.Q. He | JiaWen Jiao | Yi Zhou | Fei Zhou | Bo Dai
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Metals and Alloys

Machine learning accelerates the design of high corrosion-resistant ferritic stainless steels Corrosion ; Passivation ; Alloy ; Immersion (mathematics) ; Grain boundary ; Hydrogen Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Chidara | Cheng | Gallear
Source: Applied Sciences (article, 2026-02-07)
Field: Materials Science — Polymers and Plastics

Demonstrating an Ontological Framework for Sustainable PVC Material Science: A Holistic Study Combining Granta EduPack, Bibliometric Analysis, Thematic Analysis, Content Analysis, and Protégé Addressing the growing need for sustainable innovation in PVC materials, this study presents an illustrative framework that develops and demonstrates an ontological system that integrates lifecycle simulation using Granta EduPack, systematic literature analysis (including bibliometric, thematic, and content analytics) of peer-reviewed publications, and Protégé-based semantic reasoning, and their combination, in a holistic manner. Material and use-phase data for PVC, HDPE, PP, PET, and FRP cooling-tower components were sourced from ANSYS Granta EduPack Level-3 Polymer Sustainability 2023 R2 Version; 23.2.1, and a systematic analysis of the literature was then encoded as ontology classes, properties, and individuals following the Seven-Step ontology development method. Eco-audit simulations, standardised to a functional unit of 1 kg cooling tower fill material, reveal that the use phase dominates environmental impact (67 MJ primary energy, ~80% of total lifecycle), while material production and end-of-life recycling contribute ~15% and credits of ~900 MJ and 28 kg CO2 via recycling offsets. Ontology reasoning with corrected SWRL rules and SPARQL queries classifies VirginPVCRef and PVC10ES as strong structural materials (tensile strength ≥ 40 MPa), identifies PVCRH40 as high-moisture-risk (water absorption &gt; 0.10 g/g), and ranks hydro-thermal dechlorination (recyclability 0.90) over mechanical recycling (0.55). A systematic analysis of 40 Scopus-indexed publications (2015–2025) highlighted key themes in recycling technologies, LCA emissions, additive toxicity, ontology frameworks, machine learning integration, circular economy policy, and cooling-tower applications. Demonstrated via a simulation-based cooling-tower case study, hybrid PVC-FRP designs yield the highest justified Material Sustainability Performance Index (MSPI), outperforming PVC-only and FRP-only alternatives. This framework provides a conceptual decision-support tool for exploring PVC material optimisation, illustrating pathways to enhancing circularity and environmental responsibility in industrial applications. The proposed framework is, therefore, not intended as a validated decision-support tool, nor does it claim analytical optimisation or predictive performance but rather serves as a method of illustration that shows how domain knowledge can be formally structured using ontology principles linked to simulation representations, and that was examined for internal logical consistency. Ontology ; Sustainability ; SPARQL ; Key (lock) ; Life-cycle assessment ; Content analysis Polymer Science and PVC ; Effects and risks of endocrine disrupting chemicals ; Polymer crystallization and properties
Authors: P. V. Stishenko | Qian Chen | Julia Westermayr | Reinhard J. Maurer | Andrew J. Logsdail
Source: The Journal of Chemical Physics (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Practical integration of machine learning into <i>ab initio</i> calculations and workflows: Accelerating the SCF cycle via density matrix predictions Data-driven approaches offer great potential for accelerating ab initio electronic structure calculations of molecules and materials, but their transferability is often limited due to the vast amount of data needed for training, including the need to fine-tune universal models for each specific system to be studied. Here, we demonstrate how contributions from system-specific electronic structure machine learning (ESML) models may be combined ("stitched") to deliver density matrices of entire systems of interest, improving the initial guess for the self-consistent field cycle and delivering gains in computational efficiency. The "stitching" of density matrices is demonstrated for sequential calculations, such as geometry optimization and molecular dynamics, and we show that the synergistic use of ESML models and density matrix extrapolation algorithms can accelerate standard computational calculations. The algorithms are demonstrated for test cases relating to water clusters and a methane clathrate cage, with the benefits discussed. The future opportunities for hybrid quantum mechanical and ML (QM/ML), and also ML/ML paradigms, are broad-ranging with significant computational speed-up attainable. Extrapolation ; Field (mathematics) ; Density matrix ; Ab initio ; Matrix (chemical analysis) ; Transferability ; Density functional theory ; Quantum ; Electronic structure Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Qianyu Li | Juan Li | Binghua Jing | Didi Li | Zhimin Ao
Source: Chinese Chemical Letters (article, 2026-03-01)
Field: Materials Science — Biomaterials

Environmental risk analysis and sustainable modification of polylactic acid (PLA): Integrated bibliometric and machine learning insights Polylactic acid ; Microplastics ; Sustainable development ; Mechanism (biology) ; Risk assessment ; Risk management ; Identification (biology) biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Nanocomposite Films for Food Packaging
Authors: jiazheng xu | Qiaoyu Hu | Changzhi Sun | Shoude Zhang | Xiaoli Li | Xuemin Ding | Yichen Liao | Wenchao Tu | Youzhe Chen | Xuewen Wu | Wenjun Zhang | Zihao Shen | Yanyan Mao | Kai Zhang | Liang He | Kaixian Chen | Xuhong Qian | Hui Li
Source: ChemRxiv (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Accurate Structure Prediction of Natural Products with NatGen Natural products (NPs), as a vital source of pharmaceutical agents, have contributed to the development of 60% of marketed small-molecule drugs. However, NP-based drug discovery faces a major challenge due to the combinatorial expansion of NPs' configurational space and their complex 3D structures, which arise from atomic chirality dictated by stereospecific biosynthetic enzymes. To date, more than 30% of natural products with stereocenters lack complete stereochemical assignments, and only about 1% of known natural products have fully resolved crystal structures. To overcome this bottleneck, we introduce NatGen, an innovative deep learning framework designed to predict both the stereochemical configurations (R/S and E/Z) and the 3D conformations of natural products. NatGen integrates data augmentation strategies with generative modeling techniques and outperforms baseline models in configurational prediction, achieving a full-assignment accuracy of 78.39% (Top-5) on the COCONUT test set. In an external test set comprising 17 recently elucidated plant-derived natural products, NatGen attains a full-assignment accuracy of 88.24% (Top-5). The RMSD of the best matching conformation among the top 15 candidates generated by NatGen is consistently below 1.5 Å. Using NatGen, we successfully predicted the 3D structures of 721,010 NPs from COCONUT - the largest open NP repository to date - and made the full dataset publicly available at https://www.lilab-ecust.cn/natgen/. We believe this resource significantly expands the structural landscape of natural products and will empower researchers to cross-validate findings and accelerate progress in diverse fields including natural product chemistry, enzymatic biosynthesis, physical, organic and analytical chemistry, phytochemistry, NP and NP-derived drug discovery. Chemical space ; Natural product ; Natural (archaeology) ; Set (abstract data type) ; Matching (statistics) ; Stereocenter ; Product (mathematics) ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Ryan Liu | Eric Qu | Tobias Kreiman | Samuel M. Blau | Aditi S. Krishnapriyan
Source: ArXiv.org (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we introduce the Bond Smoothness Characterization Test (BSCT). This efficient benchmark probes the PES via controlled bond deformations and detects non-smoothness, including discontinuities, artificial minima, and spurious forces, both near and far from equilibrium. We show that BSCT correlates strongly with MD stability while requiring a fraction of the cost of MD. To demonstrate how BSCT can guide iterative model design, we utilize an unconstrained Transformer backbone as a testbed, illustrating how refinements such as a new differentiable $k$-nearest neighbors algorithm and temperature-controlled attention reduce artifacts identified by our metric. By optimizing model design systematically based on BSCT, the resulting MLIP simultaneously achieves a low conventional E/F regression error, stable MD simulations, and robust atomistic property predictions. Our results establish BSCT as both a validation metric and as an "in-the-loop" model design proxy that alerts MLIP developers to physical challenges that cannot be efficiently evaluated by current MLIP benchmarks. Spurious relationship ; Smoothness ; Differentiable function ; Energy minimization ; Metric (unit) ; Benchmark (surveying) ; Potential energy ; Potential energy surface ; Interatomic potential ; Energy (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions