jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shunya Oishi | Naoya Ohtsuka | Rio MARUYAMA | Elisa Margarita Mendoza Zamarripa | Mitsuru IRIE | Nobuyuki Mase | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Chemical Reactivity Mapping: Exploring Chemical Reaction based on Digitalization of Small Molecules through Mayr’s Parameter A new strategy for exploring novel reactions, utilizing Mayr’s reactivity parameters, is developed. We create a highly accurate prediction model of reactivity parameters using machine learning. Validating the reactivity scales in the reactions of isoquinoline and predicting the reactivity in commercially available chemicals lead to a new approach for exploring chemical reactions. Incorporating Mayr's reactivity parameters in both chemical reactivity mapping and chemical reaction space contributes to expedite the exploitation of reactions in small molecule syntheses. Small molecule ; Reactivity (psychology) ; Chemical reaction ; Molecule ; Reaction mechanism ; Yield (engineering) Machine Learning in Materials Science ; Various Chemistry Research Topics ; History and advancements in chemistry
Authors: Changbin Wang | Rui Liu | Yu Cui | Fuhui Wang | Li Liu
Source: Corrosion Science (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

A deep learning approach integrating corrosion priors for forecasting low-alloy steel corrosion rates Corrosion ; Generalization ; Deep learning ; Prior probability ; Transformer ; Predictive modelling ; Artificial neural network Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Concrete Corrosion and Durability
Authors: Sangro Lee | Chunjoong Kim | Hyun Woo Kim
Source: ChemRxiv (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Quantum-inspired Inverse Design of Heterogeneous Catalysts for Hydrogen Evolution Reaction Data-driven discovery of stable and functional materials remains a major challenge in chemistry and materials science, both from classical and quantum perspectives. Quantum machine learning provides new opportunities to optimize materials across vast chemical spaces, but hardware limitations restrict its practical application. Classical models can process large datasets, but limitations in their latent representations prevent exploration beyond the training distribution. To address these challenges, we introduce a quantum-inspired generative framework for discovering catalytic materials for hydrogen evolution reactions (HER). Inspired by variational quantum circuits (VQCs), which outperform classical models in certain tasks, we hypothesized that the norm-conserving property of VQCs plays a key role in materials representation and optimization. Thus, we incorporated the orthogonal transformations into the classical generative model to maintain latent space structure and enhance materials discovery. With additional data through transfer learning, the classical model with orthogonal transformations becomes useful. Our results provide practical guidelines for applying quantum machine learning to materials discovery and suggest the potential of quantum-inspired generative models to explore previously inaccessible chemical spaces. Generative grammar ; Representation (politics) ; Quantum ; Process (computing) ; Property (philosophy) ; Generative model ; Inverse ; Chemical space Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Junmin Chen | Qian Gao (146991) | Yange Lin | Miaofei Huang | Zheng Cheng (426625) | Wei Feng (225281) | Jing Huang | Bo Wang (86769) | Kuang Yu (1494592)
Source: Figshare (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

A Hybrid Physics-DrivenNeural Network Force Fieldfor Liquid Electrolytes Electrolyte design plays an important role in the development of lithium–ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of different solvents, additives, and salts, which is difficult to explore experimentally. High-fidelity molecular simulation can accurately predict the bulk properties of electrolytes by employing accurate potential energy surfaces, thus guiding the molecule and formula engineering. At present, the overly simplified classic force fields rely heavily on experimental data for fine-tuning, thus its predictive power on microscopic level is under question. In contrast, the newly emerged machine learning interatomic potential (MLIP) can accurately reproduce the ab initio data, demonstrating excellent fitting ability. However, it is still haunted by problems such as low transferability, insufficient stability in the prediction of bulk properties, and poor training cost scaling. Therefore, it cannot yet be used as a robust and universal tool for the exploration of electrolyte design space. In this work, we introduce a highly scalable and fully bottom-up force field construction strategy called PhyNEO-Electrolyte. It adopts a hybrid physics-driven and data-driven method that relies only on monomer and dimer EDA (energy decomposition analysis) data. With a careful separation of long/short-range and nonbonding/bonding interactions, we rigorously restore the long-range asymptotic behavior, which is critical in the description of electrolyte systems. Through this approach, we significantly improve the data efficiency of MLIP training, allowing us to achieve much larger chemical space coverage using much less data while retaining reliable quantitative prediction power in bulk phase calculations. PhyNEO-Electrolyte thus serves as an important tool for future electrolyte optimization. Electrolyte ; Battery (electricity) ; Stability (learning theory) ; Field (mathematics) ; Ab initio ; Power (physics) ; Artificial neural network ; Force field (fiction) ; Set (abstract data type) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Block Copolymer Self-Assembly
Authors: Nikoo Ghanadan | Valentina Rein | Hui Gao | Egor Bersenev | Thomas Sarrazin | M. Sodhi | I. Snigireva | N. Gauthier | Anastasios C. Manikas | Costas Galiotis | Maciej Jankowski | Gilles Renaud | A. Dimoulas | Karsten Reuter | Oleg Konovalov | Hendrik H. Heenen | Irene M. N. Groot
Source: ChemRxiv (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Hexagonal Boron Nitride on Liquid and Single-Crystal Copper: Operando X-Ray and Atomistic Insights into Growth and Interfacial Structure Two-dimensional (2D) hexagonal boron nitride (hBN) is a key dielectric for van der Waals nanoelectronics, however, its controlled synthesis by chemical vapor deposition remains challenging and poorly understood. In this context, the growth of hBN on liquid metal catalysts is promising, as the atomically flat liquid surfaces are assumed to promote high-quality 2D growth, an expectation largely informed by graphene. Here, we implement an involved operando methodology to monitor and quantify hBN growth on molten copper (Liq-Cu) and re-solidified single-crystal copper (SC-Cu) under near-ambient-pressure conditions, enabling real-time identification of growth stages, morphology, and interfacial structure. Contrary to expectation, Liq-Cu promotes multilayer and three-dimensional domain formation, whereas SC-Cu predominantly yields monolayer-limited growth. This substrate-phase dependence correlates with a larger adsorption height of hBN on Liq-Cu than on SC-Cu, as determined by X-ray reflectivity and supported by machine-learning-accelerated molecular dynamics simulations. Direct comparison with graphene on Cu further reveals a distinct directional bonding character at the hBN/Cu interface, which rationalizes the observed adsorption-height trends. More generally, these trends across 2D materials and substrates identify the resulting interfacial stabilization – together with macroscopic factors such as precursor solubility – as a key design parameter governing mono- versus multilayer growth in 2D material synthesis. Hexagonal boron nitride ; van der Waals force ; Chemical vapor deposition ; Copper ; Graphene ; Adsorption ; Metal ; Boron nitride Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials
Authors: Haoyu Zhu | Rongling Gu | Shijie Du | Lu Xu | Qin Yang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A MATLAB Toolbox for 3D Chemical Field Data Classification using PointNet++ Toolbox ; Point cloud ; Discriminative model ; Field (mathematics) ; Pattern recognition (psychology) ; MATLAB ; Point (geometry) ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jean Lu | Shengfeng Yang
Source: arXiv (Cornell University) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale Predicting atomic-scale crack propagation in aluminum nitride (AlN) is critical for semiconductor reliability but remains prohibitively expensive via molecular dynamics (MD). We develop a diffusion-based generative machine learning model to predict atomic-scale crack propagation in AlN, a critical semiconductor material, by conditioning solely on initial microstructure embeddings. Trained on MD simulations of single-crack systems, the model achieves a significant speedup while accurately forecasting dynamic fracture processes, including stress-driven crack initiation, crack branching, and atomic-scale bridging ligaments. Crucially, it demonstrates inherent physical fidelity by reproducing material-intrinsic mechanisms while disregarding periodic boundary artifacts, and generalizes to unseen multi-crack configurations. Validation against MD ground truth confirms the capability of the model to capture complex fracture physics without auxiliary stress or energy data, enabling rapid exploration of crack-mediated failure for semiconductor reliability optimization. Fracture mechanics ; Bridging (networking) ; Reliability (semiconductor) ; Multiscale modeling ; Generative grammar ; High fidelity ; Semiconductor ; Generative model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advancements in Semiconductor Devices and Circuit Design
Authors: Gereon Feldmann | Pit Steinbach | Christoph Bannwarth
Source: ChemRxiv (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

A machine learning model for nuclear Hessians based on GFN2-xTB-derived atomistic features The determination of vibrational frequencies is by now indispensable in computational chemistry. They build a cornerstone in quantum chemistry for the computation of thermodynamic corrections and vibrational spectra. Determining the harmonic vibrational frequencies, however, requires computing the nuclear Hessian matrix, which is significantly more costly than evaluating the electronic energy or its first derivatives (forces). The Hessian is thus a valuable target for prediction by machine learning models, as this promises a significant reduction in the computational cost. In this work, a physically motivated machine learning model is presented for the prediction of Hessian matrices. Features from the semi-empirical method GFN2-xTB are used to predict block matrices of the corresponding Hessian matrix using a feedforward neural network. Only one single-point calculation with the efficient GFN2-xTB method is necessary for the generation of features, which reduces the cost for the Hessian significantly. The model is fitted to reproduce GFN2-xTB Hessian matrices on a data set that covers the complete periodic table up to radon. To verify its performance, a variety of benchmark sets are evaluated. This study shows that the model can reproduce harmonic oscillator free energies with an error within the chemical accuracy (1 kcal mol−1) when combined with standard partition function approximations. Additionally, single applications are chosen for the computation of broadened line spectra to show the accuracy of the model, e.g., when used for simulation of IR spectra. The model is implemented and published is an open source Python package which can be obtained from https://git.rwth-aachen.de/bannwarthlab/hessian_ml. Hessian matrix ; Computation ; Python (programming language) ; Artificial neural network ; MNIST database ; Benchmark (surveying) ; Quasi-Newton method Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies
Authors: Alberto Torres | Alan de Oliveira | Matheus dos S. Barbosa | Leonardo Villegas Lelovsky | Valdirene Gonzaga de Resende | Flávio de Castro Dutra | Felipe Viana Pimenta | Fabrício Vilela Parreira | Amaury de Melo Souza | Matheus J. S. Matos | Alexandre Reily Rocha
Source: Scientific Reports (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for the structural properties of iron oxides Iron oxides constitute an important class of materials, exhibiting a rich and intricate range of behaviors. Despite their significance, their structural and mechanical properties, particularly Hematite ($$\alpha$$-Fe$$\phantom{0}_2$$O$$\phantom{0}_3$$), have been scarcely investigated in the literature from a theoretical standpoint. At the same time, recent developments in machine learning for interatomic potentials have revolutionized computational materials science by enabling highly accurate and efficient simulations of atomic interactions. Traditional methods, such as density functional theory (DFT) and classical force fields, often struggle with high computational costs or lack the flexibility to generalize across diverse chemical environments. ML-based approaches, have emerged as powerful alternatives, learning complex potential energy surfaces from quantum-mechanical data. These models can achieve DFT accuracy at a fraction of the computational cost, facilitating large-scale molecular dynamics (MD) simulations. In this work, we present a graph neural network interatomic potential for hematite. The model was trained on datasets generated from DFT+U calculations to account for strong electronic correlations, using atomic configurations sampled across a wide range of temperatures and pressures. Our potential accurately reproduces fundamental material properties, including the elastic moduli, anisotropic elastic constants, vibrational frequencies, and surface energies. Furthermore, we demonstrate its transferability to other bulk iron oxides. This work enables large-scale molecular dynamics (MD) simulations of iron-based materials with ab initio accuracy at a computational cost comparable to that of classical potentials, opening new opportunities for investigating these complex systems. Interatomic potential ; Density functional theory ; Molecular dynamics ; Transferability ; Ab initio ; Anisotropy ; Computational model ; Range (aeronautics) ; Work (physics) Machine Learning in Materials Science ; Iron oxide chemistry and applications ; X-ray Diffraction in Crystallography
Authors: Qiuyi Fu | Hao Yuan | H. Wang | Wenbin Liu | Guobing Zhou | Zhen Yang
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Simulations of Coordination and Diffusion Behaviors in Lithiated Gallium Electrode Molecular dynamics ; Gallium ; Diffusion ; Electrode ; Dynamics (music) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Rahul D. Jawarkar | Prashant K. Deshmukh | Bhavesh Mandwale | Long Chaiou Ming
Source: Artificial Intelligence Chemistry (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

From potential to practice: The prospective and pitfalls of generative AI and deep learning in molecular simulations Deep learning ; Generative grammar ; Artificial neural network ; Generative model ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Harish Sivasankaran | Sridharan Mohan
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Framework for Predicting Thermal Conductivity of Graphene - Enhanced Phase Change Materials Thermal conductivity ; Graphene ; Phase change ; Phase (matter) ; Thermal ; Support vector machine Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Amala Akkiraju | Athanassios Z. Panagiotopoulos
Source: The Journal of Chemical Physics (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Predicting copolymer critical parameters with a theory-integrated neural network The phase behavior of polymer solutions is essential for designing materials with targeted properties, but classical theories such as Flory-Huggins are limited by mean-field assumptions and often fail to capture sequence-specific effects. Here, we develop a machine learning framework to predict the critical temperature and critical volume fraction of copolymer sequences, combining neural networks (NNs) with physically motivated scaling relations in a theory-integrated neural network (TI-NN). Using grand canonical Monte Carlo simulations of 3351 model copolymer sequences in solvents of varying selectivity across diverse architectures, we show that a standard NN achieves reasonable accuracy, while a TI-NN significantly reduces prediction errors and enables robust extrapolation beyond the training set. Feature analysis reveals that solvent selectivity and sequence blockiness are the dominant determinants of copolymer critical parameters. Overall, our work demonstrates that embedding theoretical insights within machine learning models enhances both accuracy and interpretability for predictions of copolymer phase behavior. Interpretability ; Extrapolation ; Artificial neural network ; Copolymer ; Monte Carlo method ; Embedding ; Scaling ; Phase (matter) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: He Tao | Shucheng Tian | Jiali Ma | Jiahua Li | Shuaibo Huang | Zehua Wang | Haiping Liu | Binguo Zheng | Jibiao Zhang | Haiyu Tian | Fanshu Kong | Xueqing Li
Source: Carbon Neutral Technologies (article, 2026-03-29)
Field: Chemistry — Inorganic Chemistry

Machine learning-driven design of CO2 capture materials: A focus on progress and challenges for MOFs and ILs Focus (optics) ; Work (physics) ; Key (lock) ; Process (computing) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Aakash Ashok Naik | Nidal Dhamrait | Katharina Ueltzen | Christina Ertural | Philipp Benner | Gian-Marco Rignanese | Janine George
Source: ArXiv.org (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

A critical assessment of bonding descriptors for predicting materials properties Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuable concept for predicting materials properties. Over the years, various theoretical frameworks have been developed to characterize bonding in solid-state materials. However, integrating bonding information from these frameworks into machine learning pipelines at scale has been limited by the lack of a systematically generated and validated database. Recent advances in high-throughput bonding analysis workflows have addressed this issue, and our previously computed Quantum-Chemical Bonding Database for Solid-State Materials was extended to include approximately 13,000 materials. This database is then used to derive a new set of quantum-chemical bonding descriptors. A systematic assessment is performed using statistical significance tests to evaluate how the inclusion of these descriptors influences the performance of machine-learning models that otherwise rely solely on structure- and composition-derived features. Models are built to predict elastic, vibrational, and thermodynamic properties typically associated with chemical bonding in materials. The results demonstrate that incorporating quantum-chemical bonding descriptors not only improves predictive performance but also helps identify intuitive expressions for properties such as the projected force constant and lattice thermal conductivity via symbolic regression. Workflow ; Set (abstract data type) ; Support vector machine ; Material properties ; Characterization (materials science) ; Thermal conductivity ; Scale (ratio) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Computational Drug Discovery Methods
Authors: Xin Zhang | Hongwei Yang | Wu Li | Juan Du
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Decoding physical mechanisms governing elastic moduli in inorganic materials through interpretable machine learning Interpretable ML with 34 key features predicts shear ( G ) and bulk ( K ) moduli ( R 2 = 0.892/0.949), reveals the physical origins, and uncovers synergistic and compensatory feature interactions for rational elastic property design of inorganic materials. Property (philosophy) ; Moduli ; Decoding methods ; Feature (linguistics) ; Elastic modulus ; Shear (geology) Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Material Selection and Properties
Authors: B. S. Sindu | Jan Hamaekers
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers Epoxy polymers are widely used due to their multifunctional properties, but machine learning (ML) applications remain limited owing to their complex 3D molecular structure, multi-component nature, and lack of curated datasets. Existing ML studies are largely restricted to simulation data, specific properties, or narrow constituent ranges. To address these limitations, we developed an informed Gaussian Process Regression-based Knowledge Distillation (GPR-KD) framework for predicting multiple physical (glass transition temperature, density) and mechanical properties (elastic modulus, tensile strength, compressive strength, flexural strength, fracture energy, adhesive strength) of thermoset epoxy polymers. The model was trained on experimental literature data covering diverse monomer classes (9 resins, 40 hardeners). Individual GPR models serve as teacher models capturing nonlinear feature-property relationships, while a unified neural network student model learns distilled knowledge across all properties simultaneously. By encoding the target property as an input feature, the student model leverages cross-property correlations. Molecular-level descriptors extracted from SMILES representations using RDKit create a physics-informed model. The framework combines GPR interpretability and robustness with deep learning scalability and generalization. Comparative analysis demonstrates superior prediction accuracy over conventional ML models. Simultaneous multi-property prediction further improves accuracy through information sharing across correlated properties. The proposed framework enables accelerated design of novel epoxy polymers with tailored properties. Epoxy ; Interpretability ; Scalability ; Robustness (evolution) ; Gaussian process ; Artificial neural network ; Nonlinear system ; Deep learning ; Multiscale modeling Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Polymer crystallization and properties