jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md Omar Faruque | Dil K. Limbu | Nathan London | Mohammad R. Momeni
Source: The Journal of Physical Chemistry Letters (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Predictive Quantum Vibrational Spectra through Active Learning 4G-NNPs Predictive simulation of vibrational spectra of complex condensed-phase and interface systems with thousands of degrees of freedom has long been a challenging task of modern condensed matter theory. In this work, fourth-generation high-dimensional committee neural network potentials (4G-HDCNNPs) are developed using active learning and query-by-committee, and introduced to the essential nuclear quantum effects (NQEs) as well as conformational entropy and anharmonicities from path integral (PI) molecular dynamics simulations. Using representative bulk water and air-water interface test cases, we demonstrate the accuracy of the developed framework in infrared spectral simulations. Specifically, by seamlessly integrating nonlocal charge transfer effects from 4G-HDCNNPs with the NQEs from PI methods, our introduced methodology yields accurate infrared spectra using predicted charges from the 4G-HDCNNP architecture without explicit training of dipole moments. The framework introduced in this work is simple and general, offering a practical paradigm for predictive spectral simulations of complex condensed phases and interfaces, free from empirical parametrizations and ad hoc fitting. Dipole ; Quantum ; Spectral line ; Artificial neural network ; Interface (matter) ; Molecular dynamics ; Infrared ; Charge (physics) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Di Sheng Lee | Bomi Park | Sergio Salgado | James Dolgin | David L. Kaplan
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-02-04)
Field: Materials Science — Biomaterials

Computational design of blue melanin with peptide motif scaffolding Abstract De novo melanin design seeks to extend natural melanin colors to new, stable colors (blue, purple, green) with sequence-to-color tunability. Natural melanin, polymerized from tyrosine (Y), is a robust pigment with heterogenous molecular weights. Control of melanin size (length) is challenging; thus, only specific colors (yellow to brown) exist in nature. In this work, we describe the design of blue melanin through the polymerization of Y-containing pentapeptides with two key properties: tight packing during peptide assembly and high solubility in aqueous environments. By motif scaffolding a pentapeptide-repeat protein (PRP) with RFdiffusion, we narrowed 160,000 possible combinations to a library of 905 Y-containing pentapeptides with tight packing features. Two of the most soluble designs successfully formed stable blue melanin with λ max absorbing in 615-620 nm, contributed by homogeneous melanin length achieved around 60 Y units. Other designs also formed new colors (purple, green), along with more known colors (red, yellow, brown). We found that blue melanin exhibited thermal stability at an autoclave temperature of 121°C and photostability of weeks under 600 lux illumination. We also demonstrated the application of blue melanin as an electrophoretic ink. De novo color design from simple peptides could potentially transform how colorants are sourced and produced. Our approach with computational design should also inspire the development of new deep-learning tools to directly predict colors from amino acid sequences. Melanin ; Homogeneous ; Polymerization ; Peptide ; Scaffold ; Amino acid ; Pigment ; Tyrosine Supramolecular Self-Assembly in Materials ; Silk-based biomaterials and applications ; melanin and skin pigmentation
Authors: Amin Reiners-Sakic | Ronald Schnitzer | David Holec
Source: npj Computational Materials (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

DFT and MLIP study of solute segregation to coherent and semi-coherent α-Fe/Fe3C interfaces Abstract Solute segregation to interfaces strongly affects material behavior. Most theoretical studies focus on grain boundaries and coherent interfaces, whereas semi-coherent interfaces remain largely unexplored due to structural complexity exceeding the practical capability of density functional theory (DFT) or chemical complexity constrained by the availability of classical interatomic potentials. Here, we investigate solute segregation to the coherent and semi-coherent α -Fe/Fe 3 C interface and its mechanical impact using novel universal machine-learning interatomic potentials (uMLIPs). DFT calculated solution enthalpies, segregation energetics, and cohesion changes at the coherent interface serve to benchmark several state-of-the-art uMLIPs, identifying GRACE-2L-OAM and GRACE-2L-OMAT as most consistent with quantum-mechanical predictions. Among the studied tramp and trace elements, Cu shows the strongest segregation energy to the coherent interface of ≈ − 0.3 eV. However, at the semi-coherent interface, all elements exhibit significantly stronger segregation energetics, reaching below ≈ − 1.5 eV, with the deepest traps near the misfit dislocation core. Sb, Sn, P, and As strongly reduce the coherent interface cohesion, Cu mildly, while Ni has a negligible influence, and Cr and Mo slightly enhance cohesion. At the semi-coherent interface, all solutes except P promote embrittlement, with Sn and Sb showing the strongest effect. These results underscore the importance of realistic interface structures for predictive materials design. Density functional theory ; Grain boundary ; Interface (matter) ; Interatomic potential ; Dislocation ; Cohesion (chemistry) Machine Learning in Materials Science ; Microstructure and mechanical properties ; High Temperature Alloys and Creep
Authors: Kishore Kumar Venkatesan | Sathiyan Samikannu
Source: Springer Link (Chiba Institute of Technology) (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Intelligent Data-Driven Modeling of SARS-CoV-2 Interactions in BP–MXene–BP Heterostructure SPR Biosensors using Ridge Regression Model The global SARS-CoV-2 pandemic has emphasized the urgent need for rapid, accurate, and scalable diagnostic technologies suitable for widespread screening. Conventional laboratory methods such as RT-PCR and ELISA, although reliable, suffer from long turnaround times, high operational cost, and dependence on specialized personnel, limiting their applicability in resource-constrained environments. Surface Plasmon Resonance (SPR) biosensors have emerged as promising alternatives, offering real-time, label-free molecular detection with high sensitivity and specificity. Recent advances highlight that integrating 2D nanomaterials—particularly BP/MXene multilayer heterostructures—significantly enhances plasmonic field confinement, signal strength, sensitivity, and detection accuracy compared to traditional metal-only configurations. However, modeling and optimizing such advanced SPR architectures typically depend on computationally intensive analytical methods, such as the Trans- fer Matrix Method and Fresnel formulations, which rely on idealized material parameters and are difficult to scale for real-time optimization. To address these limitations, this work introduces an intelligent machine- learning-based prediction framework for CaF2/Ag/BP/MXene/BP SPR biosensors using regression models to learn nonlinear relationships between structural parameters and performance metrics, including sensitivity and resonance wavelength. The proposed data-driven approach enables faster and more accurate performance estimation without exhaustive simulations, supporting rapid optimization across diverse operating scenarios. By combining plasmonic nanostructures with AI- assisted predictive modeling, this study establishes a foundation for intelligent, self-optimizing SPR diagnostic platforms suitable for next-generation biomedical applications. Surface plasmon resonance ; Sensitivity (control systems) ; Scalability ; Benchmark (surveying) ; Biosensor ; Field (mathematics) ; Plasmon ; Nonlinear system MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Molecular Communication and Nanonetworks
Authors: Chuitao Zeng | Shanshan Cao | Wubin Wan | Xuwen Yuan | Tianguo Wang | Cheng Luo | Kailing Zhou | Yuhong Jin | Hao Wang
Source: Small (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Surface/Interface Design Strategies for Highly Efficient Electrocatalysts: Progress and Perspectives Electrocatalysts play a critical role in electrochemical energy storage and conversion technologies and have been extensively researched. However, the trade-off between activity and stability design of electrocatalysts under industrial high-current and corrosive working conditions is still urgently required to be addressed for practical applications. This review summarizes the latest progress and perspectives of electrocatalysts, with particular emphasis on design strategies based on the surface/interface microstructures for highly efficient electrocatalysts. It also focuses on the intrinsic relationship between structures and electrochemical behaviors, the importance of surfaces/interfaces for reducing reaction barrier and improving reaction efficiency, and the inherent principles and factors for optimizing the electrocatalytic reaction process. Finally, this review proposes the integration of artificial intelligence and machine learning based electrocatalytic properties prediction, external fields-assisted electrocatalysts preparation and microenvironment modulation-regulated electrocatalytic reaction processes for future electrocatalyst design. This review aims to provide a comprehensive reference for further fundamental research and engineering applications in the electrocatalysis field. Electrocatalyst ; Electrochemical energy storage ; Electrochemistry ; Production (economics) ; Industrial production Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Felipe Hawthorne | Marcelo Lopes Pereira Júnior | Fabiano M. Andrade | Cristiano Francisco Woellner | Raphael M. Tromer
Source: ArXiv.org (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

CARBON-2D Topological Descriptor (C2DTD): An Interpretable and Physics-Informed Representation for Two-Dimensional Carbon Networks Two-dimensional (2D) carbon networks, from pristine graphene to defect-rich and amorphous monolayers, exhibit a complex structure-energy landscape governed not only by local bonding but also by medium-range order and network topology. Capturing these multi-scale effects in a compact, interpretable, and data-efficient manner remains a major challenge for machine learning (ML) in low-dimensional materials. In this work, we introduce the CARBON-2D Topological Descriptor (C2DTD), a physically informed structural representation specifically designed for 2D carbon systems. The descriptor integrates local geometric statistics, a compact radial structural signature, and explicit primitive ring topology into a fixed-length, invariant vector that is both computationally efficient and directly interpretable. Benchmarking on diverse datasets of 2D carbon allotropes and defect-engineered graphene sheets demonstrates that C2DTD achieves robust predictive performance in small-data regimes, outperforming generic high-dimensional featurization schemes while preserving physical transparency. Unsupervised manifold analysis reveals a smoother alignment between descriptor space and the DFT energy landscape, and feature-importance and ablation studies confirm that ring topology emerges as a dominant energetic driver, particularly under vacancy-induced reconstruction. Furthermore, controlled simulations with 5-15% random vacancies show that C2DTD naturally captures the progressive transition from hexagon-dominated graphene to topologically disordered networks, enabling both dataset-level and structure-specific interpretation. Owing to its compactness, interpretability, and strong physics-based inductive bias, C2DTD provides a fast and generalizable framework for data-driven modeling, defect analysis, and high-throughput screening of 2D carbon materials. Topology (electrical circuits) ; Representation (politics) ; Manifold (fluid mechanics) ; Graphene ; Invariant (physics) ; Vector space ; Ring (chemistry) ; Space (punctuation) Graphene research and applications ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Pavlo Prysyazhnyuk
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-04-05)
Field: Materials Science — Materials Chemistry

Spectral Functionals of the Elastic Stiffness Tensor as a Physically Grounded Basis for Hardness Prediction and Directed Crystal Structure Search We propose and systematically compare ten physically motivated hardness descriptors based exclusively on the six eigenvalues (λ₁ ≤ λ₂ ≤ ··· ≤ λ₆) of the elastic stiffness tensor in Voigt notation. The models are trained on a curated 45-material dataset using elastic tensors from the GRACE-2L machine-learning interatomic potential and experimental Vickers hardness traced to primary sources. The best model, CV-λ (HV = 0.123·λ⁰·⁸⁷⁴harm·CV⁻⁰·⁹²⁶), achieves R² = 0.911 and MAE = 3.91 GPa, outperforming the classical Teter (R² = 0.818), Tian (R² = 0.878), and Mazhnik–Oganov (R² = 0.856) models. Cross-validation confirms robust generalisation (CV R² = 0.826). All anisotropy descriptors are near-equivalent (r(CV, 1−PR) = 1.000). A blind DFT transferability test shows GRACE-based predictions systematically outperform DFT-based ones. Beyond hardness prediction, we demonstrate that the eigenvectors of the stiffness tensor define physically motivated search directions for crystal structure exploration: preliminary application to the Nb–B system via eigenvalue directed exploration (EDE) recovers 3 of 6 known experimental phases with exact space group symmetry, identifies hard candidates overlooked by energy-driven evolutionary search (USPEX), and recovers 4 of 5 OQMD reference phases. Tensor (intrinsic definition) ; Eigenvalues and eigenvectors ; Stiffness ; Anisotropy ; Basis (linear algebra) ; Space (punctuation) Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Rare-earth and actinide compounds
Authors: Philipp Fleck | Christian Haider | Jan Zenisek | Wolfgang Rannetbauer | Carina Hambrock
Source: Procedia Computer Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Transfer Learning for Interpretable Prediction of HVOF Coating Properties High velocity oxygen fuel (HVOF) spraying is used to create protective coating layers on workpieces to achieve specific surface properties. Given the process parameters and a fixed coating material, the resulting coating properties can be accurately predicted using machine learning algorithms. However, since the coating material strongly influences the outcome, it is unclear how well a model trained on one specific coating material can predict properties for a different material. In this work, we demonstrate that significant differences in the marginal distributions between coating materials make direct application of existing models challenging. We present several transfer learning approaches to adapt existing models to new coating materials. Our results show that simple methods, such as linear residual models, are already sufficient to achieve accurate adaptation, drastically reducing the experimental effort for new materials and preserving knowledge from the original model. Furthermore, for symbolic regression models, we show that re-optimizing numeric coefficients is an effective and computationally efficient strategy that also maintains model interpretability. Coating ; Process (computing) ; Transfer of learning ; Thermal spraying ; Residual ; Linear regression ; Simple (philosophy) ; Transfer (computing) Machine Learning in Materials Science ; High-Temperature Coating Behaviors ; Nuclear Materials and Properties
Authors: Tian-Hao Yang | Jing-Han Wu | Ming-Ming Ding | Wen Zhai | Ke Wang | Qiang Fu | Yang Liu
Source: Chinese Journal of Polymer Science (article, 2026-01-04)
Field: Materials Science — Polymers and Plastics

Simulation of the Specific Contributions of Molecular Weight, Orientation Degree, and Crystallinity to the Tensile Mechanics of Polyethylene Fibers Crystallinity ; Ultimate tensile strength ; Ultra-high-molecular-weight polyethylene ; Molecular dynamics ; Modulus ; Polyethylene ; Young's modulus Polymer crystallization and properties ; Tribology and Wear Analysis ; Fiber-reinforced polymer composites
Authors: Brian H. Lee | Chunyu Li | Aidan D. Pantoya | James P. Larentzos | J. William Brennan | Alejandro Strachan
Source: arXiv (Cornell University) (preprint, 2026-01-05)
Field: Materials Science — Materials Chemistry

Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net Mapping microstructure to properties is central to materials science. Perhaps most famously, the Hall-Petch relationship relates average grain size to strength. More challenging has been deriving relationships for properties that depend on subtle microstructural features and not average properties. One such example is the initiation of energetic materials under dynamical loading, dominated by energy localization on microstructural features such as pores, cracks, and interfaces. We propose a conditional convolutional neural network to predict the shock-induced temperature field as a function of shock strength, for a wide range of microstructures, and obtained via two different simulation methods. The proposed model, denoted MISTnet2, significantly extends prior work that was limited to a single shock strength, model, and type of microstructure. MISTnet2 can contribute to bridging atomistics with coarse-grain simulations and enable first principles predictions of detonation initiation and safety of this class of materials. Bridging (networking) ; Shock (circulatory) ; Detonation ; Work (physics) ; Function (biology) ; Energetic material ; Field (mathematics) Machine Learning in Materials Science ; Energetic Materials and Combustion ; High-pressure geophysics and materials
Authors: Haoyu Zhou | Ping Xue | Hao Zhang | Tianfan Fu
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Preserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs Equivariant Graph Neural Networks (GNNs) are essential for physically consistent molecular simulations but suffer from high computational costs and memory bottlenecks, especially with high-order representations. While low-bit quantization offers a solution, applying it naively to rotation-sensitive features destroys the SO(3)-equivariant structure, leading to significant errors and violations of conservation laws. To address this issue, in this work, we propose a Geometric-Aware Quantization (GAQ) framework that compresses and accelerates equivariant models while rigorously preserving continuous symmetry in discrete spaces. Our approach introduces three key contributions: (1) a Magnitude-Direction Decoupled Quantization (MDDQ) scheme that separates invariant lengths from equivariant orientations to maintain geometric fidelity; (2) a symmetry-aware training strategy that treats scalar and vector features with distinct quantization schedules; and (3) a robust attention normalization mechanism to stabilize gradients in low-bit regimes. Experiments on the rMD17 benchmark demonstrate that our W4A8 models match the accuracy of FP32 baselines (9.31 meV vs. 23.20 meV) while reducing Local Equivariance Error (LEE) by over 30x compared to naive quantization. On consumer hardware, GAQ achieves 2.39x inference speedup and 4x memory reduction, enabling stable, energy-conserving molecular dynamics simulations for nanosecond timescales. Equivariant map ; Quantization (signal processing) ; Speedup ; Vector quantization ; Inference ; Invariant (physics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Polyxeni Vourna | Pinelopi P. Falara | Aphrodite Ktena | Evangelos Hristoforou | Nikolaos D. Papadopoulos
Source: Metals (article, 2026-03-21)
Field: Materials Science — Metals and Alloys

Advances in Magnetic and Electrochemical Techniques for Monitoring Corrosion and Microstructural Degradation in Steels Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Electrochemical noise ; Corrosion ; Dielectric spectroscopy ; Polarization (electrochemistry) ; Eddy current ; Corrosion monitoring ; Barkhausen effect ; Magnetic field ; Passivation Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Xinyu Deng | Jinzhe Cao | Shengyang Tao
Source: The Journal of Physical Chemistry Letters (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Space Series: Leveraging Geometric Features of Heterostructures for Cross-Composition Property Prediction of Core–Shell Quantum Dots The spatial arrangement of compositions in functional materials plays a pivotal role in modulating their properties. While various representations exist for predicting the benchmark properties of constituent compositions, geometric factors like heterogeneity beyond the atomic scale, which are crucial to the overall behavior of the material, are not encoded explicitly, leading to poor generalization to materials with scarce training data. In this work, we introduce a novel representation, called a space series, which explicitly encodes the geometric features of heterostructures. As a proof of concept, we apply this approach to the property prediction of core-shell quantum dots (CSQDs). Our model, designed to generate and leverage these geometric-compositional sequences, outperforms traditional machine learning methods in terms of generalization across different data sets. Further analysis reveals that such sequences enable the model to effectively capture spatial dependencies within the system, offering a novel pathway for incorporating geometric factors into functional material predictions. Generalization ; Leverage (statistics) ; Property (philosophy) ; Space (punctuation) ; Quantum ; Quantum dot Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications