jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A Jaelani | Muhammad Aziz Majidi | R. Q. Zhang | Yanoar Pribadi Sarwono
Source: International Journal of Quantum Chemistry (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

A Hybrid Convolutional Neural Network and Grid Extrapolation Approach for Predicting Solutions of the Electronic Schrödinger Equation ABSTRACT Solving the many‐electron Schrödinger equation is central to numerous branches of physics, yet its computational complexity often poses challenges. In this work, we explore the integration of deep learning techniques to predict electronic properties for model systems, focusing on two distinct cases: (1) single‐electron properties from three‐dimensional (3D) potential representations, and (2) two‐electron properties of the hydrogen molecule () in six‐dimensional (6D) space. Our 3D model utilizes a deep convolutional neural network architecture to handle cubic potential inputs, while our 6D extension employs a modified architecture for the system. We augment our datasets with random rotations to address rotational invariance. Through comprehensive experiments, we demonstrate the effectiveness of our approach across dimensionalities. For the 6D case, we achieve high accuracy by using the network to predict grid‐dependent energies, which are then refined with a polynomial extrapolation scheme. This hybrid approach highlights deep learning's potential as a powerful accelerator for traditional grid‐based numerical methods in electronic structure calculations. Extrapolation ; Convolutional neural network ; Grid ; Polynomial ; Deep learning ; Artificial neural network ; Representation (politics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Min Chen | Wenhao Pang | Ziyuan Zhao | Ning Cai | Jie Li
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2026-01-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ag nanoparticle-engineered carbon nitrogen as a high performance SERS platform for detecting organic pollutants assisted with machine learning Detection limit ; Plasmon ; Adsorption ; Silver nanoparticle ; Raman scattering ; Nanoparticle ; Analyte ; Pollutant ; Substrate (aquarium) Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications
Authors: Malik Almunif | Jade Le | Anthony Grbic
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Deep Neural Network Design of Reactively Loaded Metasurfaces A tandem deep neural network approach is presented for the inverse design of reactively loaded metasurfaces with prescribed far-field radiation characteristics. The proposed approach integrates a deep neural network (DNN) with a physics-based microwave network forward solver. The DNN maps target far-field patterns to distributions of reactive loads across the metasurface unit cells. The predicted distribution of reactive loads is evaluated by the forward solver to compute the resulting radiation pattern and guide the learning process through a cosine-similarity loss function. The forward solver enables a fast evaluation of the metasurface's electromagnetic response, significantly reducing the computational cost required for training. The proposed approach is applied to a metasurface with aperture-coupled unit cells loaded with reactances. Several design examples are presented to demonstrate the accurate synthesis of shaped and steered radiation patterns. Full-wave electromagnetic simulations are performed to validate the accuracy of the designed beamforming metasurfaces. Solver ; Artificial neural network ; Process (computing) ; Beamforming ; Microwave ; Inverse problem ; Radiation ; Inverse Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Hannah Darr | Ece Deniz | Marina S. Leite
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Nanoscale imaging of local electrical behavior in halide perovskites Abstract Halide perovskites (HPs) exhibit favorable characteristics for photovoltaics and LEDs including optimal optoelectronic properties, bandgap tunability, and defect tolerance. However, these devices are currently limited by their instability and the mechanism behind their degradation is not currently fully understood. Atomic force microscopy (AFM), specifically conductive (c-AFM) and Kelvin-probe force microscopy (KPFM), have been widely utilized to investigate the nanoscale electrical behavior of HPs to further the understanding of the factors driving material and device degradation. In this mini-review, we briefly discuss the operating principles of c-AFM and KPFM and highlight experiments in which these techniques have been employed to investigate the effects of grain boundaries, chemical composition, and environmental factors to the electrical stability of HPs. We also provide an overview of recent applications of machine learning (ML) to automate AFM data extraction and analysis and share a perspective on the opportunities for ML methods in AFM measurements of HPs. Graphical abstract Nanoscopic scale ; Photovoltaics ; Conductive atomic force microscopy ; Atomic force microscopy ; Microscopy ; Halide ; Kelvin probe force microscope ; Electrical conductor Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Hubert Beck | Ondrej Marsalek
Source: arXiv (Cornell University) (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

More converged, less accurate? Reassessing standard choices for ab initio water using machine learning potentials Accurately simulating the properties of liquid water remains a central challenge in molecular simulations. In this work, we use machine learning potentials to investigate how the convergence settings of electronic structure calculations impact the predicted structural and dynamical properties of simulated water and ice. We evaluate the true performance of several reference methods in classical and path-integral molecular dynamics. When we compare a popular, computationally pragmatic revPBE0-D3 setup against a highly converged one, our results reveal that its widely reported experimental agreement degrades. Applying the same highly converged settings to the $\mathrmω$B97X-rV functional, we find an improved agreement with experimental results. MP2 with a triple-$ζ$ basis set commonly used for liquid water shows poor performance, which is indicative of insufficient convergence. These findings underscore the need for fully converged reference calculations when evaluating the fundamental accuracy of electronic structure methods and developing reliable models for aqueous systems. Liquid water ; Convergence (economics) ; Set (abstract data type) ; Ab initio ; Electronic structure ; Reference data ; Water model Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Block Copolymer Self-Assembly
Authors: Chaokun Huang | Xiujuan Wang
Source: Journal of Applied Polymer Science (article, 2026-03-17)
Field: Materials Science — Polymers and Plastics

Study on the Healing Efficiency of Hydrogen‐Bonded Self‐Healing Polyurethane Based on Machine Learning Techniques ABSTRACT To address data scarcity in machine learning‐assisted design of high‐performance polymers, this study develops a novel data‐augmentation strategy. Using hydrogen‐bonded self‐healing polyurethane as a case study, a series of equations describing the self‐healing mechanism were derived based on the self‐healing mechanism of hydrogen‐bonded self‐healing polyurethane. The study generated extensive data by utilizing these equations. The Elastic Net method was employed for feature selection, performing correlation analysis and removing highly correlated features. The selected features were used as inputs for a transformer model to predict the effect of temperature and time on self‐healing efficiency of polyurethanes. The model achieved high accuracy for both linear (R 2 = 0.941, RMSE = 4.972%) and branched (R 2 = 0.948, RMSE = 4.496%) structures. Predictions for individual samples also showed high accuracy, with R 2 = 0.929 and RMSE = 2.746% under isothermal conditions, and R 2 = 0.984 and RMSE = 1.666% under fixed time conditions. These results demonstrate that this study provides a viable solution to the issue of data scarcity in machine learning‐assisted polymer design. The equations and features analyses reveal that chain diffusion and the rate of hydrogen bond formation significantly influence the self‐healing process of hydrogen‐bonded polyurethanes. Mean squared error ; Feature selection ; Isothermal process ; Polyurethane ; Feature (linguistics) ; Polymer ; Scarcity Polymer composites and self-healing ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Adam Coxson | Ömer H. Omar | Marcos del Cueto | Alessandro Troisi
Source: Journal of Chemical Theory and Computation (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Predicting S <sub>1</sub> TDDFT Energies from ZINDO Calculations Using Message-Passing ΔML with Electronically Informed Descriptors We present a machine learning approach (ΔML) capable of enhancing the accuracy of semiempirical excited-state energy calculations to a level close to that of Time-Dependent Density Functional Theory (TDDFT). Using a data set of 7600 organic π-conjugated molecules calculated at the ZINDO and M06-2X/3-21G* TDDFT computational levels, we trained a set of models to learn the systematic errors of the low-level method and correct it toward higher-level accuracy values. The best performing model improved the correlation of ZINDO S<sub>1</sub> energy predictions from 0.77 to 0.96 on a 9500 molecule test set of TDDFT target energies. Our ΔML-ZINDO model presents a negligible additional cost (∼2 ms per molecule) to a standard ZINDO calculation (∼2 s per molecule), enabling the computational screening of large data sets of molecules. Critical to the performance of the model is the AttentiveFP Message-Passing Neural Network with added electronic information derived from ZINDO calculations such as particle-hole densities. We also investigate the utility of the Morgan fingerprint and a novel descriptor designed to capture the electronic structure of molecules: a molecular orbital-weighted radial distribution function. The ΔML framework is retrainable to other low- and high-level calculation pairs, achieving an improvement in correlation from 0.88 to 0.99 on a test set of 24,000 molecules from the QCDGE data set, when mapping ZINDO to ωB97X-D/6-31G* energies. We also adapt ΔML-ZINDO for S<sub>1</sub> oscillator strength prediction, improving ZINDO predictions from a correlation of 0.524 to 0.839 on our M06-2X/3-21G* target test set, thus enabling the identification of emissive molecules. ZINDO ; Time-dependent density functional theory ; Set (abstract data type) ; Test set ; Data set ; Experimental data ; Representation (politics) ; Correlation Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Guan Junlei | Dai Li-jun | Cui‐Liu Fu | Wan-Chen Zhao | Xianbo Huang | Sun Zhao-yan
Source: The Journal of Chemical Physics (article, 2026-01-08)
Field: Materials Science — Polymers and Plastics

Rigidity-driven tail extension controls interfacial thickness in polymer–nanoparticle composites We employ coarse-grained molecular dynamics simulations to investigate interfacial reorganization in polymer-nanoparticle composites, focusing on the competing effects of chain rigidity (Kbend) and attractive strength (ɛ). Geometric constraints create a critical adsorption threshold ɛk. Below this threshold, increasing attraction converts loops and tails into extended trains, improving surface-parallel alignment. Beyond ɛk, saturation causes competitive displacement that fragments trains and reduces orientational order. Machine learning analysis identifies the average tail segment length, ⟨Ltail⟩, as the primary controlling parameter of interfacial thickness δRMS (relative importance >89%). The derived scaling laws describe how rigidity enhances tail extension efficiency. Attractive strength influences thickness indirectly through its effect on ⟨Ltail⟩ within adsorption saturation constraints. These results establish two design principles: using rigidity-controlled tail manipulation for precise thickness tuning and applying ɛk-optimized attraction to maximize adsorption efficiency. This provides concrete guidelines for engineering nanocomposite interfaces. Rigidity (electromagnetism) ; Adsorption ; Saturation (graph theory) ; Scaling ; Molecular dynamics ; Scaling law ; Displacement (psychology) Polymer Nanocomposites and Properties ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Anna Katharina Picha | Johannes Karwounopoulos | Linus C. Erhard | Stefan Boresch | Esther Heid
Source: ChemRxiv (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

GRACE-OFF: A machine-learned interatomic potential for organic liquids using the GRACE architecture Machine-learned interatomic potentials (MLIPs) have become an increasingly important tool for molecular dynamics (MD) simulations, enabling near quantum-mechanical accuracy at significantly reduced computational cost. In this work, we assess the Graph Atomic Cluster Expansion (GRACE) neural network architecture for the prediction of potential energy surfaces. GRACE models of varying depth and size are trained on the SPICE v2.0 dataset—which comprises a large and chemically diverse set of organic molecules—and are integrated into the MD program ASE. We validate the resulting models using a variety of benchmarks. These include single-point energy and force predictions, torsional energy profiles, condensed phase properties of organic liquids and water (thermodynamic properties, self-diffusion coefficients, radial distribution functions, and temperature-dependent water density), as well as the stability of biomolecular MD simulations for gas-phase Ala15 and solvated crambin. For the single molecule benchmarks (single point energies and forces, torsional energy profiles) the one-layer models showed only mediocre performance, whereas the two-layer models outperformed the MACE-OFF models to which we compare to, including the latest MACE-OFF24(M). For the condensed phase properties, in particular the medium and large two-layer models gave significantly better results than the MACE-OFF family of MLIPs. For water and hexane, the GRACE-OFF models also beat the much more expensive UMA(S) model. In particular, the two-layer GRACE-OFF models accurately reproduce experimental water radial distribution functions and predict water densities in close agreement with experimental data over a wide temperature range, substantially reducing the systematic overestimation observed for MACE-OFF. Finally, GPU throughput benchmarks demonstrate that GRACE-OFF achieves substantially higher MD performance than comparable MACE models in both single and double precision. This establishes GRACE-OFF as an accurate and computationally efficient foundation potential for routine simulations of organic liquids and biomolecular systems. Architecture ; Interatomic potential ; Molecular dynamics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Vasiliy S. Krasnikov | Dmitriy S. Voronin | Alexander E. Mayer
Source: International Journal of Plasticity (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Competing mechanisms and pathways of dislocation-precipitate interaction in Al-Cu alloys under dynamic deformation Climb ; Dislocation ; Mechanism (biology) ; Molecular dynamics ; Deformation (meteorology) ; Flow stress ; Stress (linguistics) ; Stress field Microstructure and mechanical properties ; Metallurgy and Material Forming ; High-Velocity Impact and Material Behavior
Authors: Rimlee Dutta
Source: Research Square (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

Machine-learning-guided discovery of defect-engineered ZnO nanostructures for enhanced antibacterial activity Antibacterial activity ; Nanoparticle ; Dopant ; Nanomaterials ; Nanostructure ; Particle size ; Doping ; Antibacterial agent Machine Learning in Materials Science ; Nanoparticles: synthesis and applications ; ZnO doping and properties
Authors: Prarena Jamwal | Rajeev Ahuja | Rakesh Kumar
Source: Nanotechnology (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Two-dimensional superconductivity: a review of computational approaches and emerging phenomena Two-dimensional (2D) materials offer an exceptional platform for exploring quantum phenomena, as their reduced dimensionality significantly enhances tunability via external parameters. Among these, superconductivity in 2D systems is of particular interest due to its fundamental significance and potential applications in quantum technologies. Despite ongoing experimental challenges in realizing novel 2D superconductors, first-principles calculations have emerged as powerful tools for guiding their prediction and design. While many prior reviews focus broadly on low-dimensional superconductivity, this article specifically surveys computationally predicted 2D superconductors, with an emphasis on the underlying theoretical frameworks and their limitations. We highlight how external perturbations such as strain, doping, chemical functionalization, and intercalation, modify electron-phonon coupling and superconducting critical temperatures, and we examine cases where superconductivity competes or coexists with other quantum orders, including charge density waves and nontrivial band topology. We further discuss the growing role of machine-learning and high-throughput approaches in accelerating materials discovery, along with the challenges associated with data quality and model reliability. Overall, this review underscores the potential and current limitations of first-principles and data-driven approaches in advancing the understanding and discovery of 2D superconductors. Superconductivity ; Curse of dimensionality ; Quantum ; Focus (optics) ; Coupling (piping) ; Quality (philosophy) ; Current (fluid) 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Kaustuv Mazumder | Shruti Gupta | Balasubramanian Kandasubramanian
Source: Journal of Polymer Research (article, 2026-04-01)
Field: Materials Science — Polymers and Plastics

Emerging trends in additive manufacturing of shape memory polymer lattices: from properties to applications Shape-memory alloy ; Shape-memory polymer ; 3D printing ; Transformative learning ; Smart material ; Efficient energy use ; Smart polymer Polymer composites and self-healing ; Advanced Materials and Mechanics ; Cellular and Composite Structures
Authors: Hong Han | Haiyuan Bi | Xiao Wang | Ying Liu | Wei Gao | Qiyang Li | Bolun Liu | Shuhong Liu | Lilan Gao | Chunqiu Zhang | Chunyong Liang | Xiaohao Sun | Debao Liu
Source: Virtual and Physical Prototyping (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Unveiling microstructure evolution and governing factors for mechanical and magnetic response in electron beam powder bed fusion processed Zr-based biomedical alloys Zirconium (Zr)-based alloys are promising for biomedical implants due to their excellent biocompatibility, corrosion resistance and low magnetic susceptibility, critical for magnetic resonance imaging (MRI) compatibility. However, the relationship between additive manufacturing parameters, microstructure and functional properties of Zr-based alloys remains underexplored. Herein, Zr -2.5Nb alloy parts were fabricated via electron beam powder bed fusion (EB-PBF) using 40 distinct parameter sets with energy densities ranging from 25 to 230 J·mm−3. The effects of processing parameters on densification, microstructural evolution, mechanical properties and MRI compatibility were systematically investigated. Multiscale and phase-field simulations revealed the mechanisms of melt pool dynamics, thermal history and spinodal decomposition during EB-PBF. A convolutional neural network (CNN) was employed as a supplementary data-analysis approach to reveal the relative influence of coupled microstructural descriptors on magnetic susceptibility. Energy density significantly influences phase composition, microstructure and texture, dictating mechanical strength and magnetic response. Optimal parameters yielded near-full densification, a balanced tensile strength of 621.7 MPa with 23.6% elongation and a 58% reduction in MRI artefact volume compared to Ti -6Al -4V. This work provides a comprehensive framework for tailoring microstructure and multi-performance of the EB-PBF-processed Zr -2.5Nb alloys, advancing their application in the next-generation MRI-compatible implants. Microstructure ; Ultimate tensile strength ; Alloy ; Zirconium ; Fusion ; Spinodal decomposition ; Zirconium alloy ; Porosity Nuclear Materials and Properties ; Additive Manufacturing Materials and Processes ; Titanium Alloys Microstructure and Properties
Authors: Xiaoran Yu | Z. Lin
Source: CSIAM transactions on life sciences. (article, 2026-03-23)
Field: Chemistry — Spectroscopy

Computational Methods for Mass Spectrometry-Based Single-Cell Proteomics Data Mass spectrometry-based single-cell proteomics (MS-SCP) enables the quantification of protein abundance in individual cells, offering a molecular perspective on post-transcriptional regulation and heterogeneity that cannot be inferred from transcriptomic data alone. However, MS-SCP data exhibit high rates of missing values, batch effects, and low-input noise, which require tailored computational models. In this review, we examine computational developments across the MS-SCP pipeline, covering protein identification and quantification, public repositories, data enhancement, and downstream analysis. We emphasize algorithms that integrate statistical modeling and deep learning for identification, quantification, and the joint correction of missingness and batch effects. We highlight the unique role of deep learning in modeling non-linear batch-dependent effects and learning robust protein representations from sparse, high-dimensional MS-SCP data. Finally, we outline future directions for method developers, including the incorporation of biological priors, the construction of abundance-level foundation models, the curation of single-cell perturbation datasets, and the integration of proteomic information with spatial and multimodal data. Proteomics ; Missing data ; Identification (biology) ; Deep learning ; Data integration ; Computational model ; Statistical learning ; Human proteins Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cell Image Analysis Techniques
Authors: Jie Li | Bin Zou | Xingna Zheng | Yongxin Li | Hanyao Huang
Source: Talanta (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

A smartphone-detection strategy for accurate discrimination of Chinese teas via simultaneous sensing of infusion and aroma using multi-metal nanozymes Chinese tea ; Pattern recognition (psychology) ; Signal processing ; Sensor array ; Instrumentation (computer programming) ; SIGNAL (programming language) ; Workflow Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Advanced Chemical Sensor Technologies