jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ali Kalantarnia | Abdollah Mirzabeigi
Source: IET Science Measurement & Technology (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Approach for Predicting Electromagnetic Shielding Effectiveness of Metallic Grids in the Time and Frequency Domains ABSTRACT Accurate evaluation of electromagnetic shielding effectiveness (SE) is crucial for protecting modern electronic systems against electromagnetic interference (EMI) and transient disturbances such as electromagnetic pulses (EMP). This study investigates both time‐domain shielding effectiveness (TDSE) and frequency‐domain shielding effectiveness (FDSE) of metallic grid structures on dielectric substrates. TDSE metrics, including Peak SE and Derivative SE for electric and magnetic fields, quantify the attenuation of both field amplitude and its temporal rate of change under transient EM exposure. Full‐wave simulations using the Finite Integration Technique (FIT) in computer simulation technology microwave studio (CST‐MWS) were performed to generate datasets for training multilayer perceptron (MLP) neural networks. The MLP models map five structural and material parameters—aperture width, metal thickness, substrate thickness, relative permittivity, and loss tangent—to TDSE and FDSE responses. For TDSE prediction, the trained network achieves a root mean square error (RMSE) of 0.02215 and R 2 of 0.9846 on test data, demonstrating high predictive accuracy. For FDSE prediction across 1–4 GHz, the network provides close agreement with simulated spectra. Furthermore, a neural network‐based surrogate model is employed for rapid optimisation of metallic grid design under target shielding criteria. Comparisons with the Trust Region Framework in CST show that the surrogate‐based approach maintains high accuracy while significantly reducing computational time and optimisation cost. The proposed methodology enables efficient evaluation, prediction, and optimisation of metallic grid configurations for electromagnetic shielding applications under transient conditions. Electromagnetic shielding ; Artificial neural network ; Transient (computer programming) ; Grid ; Multilayer perceptron ; Attenuation ; Electromagnetic interference ; Eddy current ; Amplitude Electromagnetic wave absorption materials ; Electromagnetic Compatibility and Measurements ; Advanced Antenna and Metasurface Technologies
Authors: An-Jun Liu | Bryan K. Clark
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Neural network backflow for ab-initio solid calculations Accurately simulating extended periodic systems is a central challenge in condensed matter physics. Neural quantum states (NQS) offer expressive wavefunctions for this task but face issues with scalability. In this work, we successfully extend the neural network backflow (NNBF) approach to ab-initio solid-state materials. Building on our scalable optimization framework for molecules [Liu et al., PRB 112, 155162 (2025)], we introduce a two-stage pruning strategy to manage the massive configuration space expansions: by utilizing a computationally cheap, physics-informed importance proxy, we devote exact NNBF amplitude evaluations solely to the most relevant determinants, significantly improving optimization efficiency, energy estimation, and convergence. Our framework achieves state-of-the-art accuracy across diverse solid-state benchmarks. For 1D hydrogen chains, NNBF matches or surpasses DMRG and AFQMC, remains robust in strongly correlated bond-breaking regimes where coupled-cluster methods fail, and smoothly extrapolates to the thermodynamic limit. We further demonstrate its scalability by computing ground-state potential energy curves for 2D graphene and 3D silicon. Finally, ablation studies validate the computational savings of our pruning strategy and highlight the dependence of the NNBF energies on basis sets. Scalability ; Artificial neural network ; Floquet theory ; Pruning ; Quantum ; Backflow ; Wave function ; Energy (signal processing) ; Optimization problem Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Qian Gao | Junmin Chen | Kuang Yu
Source: arXiv (Cornell University) (preprint, 2026-01-26)
Field: Materials Science — Materials Chemistry

Refinement and Performance Benchmark for Range-Separated Water Force Field In our previous work, we developed a CCSD(T)-level range-separated water force field that combines the power of physics-driven and machine learning models. However, it was found that expensive CCSD(T)/CBS calculations lead to limited number of QM data as well as the missing of force labels, both of which lead to training instability issues. Bulk properties show large variations that cannot be resolved by simply reducing the fitting error in small cluster QM dataset. Such instability in bulk phase simulation is a universal problem in the training of machine learning potentials (MLPs), and is particularly severe at CCSD(T) level of theory.In this work, using our range-separated water model as an example, we aim to overcome these limitations by developing a new training workflow. It is composed by several techniques including: 1. an active learning protocol that ensures more thorough sampling in different temperatures and densities; 2. an intermediate force label technique employing machine learning density functional; and 3. an ensemble knowledge distillation (EKD) method. These techniques significantly stabilize the resulting water model, consistently achieving sub-chemical accuracies in both cluster energies and experimental properties. Benchmarks are carried out for various properties including densities, radial distribution functions (RDFs), dielectric constants, diffusivity, and infrared spectra, all showing state-of-the-art (SOTA) performances and proving the effectiveness of the training protocol. Benchmark (surveying) ; Stability (learning theory) ; Field (mathematics) ; Force field (fiction) ; Sampling (signal processing) ; Cluster (spacecraft) ; Phase (matter) ; Missing data Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Maliheh Hasannia | Ali Abounoori | Mahdi Abounoori | Maryam Shirzad | Abbas Rahdar | Sonia Fathi-karkan | Luiz Fernando Ferreira
Source: Seminars in Oncology (article, 2026-04-01)
Field: Materials Science — Biomaterials

Physics-Informed Machine Learning for Tumor Microenvironment-Responsive Nanomedicine: Recent Updates MEDLINE ; Cancer ; Deep learning ; Text mining Nanoparticle-Based Drug Delivery ; Immunotherapy and Immune Responses ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhenxing Dai | Zhong Yang | Mingjue Ni | Menglin Huang | HongJun XIANG | Xin-Gao Gong | Shiyou Chen
Source: arXiv (Cornell University) (preprint, 2026-04-08)
Field: Materials Science — Materials Chemistry

Machine learning Hamiltonian enables scalable and accurate defect calculations: The case of oxygen vacancies in amorphous SiO$_2$ Point defects critically influence the properties of materials and devices, yet density functional theory (DFT) remains computationally demanding for defect supercell calculations. Machine learning interatomic potentials (MLIPs) offer high efficiency but require extensive datasets. MLIPs trained only on defect configurations in small supercells exhibit systematic energy errors in larger supercells, demonstrating limited transferability. Here, we present a machine learning Hamiltonian (MLH) model-based method for calculating total energies and atomic forces in defect supercells with linear-scaling computational cost, enabling efficient structural relaxation and accurate formation energy predictions. We take oxygen vacancies in amorphous SiO$_2$ as an example and train the MLH model on defect configurations in 95-atom supercells, with the training data derived from 120 self-consistent field calculations and 12 structural relaxations. The MLH model enables efficient structural relaxations for host (defect-free) and defect systems in larger supercells, avoiding the systematic energy errors observed in MLIPs. The cancellation of energy errors between host and defect systems yields accurate formation energy predictions, with deviations from DFT below 50 meV. The proposed method holds significant potential for defect simulations in complex materials. Density functional theory ; Hamiltonian (control theory) ; Crystallographic defect ; Scalability ; Amorphous solid ; Energy (signal processing) ; Molecular dynamics ; Interatomic potential ; Point (geometry) Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Catalysis and Oxidation Reactions
Authors: Youngho Sihn | Sungjune Sohn | Min Kyeong Jeon | Sung-Wook Kim | Hyunwoo Seong | Kanghee Lee | Jiwon Nam
Source: Korean Journal of Chemical Engineering (article, 2026-02-02)
Field: Chemistry — Inorganic Chemistry

Advances in Coordination Chemistry and Molecular Design of Ligands for Nuclear Fuel Resources: Efficient Uranium Extraction from Seawater — A Review Uranyl ; Coordination complex ; Uranium ; Ligand (biochemistry) ; Hydrogen bond ; Rational design ; Extraction (chemistry) ; Covalent bond ; Chelation Radioactive element chemistry and processing ; Lanthanide and Transition Metal Complexes ; Supramolecular Chemistry and Complexes
Authors: Zhenxing Dai | Zhong Yang | Mingjue Ni | Menglin Huang | HongJun XIANG | Xin-Gao Gong | Shiyou Chen
Source: arXiv (Cornell University) (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Machine learning Hamiltonian enables scalable and accurate defect calculations: The case of oxygen vacancies in amorphous SiO$_2$ Point defects critically influence the properties of materials and devices, yet density functional theory (DFT) remains computationally demanding for defect supercell calculations. Machine learning interatomic potentials (MLIPs) offer high efficiency but require extensive datasets. MLIPs trained only on defect configurations in small supercells exhibit systematic energy errors in larger supercells, demonstrating limited transferability. Here, we present a machine learning Hamiltonian (MLH) model-based method for calculating total energies and atomic forces in defect supercells with linear-scaling computational cost, enabling efficient structural relaxation and accurate formation energy predictions. We take oxygen vacancies in amorphous SiO$_2$ as an example and train the MLH model on defect configurations in 95-atom supercells, with the training data derived from 120 self-consistent field calculations and 12 structural relaxations. The MLH model enables efficient structural relaxations for host (defect-free) and defect systems in larger supercells, avoiding the systematic energy errors observed in MLIPs. The cancellation of energy errors between host and defect systems yields accurate formation energy predictions, with deviations from DFT below 50 meV. The proposed method holds significant potential for defect simulations in complex materials. Density functional theory ; Hamiltonian (control theory) ; Crystallographic defect ; Scalability ; Amorphous solid ; Energy (signal processing) ; Molecular dynamics ; Interatomic potential ; Point (geometry) Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Catalysis and Oxidation Reactions
Authors: Bazri Shahab | Mapelli Carlo | Mombelli Davide | Nemfardi Renato | Bedini Renato | Zucchelli Giorgio
Source: Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

From kinetics to imagery: A JMAK-informed, chained predictive artifi intelligence method for interpretable steel microstructure simulation This paper presents a novel, physically informed machine learning (ML) framework for the accurate modeling and visualization of steel microstructure evolution during annealing. Utilizing a chained support vector regression (SVR) architecture with optimized hyperparameters, the model sequentially predicts key microstructural states, ensuring metallurgical consistency. The process begins by forecasting recrystallization fraction (RF) kinetics, which is critically constrained by the classical Johnson–Mehl–Avrami–Kolmogorov (JMAK) model. The resulting JMAK-corrected RF then serves as a fundamental input to subsequent SVR models, which forecast the average grain size (AGS) and, finally, essential image-based microstructural features (mean and standard deviation of pixel intensity). This chained approach inherently prioritizes physically sound outputs, avoiding the consistency issues of isolated predictions. A unique visualization methodology is introduced, which selects and maps the closest experimental inverse pole figure (IPF) maps to the predicted states. This robust, multi-stage framework establishes a powerful, data-driven tool for simulating complex material evolution, thus minimizing the need for extensive experimental operations in materials design and process optimization. Visualization ; Process (computing) ; Inverse ; Microstructure ; Support vector machine ; Consistency (knowledge bases) ; Key (lock) ; Recrystallization (geology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and Mechanical Properties of Steels
Authors: Yalan Zhang | Yike Guo | Yuanyuan Zhou
Source: Matter (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

GrainBot: Quantifying multi-variable microstructure disorder in materials Microstructure ; Cementite ; Welding ; Metallography ; Work (physics) Microstructure and mechanical properties ; Machine Learning in Materials Science ; Composite Material Mechanics
Authors: Dongyu Bai | Junxian Liu | Yihan Nie | YuanTong Gu | Dongchen Qi | Arkady V. Krasheninnikov | Liangzhi Kou
Source: Small Methods (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Ferroelectric Domains and Evolution Dynamics in Twisted CuInP <sub>2</sub> S <sub>6</sub> Bilayers (Small Methods 1/2026) Inside Back Cover Using density functional theory and deep-learning molecular dynamics, in article number 2500683, Kou and co-workers show that polar domains emerge and can be manipulated in twisted bilayers of ferroelectric CuInP2S6. Their thermal stability and polarization lifetimes are sensitive to twist angle and temperature, and tunable by external electric fields and strain, offering a pathway to control local polarization through rotational manipulation. Polarization (electrochemistry) ; Ferroelectricity ; Polar ; Electric field ; Twist ; Thermal ; Molecular dynamics ; Density functional theory 2D Materials and Applications ; Chemical and Physical Properties of Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Zehong Wang | Xiaolong Han | Qi Yang | Xiangru Tang | Fang Wu | Xiaoguang Guo | Weixiang Sun | Tianyi Ma | Pietro Lio | Le Cong | Sheng Wang | Chuxu Zhang | Yanfang Ye
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Molecular Representations in Implicit Functional Space via Hyper-Networks Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipeline: molecules are encoded as sequences, graphs, or point clouds, mapped to fixed-dimensional embeddings, and then used for task-specific prediction. This paradigm treats molecules as discrete objects, despite their intrinsically continuous and field-like physical nature. We argue that molecular learning can instead be formulated as learning in function space. Specifically, we model each molecule as a continuous function over three-dimensional (3D) space and treat this molecular field as the primary object of representation. From this perspective, conventional molecular representations arise as particular sampling schemes of an underlying continuous object. We instantiate this formulation with MolField, a hyper-network-based framework that learns distributions over molecular fields. To ensure physical consistency, these functions are defined over canonicalized coordinates, yielding invariance to global SE(3) transformations. To enable learning directly over functions, we introduce a structured weight tokenization and train a sequence-based hyper-network to model a shared prior over molecular fields. We evaluate MolField on molecular dynamics and property prediction. Our results show that treating molecules as continuous functions fundamentally changes how molecular representations generalize across tasks and yields downstream behavior that is stable to how molecules are discretized or queried. Function (biology) ; Space (punctuation) ; Discretization ; Property (philosophy) ; Molecular dynamics ; Point (geometry) ; Field (mathematics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Andrés Sebastián Pirolo
Source: Research Square (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Breaking the 1-WL Barrier: Turbo Fiedler Deck as aSpectral Positional Encoding for Industrial-ScaleGNNs Eigenvalues and eigenvectors ; Abelian sandpile model ; Encoding (memory) ; Graph ; Criticality ; Homogeneous space ; Hamming distance ; Binary number Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topological and Geometric Data Analysis
Authors: Roberto Riganti | Luca Dal Negro
Source: arXiv (Cornell University) (preprint, 2026-03-30)
Field: Materials Science — Materials Chemistry

A Unified Multiscale Auxiliary PINN Framework for Generalized Phonon Transport Nanoscale thermal transport is governed by the phonon Boltzmann transport equation (BTE). However, simulating the sub-continuum dynamics remains computationally prohibitive due to the high dimensionality of the phase space and the intrinsic nonlinearity of the scattering collision operator. Traditional numerical solvers and standard physics-informed neural networks (PINNs) inherently struggle with these integro-differential equations due to deterministic quadrature limitations, artificial thermalization introduced by the relaxation time approximation (RTA), and multiscale spectral bias. This work introduces a multiscale auxiliary physics-informed neural network (MTNet) to solve the generalized equation of phonon radiative transfer (GEPRT). By leveraging an auxiliary formulation, this mesh-free framework recasts the GEPRT into a fully differential system, enabling the analytical evaluation of scattering operators via automatic differentiation and facilitating scalable multi-GPU parallelization. To circumvent optimization stiffness, the architecture employs a decoupled, shallow neural network explicitly constrained by radiative equilibrium. MTNet is validated by simulating steady-state cross-plane transport in a silicon thin film, successfully capturing ballistic-diffusive regimes and characteristic boundary slips across extreme temperature gradients ($ΔT = 100$ K) beyond the standard linearization approach. Furthermore, we show that our framework successfully solves a geometric inverse problem in a slab geometry, retrieving the unknown slab thickness based only on interface temperature constraints in the mesoscopic regime. Ultimately, MTNet establishes a robust, fully differentiable foundation for predicting high-fidelity kinetic transport and extracting material properties in next-generation nanostructures. Boltzmann equation ; Phonon ; Boundary value problem ; Artificial neural network ; Nonlinear system ; Radiative transfer ; Curse of dimensionality ; Solver ; Inverse problem ; Computation Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Model Reduction and Neural Networks
Authors: Zehong Wang | Xiaolong Han | Qi Yang | Xiangru Tang | Fang Wu | Xiaoguang Guo | Weixiang Sun | Tianyi Ma | Pietro Lio | Le Cong | Sheng Wang | Chuxu Zhang | Yanfang Ye
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

Molecular Representations in Implicit Functional Space via Hyper-Networks Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a discrete pipeline: molecules are encoded as sequences, graphs, or point clouds, mapped to fixed-dimensional embeddings, and then used for task-specific prediction. This paradigm treats molecules as discrete objects, despite their intrinsically continuous and field-like physical nature. We argue that molecular learning can instead be formulated as learning in function space. Specifically, we model each molecule as a continuous function over three-dimensional (3D) space and treat this molecular field as the primary object of representation. From this perspective, conventional molecular representations arise as particular sampling schemes of an underlying continuous object. We instantiate this formulation with MolField, a hyper-network-based framework that learns distributions over molecular fields. To ensure physical consistency, these functions are defined over canonicalized coordinates, yielding invariance to global SE(3) transformations. To enable learning directly over functions, we introduce a structured weight tokenization and train a sequence-based hyper-network to model a shared prior over molecular fields. We evaluate MolField on molecular dynamics and property prediction. Our results show that treating molecules as continuous functions fundamentally changes how molecular representations generalize across tasks and yields downstream behavior that is stable to how molecules are discretized or queried. Function (biology) ; Space (punctuation) ; Discretization ; Property (philosophy) ; Molecular dynamics ; Point (geometry) ; Field (mathematics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Guillaume Godin
Source: arXiv (Cornell University) (preprint, 2026-03-11)
Field: Materials Science — Materials Chemistry

SCORE: Replacing Layer Stacking with Contractive Recurrent Depth Residual connections are central to modern deep neural networks, enabling stable optimization and efficient information flow across depth. In this work, we propose SCORE (Skip-Connection ODE Recurrent Embedding), a discrete recurrent alternative to classical layer stacking. Instead of composing multiple independent layers, SCORE iteratively applies a single shared neural block using an ODE (Ordinary Differential Equation)-inspired contractive update: ht+1 = (1 - dt) * ht + dt * F(ht) This formulation can be interpreted as a depth-by-iteration refinement process, where the step size dt explicitly controls stability and update magnitude. Unlike continuous Neural ODE approaches, SCORE uses a fixed number of discrete iterations and standard backpropagation without requiring ODE solvers or adjoint methods. We evaluate SCORE across graph neural networks (ESOL molecular solubility), multilayer perceptrons, and Transformer-based language models (nanoGPT). Across architectures, SCORE generally improves convergence speed and often accelerates training. SCORE is reducing parameter count through shared weights. In practice, simple Euler integration provides the best trade-off between computational cost and performance, while higher-order integrators yield marginal gains at increased compute. These results suggest that controlled recurrent depth with contractive residual updates offers a lightweight and effective alternative to classical stacking in deep neural networks. Ode ; Residual ; Artificial neural network ; Block (permutation group theory) ; Convergence (economics) ; Recurrent neural network ; Ordinary differential equation ; Backpropagation ; Stability (learning theory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion