jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weitao Wang | Xin Wu | Yunhui Wu | Sebastian Volz | Takeshi Takagi | Masahiro Nomura
Source: Materials Today Physics (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Interfacial thermal transport analysis using machine learning potential at AlN/Cu van der Waals interface Three-dimensional integration is critical for next-generation integrated circuits, yet thermal management remains challenging due to complex interface environment. Owing to its high thermal conductivity, aluminum nitride (AlN) is a promising insulating layer for through-silicon via structures, yet its interfacial thermal behavior with copper (Cu) electrodes remains insufficiently understood. Here, we investigate phonon transport across non-bonded AlN-Cu interfaces governed by van der Waals (vdW) interactions using a machine learning potential (MLP) trained by density functional theory (DFT) data. The MLP accurately reproduces DFT-level phonon dispersion, energy-volume relationships, and interfacial binding energies. Non-equilibrium molecular dynamics simulations reveal total thermal resistance values of 10-14 m 2 K/GW at room temperature, approximately five times smaller than values obtained with empirical potentials and in excellent agreement with experimental data for similar semiconductor-metal interfaces. Unlike empirical potentials, the MLP predicts increasing thermal resistance with temperature, attributed to phonon softening and vibrational mismatch. Spectral decomposition analyses confirm reduced phonon thermal conductance at elevated temperatures. This work provides fundamental insights into weakly bonded semiconductor-metal interfaces and guidance for thermal management in 3D integrated systems. van der Waals force ; Phonon ; Interfacial thermal resistance ; Thermal conductivity ; Thermal ; Molecular dynamics ; Density functional theory ; Work (physics) Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Yongqi Jin | Yecheng Wang | Jun-jie Wang | Rong Zhu | Guolin Ke | Weinan E
Source: arXiv (Cornell University) (preprint, 2026-01-26)
Field: Chemistry — Spectroscopy

From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is fundamental to spectral analysis and molecular structure elucidation, yet existing machine learning methods rely on limited, labor-intensive atom-assigned datasets. We propose a semi-supervised framework that learns NMR chemical shifts from millions of literature-extracted spectra without explicit atom-level assignments, integrating a small amount of labeled data with large-scale unassigned spectra. We formulate chemical shift prediction from literature spectra as a permutation-invariant set supervision problem, and show that under commonly satisfied conditions on the loss function, optimal bipartite matching reduces to a sorting-based loss, enabling stable large-scale semi-supervised training beyond traditional curated datasets. Our models achieve substantially improved accuracy and robustness over state-of-the-art methods and exhibit stronger generalization on significantly larger and more diverse molecular datasets. Moreover, by incorporating solvent information at scale, our approach captures systematic solvent effects across common NMR solvents for the first time. Overall, our results demonstrate that large-scale unlabeled spectra mined from the literature can serve as a practical and effective data source for training NMR shift models, suggesting a broader role of literature-derived, weakly structured data in data-centric AI for science. Chemical shift ; Robustness (evolution) ; Matching (statistics) ; Spectral line ; NMR spectra database ; Generalization ; Set (abstract data type) ; Training set Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Xingze Geng | Wentao Zhang | Lin-Wang Wang | Xiangying Meng
Source: npj Computational Materials (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Origin of the machine learning forces field errors across metal elements The overall development of the machine learning force field (MLFF) has advanced rapidly, with a wide range of models emerging in recent years. However, some fundamental questions remain underexplored, such as why certain systems are intrinsically more difficult to train than others. Understanding this question can help us to propose different models and prepare appropriate datasets for different situations. We constructed Metal-43, a high-quality dataset comprising elemental structures of 43 metallic elements. Through systematic analysis, we reveal regular trends of fitting accuracies of these elemental metals in the periodic table. Unlike previous approaches that generally attribute fitting challenges to a vague notion of a “complex potential energy surface (PES)”, which is almost a synonym of the fitting difficulty, we provide a physical picture which connects the Fermi surface complexity to this complexity of PES. Furthermore, we demonstrate that current MLFF models still face clear limitations in capturing the complex PES even for elemental materials. These findings can provide a theoretical foundation and directional guidance for the development of more general and accurate MLFF models in the future. Field (mathematics) ; Range (aeronautics) ; Face (sociological concept) ; Surface (topology) ; Energy (signal processing) ; Development (topology) ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: M Arunadevi | Manjula H.M. | Kousik S | K. Balakrishnan | G. N. Kumaraswamy | Santosh Srinath K | ShivaPrakash S | Kumara Thanaiah | Srinivasan | Saravana Bavan | Adem Abdirkadir Aden
Source: Journal of The Institution of Engineers (India) Series D (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

Material Classification Using Decision Tree Algorithm, Support Vector Machine, and K-Means Clustering based on Density and Strength Decision tree ; Support vector machine ; Cluster analysis ; Decision tree learning ; Selection (genetic algorithm) ; Supervised learning ; Property (philosophy) ; Pattern recognition (psychology) ; Feature selection Machine Learning in Materials Science ; Material Selection and Properties ; Calcium Carbonate Crystallization and Inhibition
Authors: Bo Song | Ting Liu | M. X. Zhao | Yongcan Shuang | Junghui Chen | Zoltan K. Nagy | R. Findeisen
Source: Journal of Process Control (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Efficient data-driven modeling and multi-objective optimization of batch crystallization processes using integrated neural network Batch crystallization is a key operation in the pharmaceutical industry, yet the optimization of product properties such as crystal size distribution, aspect ratio, and yield remains challenging. The difficulty arises from the complex crystallization process and the high cost of collecting sufficient experimental data. In this paper, a data-driven framework that integrates an efficient design of experiments (EDoE) with an integrated neural network (INN) model is proposed to enable systematic multi-objective optimization of such process. A data-driven modeling approach based on integrating the convolutional neural network (CNN) and recurrent neural network (RNN) is presented to map the relationship between the primary operating conditions (including the initial supersaturation and cooling rate) and two-dimensional (2-D) crystal size distribution (CSD). An EDoE is developed to generate informative data while reducing the number of experiments for the above modeling, by taking sensitivity analysis on the product CSD regarding the primary operating conditions. Meanwhile, a comprehensive loss function related to the prediction errors on product CSD is introduced to optimize the INN model hyperparameters. Building on this model, three alternative multi-objective optimization programs are offered to optimize these operating conditions for a good trade-off between product yield, the concentration of product CSD, and the aspect ratio (AR) of crystals. Simulation studies and experiments on β-form L -glutamic acid demonstrate that the proposed approach improves predictive accuracy, reduces experimental effort, and identifies superior operating strategies. • INN-based modeling maps the nonlinear relationship between primary operating conditions and 2-D crystal size distribution. • Efficient DoE via sensitivity analysis yields informative INN modeling data while minimizing experiment numbers. • Three MOO programs optimize operating conditions to balance product yield, CSD concentration, and crystal aspect ratio (AR). • A comprehensive loss function related to product CSD prediction errors optimizes the INN model hyperparameters. • L-glutamic acid experiments validate enhanced MOO and reduce modeling experiments by about 50% compared to traditional DoE. Artificial neural network ; Sensitivity (control systems) ; Process (computing) ; Convolutional neural network ; Key (lock) ; Process optimization ; Product (mathematics) ; Nonlinear system ; Supersaturation Crystallization and Solubility Studies ; Process Optimization and Integration ; Drug Solubulity and Delivery Systems
Authors: Zahra Tohidi Nafe | Ádám Madarász
Source: npj Computational Materials (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Dielectric properties of disordered crystalline materials: a computational case study on hexagonal ice Abstract The dielectric properties of disordered crystalline materials are governed by long-range orientational correlations arising from local structural disorder. We present a statistical and topological framework that connects hydrogen-bond network features to macroscopic dielectric anisotropy. Using hexagonal ice as a model system, we represent the network as a directed graph and employ the polarization index, originally introduced in the GenIce software, to measure the net traversal of percolating hydrogen-bond chains through the periodic lattice. Effective bond dipole moments, determined from moderately sized simulation cells, are combined with the variance of the polarization index to predict dielectric constants for much larger cells without additional computations on three-dimensional structures. We validate this Polarization Index-Based Effective Dipole (PIBED) model using the AMOEBA14 and neural network potentials, with and without nuclear quantum effects. The results agree with the estimates from the traditional Total Dipole Fluctuation (TBF) model and exhibit improved statistical convergence, enabling robust estimation of the small dielectric anisotropy of ice Ih. Our findings establish a generalizable method for quantifying dielectric response in disordered crystals and may offer insights into the dielectric behavior of partially ordered systems such as hybrid perovskites and solid-state proton conductors. Dielectric ; Dipole ; Anisotropy ; Polarization (electrochemistry) ; Ice Ih ; Hexagonal crystal system ; Polarizability ; Statistical model Material Dynamics and Properties ; Solid-state spectroscopy and crystallography ; Machine Learning in Materials Science
Authors: Jonas Grandel | Philipp Benner | Janine George
Source: arXiv (Cornell University) (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how different fine-tuning strategies influence the prediction of harmonic phonon band structures, thermal properties, and the potential energy surface along imaginary phonon modes. We achieve substantial accuracy improvements with minimal additional data, with as few as 10 additional training structures already yielding significant gains. In addition to existing approaches, we introduce Equitrain, a finetuning framework that implements LoRA-based adaptation. Across 53 materials systems, we show that fine-tuned models consistently outperform both the underlying pretrained model and models trained from scratch. Equitrain achieves the best overall performance, and our results demonstrate that fine-tuning enables accurate phonon predictions. Phonon ; Thermal ; Interatomic potential ; Energy (signal processing) ; Density functional theory Machine Learning in Materials Science ; Thermal properties of materials ; Quantum many-body systems
Authors: Henning Windeck | Daniel Willimetz | Andreas Erlebach | Christopher J. Heard | Lukáš Grajciar | Fabian Berger | Joachim Sauer
Source: The Journal of Physical Chemistry Letters (article, 2026-01-14)
Field: Chemistry — Inorganic Chemistry

Water Adsorption at Pairs of Proximate Brønsted Acid Sites in Zeolites We model water adsorption at pairs of proximate Brønsted acid sites (BASs) in zeolites H-MFI, H-FAU, and H-CHA. We use machine-learning potentials to explore the potential energy surface, combined with quantum mechanical methods for chemically accurate energies of selected structures. We identify BAS pairs that adsorb water cooperatively, forming an H-bonded chain that connects the two BASs and provides additional stabilization. The formation of such a water bridge requires at least two molecules, making the adsorption of the second water molecule stronger than the first, e.g., by 20 and 44 kJ mol<sup>-1</sup> for an Al9-Al10 and an Al4-Al6 pair, respectively, in H-MFI, and by 11 kJ mol<sup>-1</sup> for H-FAU. The magnitude of this extra-stabilization depends on the relative alignment of the BASs. Both Al pairs separated by just one SiO<sub>4</sub> tetrahedron (next-nearest neighbor sites) and pairs across a 10-membered ring are included. The increase of the heat of adsorption with the water loading per BAS contrasts with the decrease obtained for isolated BASs and aligns with observations in some experiments. Adsorption ; Molecule ; Tetrahedron ; Zeolite ; Proximate Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Ammonia Synthesis and Nitrogen Reduction
Authors: Sung-Wook Hwang
Source: Journal of Wood Science (article, 2026-02-05)
Field: Chemistry — Organic Chemistry

Machine learning-driven research in wood science: from prediction to understanding through the framework of Wood Informatics Abstract Machine learning (ML) has rapidly expanded across wood science, enabling data-driven approaches for the characterization, performance evaluation, and process optimization of wood and wood-based materials. Although these approaches have demonstrated remarkable predictive accuracy, the field now faces critical challenges related to data heterogeneity, model generalization, and interpretability. Most existing models are developed under narrowly defined experimental conditions, limiting their robustness across different wood species, measurement instruments, and environmental settings. Similarly, while explainable artificial intelligence techniques have enhanced model transparency, their outputs often remain qualitative and insufficiently aligned with the physical and chemical mechanisms governing wood behavior. This review synthesizes the current landscape of ML-driven research in wood science and identifies key challenges for future advancement, emphasizing the need for AI-ready datasets, reliable and generalizable models, and scientifically interpretable approaches. To address these issues, the concept of Wood Informatics is introduced as an integrative framework that connects data standardization, model reliability, and physics-informed interpretability within a unified research ecosystem. By linking prediction to understanding, Wood Informatics—integrating standardized datasets, reliable models, and physically consistent interpretations—establishes a robust foundation for data-centric, reproducible, and explanatory wood science. This transition signifies not only a technological advancement but also a paradigm shift in how wood and wood-based systems are analyzed, understood, and designed. Interpretability ; Robustness (evolution) ; Field (mathematics) ; Informatics ; Process (computing) ; Limiting Wood and Agarwood Research ; Wood Treatment and Properties ; Machine Learning in Materials Science
Authors: Daniel Zhakupov
Source: ChemRxiv (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Physics-Informed Multi-Output Machine Learning for Predicting Nanozyme Kinetic Parameters Nanozymes-artificial nanomaterials with enzyme-like catalytic properties-offer superior durability compared to natural enzymes, enabling broad applications in biosensing and therapeutics. However, their rational design remains challenging due to immense structural heterogeneity and a reliance on trial-and-error. While machine learning (ML) accelerates materials discovery, conventional models often lack physicochemical grounding. We present a multi-output, physics-informed ML framework for predicting the core catalytic kinetics (Km, kcat, Vmax) of nanozymes. Using a curated dataset from NanozymeDB, we integrated the Arrhenius equation (activity-specific activation energies), pH-dependent Gaussian zones, and material properties (electronegativity, redox potential). Our model achieves robust predictive performance: R² = 0.555 for Km, 0.319 for kcat, and 0.524 for Vmax, with 55.6 of Vmax predictions falling within a factor of two of experimental values. Feature importance analysis confirmed that the Arrhenius factor (importance = 0.228) and pH zones (0.30 combined) dominate predictions, validating the physics-informed approach. This demonstrates that embedding thermodynamic constraints into ML enhances both predictive reliability and interpretability for rational nanozyme design. Interpretability ; Arrhenius equation ; Reliability (semiconductor) ; Embedding ; Representation (politics) ; Gaussian process ; Feature (linguistics) Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Mostafa Sadeghian | A. Palevičius | Paulius Griškevičius | Giedrius Janusas
Source: Applied Sciences (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Machine Learning Applications in the Mechanical Analysis of Nanomaterials and Nanostructures Machine learning (ML) is increasingly used to address the computational complexity and multiscale nature of mechanical analysis in nanomaterials and nanostructures. Traditional analytical, numerical, and atomistic approaches, such as continuum mechanics, finite element methods, and molecular dynamics (MD), often suffer from high computational cost or limited scalability when applied to nanoscale systems. Recently, ML techniques have been increasingly used to predict mechanical properties, analyze static and dynamic responses, and solve governing equations of nanostructures to improve efficiency and accuracy. This review provides a comprehensive overview of ML applications in the mechanical analysis of nanomaterials and nanostructures, including mechanical property prediction, static response analysis, and vibration analysis. Various ML techniques based on the property or type of the mechanical problem are discussed in detail. The review highlights current trends and provides structured guidance for future research on reliable and physically consistent ML methods for nanoscale mechanical analysis. Scalability ; Nanomaterials ; Nanostructure ; Finite element method ; Property (philosophy) ; Nanoscopic scale Machine Learning in Materials Science ; Microstructure and mechanical properties ; Material Properties and Failure Mechanisms
Authors: Gianmarco G Terrones | S. Huang | Akash Ball | Changhwan Oh | Matthew P. Rivera | Heather J Kulik
Source: ChemRxiv (article, 2026-03-06)
Field: Chemistry — Inorganic Chemistry

What makes a suitable metal-organic framework linker? Discovery from a general chemical database Metal-organic frameworks (MOFs) have attracted significant interest for their diverse applications and tunable properties derived from their organic linkers and inorganic secondary building units.However, the organic linkers historically used in MOFs constitute only a narrow subset of the synthetically realizable small-to medium-sized organic molecules.In this work, we examine the typical characteristics of linkers in experimentally characterized MOFs, and we investigate the potential of the largest open-access chemical database, PubChem, as a source of organic molecules that could be incorporated into MOFs as linkers.Based on the analysis of connectivity, rigidity, and geometry of linkers present in the CoRE MOF (Computation-Ready, Experimental metal-organic framework) database, we apply a series of filters to PubChem molecules to identify linker candidates that we incorporate into hypothetical MOFs.We then predict the stabilities of a representative subset of hypothetical MOFs with machine learning models.From the most stable hypothetical MOFs, we identify commonly occurring PubChemderived linkers, and we identify design rules for multivariate stability such as the importance of moderate steric bulk.By demonstrating a workflow for identifying potential MOF linkers from chemical databases and by disseminating our candidate linkers and hypothetical MOFs, we expect this work will facilitate the discovery of new linkers and the creation of new MOFs with tailored properties. Key (lock) ; Relational database ; Identification (biology) ; Feature (linguistics) Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection
Authors: K. Venkateswara Reddy | Suresh Gudala | Rajasekhar Thota | Lava Kumar P | Dhiraj Raj | Ramesh Ganugapenta
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predictive modelling of compressive strength in silicon nitride-reinforced aluminium composites using supervised machine learning: A comparative study of random forest and artificial neural networks The integration of machine learning and material science is now changing the way property predictions are made, accelerating the process of discovery and optimization of advanced materials. For this research, we employed a supervised ML to estimate the compressive strength of AMCs reinforced by silicon nitride. To sharpen predictive accuracy, hyperparameter tuning using GridSearch CV was performed. We employed two different algorithms-RF and ANN-to unravel the complex links between the inputs, such as compaction pressure, reinforcement content, sintering temperature, and sintering time with the target strength. Regularization was used to guard against overfitting, and training versus testing performance was compared rigorously. The results indicated that the RF outperformed the ANN model, giving an R² of 0.88, while the ANN reached an R² of only 0.80. These results suggested that the RF model had tighter residuals and higher accuracy since it emphasizes the parameters that drive most of the variance in the dataset. This work illustrates the potential of ML, especially RF, in reliably predicting properties of materials and informing the design of high-performance composites. Hyperparameter ; Random forest ; Artificial neural network ; Compressive strength ; Compaction ; Regularization (linguistics) ; Aluminium ; Sintering ; Random search Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis ; Machine Learning in Materials Science
Authors: Tamoghna Das
Source: Research Square (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

An information theoretic measure of softness in liquids Measure (data warehouse) ; Deformation (meteorology) ; Variable (mathematics) ; Characterization (materials science) ; Field (mathematics) ; Stress (linguistics) ; Complement (music) ; Transformation (genetics) Machine Learning in Materials Science ; Theoretical and Computational Physics ; Material Dynamics and Properties