jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyu Xu | Arif Ullah | Ming Yang
Source: arXiv (Cornell University) (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

A Physics-Informed Chemical Rule for Topological Materials Discovery Topological phases of matter$\unicode{x2013}$comprising both insulators and semimetals$\unicode{x2013}$offer great potential for quantum applications, but identifying new candidates remains challenging due to expensive first-principles simulations and labor-intensive experimental workflows. Here we introduce a physics-informed chemical rule that integrates compositional, orbital and crystallographic descriptors within an interpretable linear framework. By explicitly encoding electron filling, space-group symmetry and orbital-resolved chemical environments, our method overcomes a fundamental limitation of composition-only heuristics$\unicode{x2013}$their inability to distinguish polymorphs with identical stoichiometry but different crystal structures. Using only elemental characteristics, our approach reduces a material's topological propensity to a single, physically interpretable score, enabling rapid and high-throughput assessment. The model achieves superior predictive performance while maintaining physical transparency, and identifies candidate topological materials where conventional symmetry indicators fail. Consequently, our framework enables rapid and interpretable exploration of complex materials spaces, establishing a scalable paradigm for the intelligent discovery of next-generation topological and quantum materials. Topological insulator ; Scalability ; Topology (electrical circuits) ; Symmetry (geometry) ; Topological data analysis ; Quantum chemical ; Encoding (memory) Machine Learning in Materials Science ; Topological Materials and Phenomena ; Advanced Electron Microscopy Techniques and Applications
Authors: Yujin Han | Hanhwi Jang | K-Y Ahn | Gwangsik Mun | Sumin Kim | Taek‐Soo Kim | Taejoo Kim | JongYul Kim | Min-Wook Oh | Yeon Sik Jung
Source: Advanced Materials (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Hierarchical Co‐Assembly Achieves Shape‐Programmable All‐Boron‐Nitride Monoliths with Excellent Thermophysical Performances Despite boron nitride's (BN) exceptional physical performance and durability, the current lack of a systematic methodology for BN processing, which stems from its extreme robustness, often necessitates the use of additive materials, thereby frequently sacrificing its desirable properties. Here, we report binder-free BN monoliths derived from a suspension with tunable rheology and long-term colloidal stability. The control of solvent affinity allows the production of two distinct BN morphologies: (1) physically exfoliated, large-size BN flakes (p-BN) and (2) mechanochemically produced, small-size BN particles (m-BN) with hydroxyl-functionalized edges. Crucially, the interfacial interactions and aspect ratio complementarity between the two BN components enable spontaneous co-assembly into a long-term stable, binder-free suspension with programmable rheology. The resulting binder-free BN films exhibit a 19-fold enhancement in cohesive energy (3.8 J·m<sup>-</sup> <sup>2</sup> vs. 0.20 J·m<sup>-</sup> <sup>2</sup> for p-BN), high in-plane thermal conductivity (>40.6 W·m<sup>-</sup> <sup>1</sup>·K<sup>-</sup> <sup>1</sup>), and a neutron absorption coefficient of 28.3 cm<sup>-</sup> <sup>1</sup>, offering a promising solution for advanced aerospace, nuclear, and optoelectronic systems operating under severe environmental constraints. Rheology ; Thermal conductivity ; Suspension (topology) ; Colloid ; Boron ; Conductivity ; Thermal ; Neutron capture Thermal properties of materials ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Beibei Wang | Jianjun Wang
Source: Molecules (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Prediction of Bandgap and Key Feature Analysis of Lead-Free Double Perovskite Oxides Based on Deep Learning Lead-free double perovskites possess the capabilities of wide bandgap control, excellent photoelectric performance, and environmental friendliness. They are an ideal alternative system for addressing the heavy metal toxicity of lead-based perovskites and promoting their large-scale application. Precise control of their bandgap is key to the green transformation of optoelectronic devices. Bandgap, as a key parameter determining the photoelectric properties of materials, has limitations in traditional experimental determination and DFT calculation methods, such as being time consuming, labour intensive, costly, and difficult to achieve high-throughput screening. Deep learning provides an efficient solution to this problem, but current research has issues such as a single-model architecture and poor interpretability, which cannot effectively support bandgap regulation. This study utilised 2367 valid datasets of lead-free double perovskites sourced from the Materials Project database and relevant literature. Following preprocessing steps, including MinMaxScaler normalisation and Pearson correlation coefficient screening, the dataset was divided into a ratio of 7:1:2. The bandgap prediction capabilities of four models-MLP, deep ensemble learning, PINN, and Transformer-were systematically compared, with feature importance analysed using the SHAP method. The results show that the MLP model performs the best in medium-scale, structured feature prediction. The R<sup>2</sup> value of the test set is 0.9311, while the MAE, MSE, and RMSE are 0.1915 eV, 0.0975 eV<sup>2</sup>, and 0.3122 eV, respectively. A total of 98% of the test samples have a prediction error of ≤0.4 eV, highlighting the stability of low bandgap systems. The Transformer is more suitable for large-scale, sequential feature prediction, while the MLP has limited generalisation ability for medium-to-high bandgap systems containing elements such as Si and Mg. The SHAP analysis revealed that the five electronic structure descriptors, such as B_HOMO+ and A_LUMO+, are the key influencing factors of the bandgap. The research results are helpful for the high-precision prediction and mechanism explanation of the bandgap of lead-free double perovskites, providing theoretical support for rational material design, performance optimisation, and bandgap-oriented regulation. They also point out the direction for subsequent model improvement. Band gap ; Photoelectric effect ; Stability (learning theory) ; Feature (linguistics) ; Preprocessor ; Deep learning ; Mean squared error ; Perovskite (structure) ; Feature extraction Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Ting Su | Chao Rong | Tianhao Yu | Shan Hu | Pan He | Bowei Zhang | Yabin Yan | Fu-Zhen Xuan
Source: UCL Discovery (University College London) (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Progress and perspectives of high-quality mechanical properties testing and mechanisms for 2D materials Two-dimensional (2D) materials have attracted extensive attention from aerospace, integrated circuits, precision sensors, and flexible electronics due to their unique layered structure and excellent physicochemical properties. In practice applications, the components of functional nanodevices are subjected to mechanical stress, which can affect the robust performance and structural reliability of these devices. Therefore, it is imperative to explore the mechanical properties and underlying mechanisms of 2D materials. However, researchers have an inadequate understanding of the accuracy of various in situ microscopy techniques and neglect the significance of high-quality, clean transfer techniques, resulting in deviated measurement results. There is now an urgent need to develop guidelines that allow researchers to select appropriate material transfer techniques and mechanical testing strategies based on the specific properties of 2D materials. Furthermore, the mechanical mechanism of 2D materials lacks systematic and comprehensive studies, which hinders researchers from deeply understanding the relationship between the material structure and the device performance. This work reviews the latest progress in the mechanics of 2D materials, focusing on the challenges of various transfer techniques and in situ microscopy techniques in mechanical testing, and provides effective guidance for the formulation of experimental schemes for mechanical testing. In addition, we offer detailed mechanistic insights into the fracture behavior, geometric dimension effects, edge defects, and interlayer bonding effects of 2D materials. This work is expected to advance the field development of 2D material mechanics. Reliability (semiconductor) ; Mechanism (biology) ; Field (mathematics) ; Work (physics) ; Materials testing ; Electronics ; Enhanced Data Rates for GSM Evolution 2D Materials and Applications ; Graphene research and applications ; Thermal properties of materials
Authors: Quinn Gallagher | Sonia Hasko | Michael Webb
Source: ChemRxiv (article, 2026-03-22)
Field: Materials Science — Materials Chemistry

Minimizing Data Requirements for Material Property Prediction via Cluster-based Training Set Selection Machine learning (ML) is increasingly employed to predict material properties, and when data are scarce, data acquisition is often necessary to support these efforts. Despite the practical advantages of minimizing acquisition costs, it remains unclear which data acquisition strategies are most efficient across diverse property prediction tasks or what principled basis underlies their relative performance. Here, we survey 100 space-filling and active learning algorithms across 60 property prediction tasks, finding that cluster-based training set selection consistently outperforms active learning, a result that runs counter to prevailing practice and is robust to the dimensionality of material representations, the choice of fixed or learned representations, and training set size. A theoretical framework grounded in Gaussian process variance establishes cluster-based selection as the principled optimum in the low-data limit, providing a unified basis for understanding training set quality across contexts. Beyond predictive accuracy, cluster-based selection is label-independent and fully parallelizable, enabling simultaneous dataset construction for multiple properties without iterative experimental feedback. Overall, this work establishes cluster-based training set selection as a broadly effective and principled strategy for data-efficient, ML-enabled materials design. Property (philosophy) ; Set (abstract data type) ; Selection (genetic algorithm) ; Training set ; Curse of dimensionality ; Process (computing) ; Variance (accounting) ; Data set ; Quality (philosophy) Machine Learning in Materials Science ; Nanoporous metals and alloys ; Catalysis and Oxidation Reactions
Authors: Raphael M. Tromer | Isaac M. Félix | Rafael Besse | Marcelo L. Pereira Junior | Marcos G. E. da Luz
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

A Comparative Study of Structural Representations for 2D Materials: Insights from Dynamic Collision Fingerprint and Matminer In materials science, the selection of structural descriptors for machine learning protocols strongly influences predictive performance and the degree of physical interpretability that can be achieved from the derived models. Although more complex descriptors may improve numerical accuracy, they often represent extra computational load, also reducing transparency into the underlying structural information. A framework called the Dynamic Collision Fingerprint (DCF) was recently proposed with the goal of producing concise, physically significant representations, generating descriptors via dynamical probing of atomic structures. In this work, we benchmark DCF using a dataset composed of 120 two-dimensional carbon allotropes and compare its performance with the widely considered Matminer library. The analysis employs three regression models, linear regression, decision tree, and XGBoost, evaluated over train and test partitions ranging from 10\% to 90\% and repeated over multiple random seeds in order to characterize statistical variability. The obtained results demonstrate that DCF easily matches Matminer in terms of predicting accuracy across all learning algorithms. However, it accomplishes this using descriptors that are significantly lower dimensional, pointing to manageable computing costs. Moreover, compared to the rather technical Matminer descriptions, the DCF exhibits considerably clearer physical interpretability. These findings suggest that DCF is a significant substitute for high-dimensional descriptor libraries as structural representation since it is both computationally flexible and physically grounded. Interpretability ; Representation (politics) ; Benchmark (surveying) ; Collision ; Feature selection ; Fingerprint (computing) ; Selection (genetic algorithm) ; Regression Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Advanced Graph Neural Networks
Authors: Qi Li | Mingyu Guo | Rui Jiao | Jing Gao | Fanjie Xu | Haonan Xue | Weixiong Zhang | Wenbing Huang | Junchi Yan | Linfeng Zhang | Cheng Ping Wang | Zhuang Yan | Guolin Ke | Weinan E | Zhiyong Tang | Shifeng Jin | Lin Yao
Source: ArXiv.org (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Experimental Powder X-ray Diffraction Crystal Structure Determination with RealPXRD-Solver Determining crystal structures from experimental powder X-ray diffraction data remains challenging because peak overlap, preferred orientation, and impurity phases obscure atomic arrangements. We present RealPXRD-Solver, a generative model trained on 6,250,238 theoretical structures with experiment-mimicking augmentations and a universal encoder of d-spacing--intensity fingerprints, enabling both lattice-conditioned and lattice-free inference. RealPXRD-Solver reaches a 98.3% Top-20 match rate on a 10,000-structure theoretical benchmark and achieves Top-1/Top-20 accuracies of 77.9%/91.9% on CNRS and 78.8%/92.9% on RRUFF experimental datasets, and it solved 39 previously unreported Powder Diffraction File entries. Diffraction ; Powder diffraction ; Crystal structure ; X-ray crystallography ; Impurity ; Encoder X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Martin Hoffmann Petersen | Chiku Parida | Jonas Busk | Jin Hyun Chang | Arghya Bhowmik | Juan Maria Garcia Lastra
Source: Advanced Intelligent Discovery (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials Recently, universal machine learning interatomic potentials (MLIPs) are being increasingly adopted to study various physical properties of promising new materials. To enhance the energy density and ionic conductivity of sodium‐ion batteries (SIBs), polyanion‐type cathode materials with transition metal ion doping are being explored, for which MLIPs can be used. Even with broad spectrum training data, universal MLIPs still suffer from inaccurate predictions in out‐of‐distribution systems. We developed an MLIP based on an update to the popular PaiNN(charge‐PaiNN, cPaiNN) architecture to include a description of atomic charges that can work with training datasets with partial labeling of atomic charges. By comparing a cPaiNN model specifically trained on polyanion sodium cathode materials against three universal MLIPs, CHGNet, MACE‐MP‐0, and M3GNet, we demonstrate the necessity of system‐specific datasets to improve model accuracy. We further demonstrate that fine‐tuning universal MLIPs on system‐specific datasets improves performance, achieving accuracy comparable to or better than training from scratch. Nonetheless, cPaiNN generally outperforms all foundation models in both accuracy and computational speed on the system specific test dataset. Furthermore, we check the importance of atomic charge predictions for accurately predicting the physical properties of cathode materials. Cathode ; Work (physics) ; Energy (signal processing) ; Foundation (evidence) ; Ionic bonding ; Characterization (materials science) ; Doping ; Ion Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Ruiling Liao | Yan Liu | Sude Ma | Y. Zhang
Source: Coatings (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Sol–Gel CaCO3/SiO2 Boost Anti-Flashover Silicones This study developed high-performance anti-flashover silicone coatings using sol–gel-synthesized CaCO3/SiO2 hierarchical fillers optimized via L16(45) orthogonal design. The optimal filler (Sample 5) was prepared under 70 vol% ethanol, with nTEOS:nCaCO3 = 1:1 and 0.2 mol/L NH3·H2O, at 45 °C, for 18 h, featuring covalent Si-O-Ca bonding, a dual-scale microstructure (2–4 μm CaCO3 cores + 20–40 nm SiO2 nodules), a 14.44 m2/g specific surface area, and bimodal porosity (8–80 nm). Composite C7 (30 wt% filler, 3 wt% KH-570, 1:2 resin-to-filler ratio) achieved superhydrophobicity (a 153° contact angle via Cassie-Baxter stabilization), ultrahigh electrical insulation (3.20 × 1014 Ω·cm volume resistivity, 1.60 × 1013 Ω surface resistivity), and robust mechanical properties (Shore 3H hardness, 5B adhesion). Standardized IEC 60507:2020 tests showed that C7’s flashover voltages (14.8 kV for KMnO4, 14.3 kV for NaCl/KMnO4, 13 kV for NaCl) exceeded that of neat silicone resin (NSR) and conventional CaCO3-filled composite (SR-CC) by &gt;135%. Additionally, C7 retained superhydrophobicity after 500 h UV aging and maintained a 124° contact angle after 12 months of outdoor exposure. The superior performance stems from synergistic hierarchical topology, tortuous discharge paths, and interfacial passivation. This work establishes a microstructure-driven design paradigm for grid protection materials in harsh environments. Contact angle ; Composite number ; Silicone ; Microstructure ; Porosity ; Filler (materials) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Surface Modification and Superhydrophobicity
Authors: Yang Li | Yanzhen Wang | Boheng Zhao | Xiaoxun Gong | Yuxiang Wang | Zechen Tang | Zixu Wang | Zilong Yuan | Jialin Li | Minghui Sun | Zezhou Chen | Honggeng Tao | Baochun Wu | Yuhang Yu | He Li | Felipe H. da Jornada | WenHui DUAN | Yong Xu
Source: ArXiv.org (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery. Field (mathematics) ; Artificial neural network ; Software ; Electronic structure ; Implementation ; Electronic materials ; Deep learning Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Weiqi Liu | Fenglei Cao | Yuan Qi | Li‐Cheng Xu
Source: ArXiv.org (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

A Pre-trained Reaction Embedding Descriptor Capturing Bond Transformation Patterns With the rise of data-driven reaction prediction models, effective reaction descriptors are crucial for bridging the gap between real-world chemistry and digital representations. However, general-purpose, reaction-wise descriptors remain scarce. This study introduces RXNEmb, a novel reaction-level descriptor derived from RXNGraphormer, a model pre-trained to distinguish real reactions from fictitious ones with erroneous bond changes, thereby learning intrinsic bond formation and cleavage patterns. We demonstrate its utility by data-driven re-clustering of the USPTO-50k dataset, yielding a classification that more directly reflects bond-change similarities than rule-based categories. Combined with dimensionality reduction, RXNEmb enables visualization of reaction space diversity. Furthermore, attention weight analysis reveals the model's focus on chemically critical sites, providing mechanistic insight. RXNEmb serves as a powerful, interpretable tool for reaction fingerprinting and analysis, paving the way for more data-centric approaches in reaction analysis and discovery. Embedding ; Curse of dimensionality ; Visualization ; Focus (optics) ; Bridging (networking) ; Reaction coordinate ; Dimensionality reduction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Anietie Okon Etim | Paul Musonge | Joseph K. Bwapwa
Source: ChemistrySelect (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Used Frying Oil Decolorization Study Using Eco‐Friendly Nano‐Clay Adsorbent: Process Optimization by RSM and ANFIS Approaches ABSTRACT Waste remediation offers a sustainable pathway for the recovery and valorization of waste resources. In this study, the process of decolorization of used frying oil (UFO) was investigated using an eco‐friendly Fuller's earth (FE) adsorbent. The key process parameters, including adsorbent dosage, contact time, and temperature, were optimized and analyzed using Box Behnken Design—response surface methodology (BBD‐RSM) and Adaptive Neural Fuzzy Inference System (ANFIS) models. Under the established optimal condition of FE dosage of 4.5 wt%, contact time of 30 min, and temperature of 90°C, the predicted color removal efficiencies of the RSM and ANFIS were 94.91% and 95.60%, respectively, while the actual removal efficiency achieved was 95.65%. The statistical analyses of the models showed that the ANFIS model exhibited superior predictive capability, with a higher coefficient of determination ( R 2 ) of 0.999 and a lower mean absolute percentage deviation (MAPD) of 0.20%, as compared to the RSM model with R 2 of 0.987 and MAPD of 0.68%. Sensitivity analysis indicated that the bleaching temperature and adsorbent dosage were the most significant variables affecting the process. The physicochemical characteristics of decolorized used frying oil (DUFO) suggest its potential as a useful industrial raw material for the production of value‐added products, such as biodiesel, hydraulic fluids, soaps, and bio‐lubricants. Response surface methodology ; Adaptive neuro fuzzy inference system ; Raw material ; Box–Behnken design ; Artificial neural network ; Inference system ; Process (computing) ; Design of experiments ; Coefficient of determination Edible Oils Quality and Analysis ; Biodiesel Production and Applications ; Adsorption and biosorption for pollutant removal