jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Debendra Meher | Nikhil V. S. Avula | Sundaram Balasubramanian
Source: The Journal of Chemical Physics (article, 2026-03-25)
Field: Materials Science — Ceramics and Composites

On the boroxol ring fraction in melt-quenched B2O3 glass: Insights from machine learning potentials An atomistic structural model for melt-quenched B2O3 glass has eluded the simulation community so far. The difficulty lies in the abundance of six-membered boroxol rings-an intermediate-range order motif suggested by Raman and NMR spectroscopy-which is challenging to capture in atomistic molecular dynamics simulations. Here, we report the development of a density functional theory-accurate machine-learned potential and employ quench rates as low as 109 K/s to obtain B2O3 glasses with more than 30% of boron atoms in boroxol rings. Additionally, we show that the pressure, and consequently the boroxol fraction, in the deep potential molecular dynamics simulations critically depends on the range of the geometry descriptor used in the embedding neural network, and it converges beyond a value of 7 Å. The boroxol ring fraction increases with decreasing quench rate. Finally, amorphous B2O3 configurations display a minimum in energy at a boroxol fraction of 75%, remarkably close to the experimental estimate in B2O3 glass. Molecular dynamics ; Ring (chemistry) ; Boron ; Raman spectroscopy ; Fraction (chemistry) ; Amorphous solid ; Artificial neural network Glass properties and applications ; Material Dynamics and Properties ; Phase-change materials and chalcogenides
Authors: Imene Gritli | Idoumou Bouya Ahmed | Zakarya Ahmed | Chemseddine Maatki | Fehri Krout | I. Ben Assaker
Source: Research Square (preprint, 2026-03-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synergetic Experimental and ANN- Based Modeling of Cyclic Voltammetry Behavior in NH 4 F-Assisted MnCo 2 O 4 Nanosheet for High-Performance Supercapacitors Nanosheet ; Supercapacitor ; Capacitance ; Cyclic voltammetry ; Nanowire ; Electrode ; Electrochemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Zengshan Yu | Zhibin Zhang | Shan Guo | Zelong Li | Hao Chen | Jichuan Gai | Jiyuan Wei | Shiqi Xu | Mingli Wang | Guochao Shi
Source: Microchimica Acta (article, 2026-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Immunochromatographic SERS sensor-deep learning combined strategy for intelligent diagnosis of chronic kidney disease Pattern recognition (psychology) ; Deep learning ; Residual ; Artificial neural network ; Raman scattering ; Receiver operating characteristic ; Feature (linguistics) ; Kidney disease Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Nicholas Gao | Till Grutschus | Frank Noé | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2026-03-15)
Field: Materials Science — Materials Chemistry

Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient numerical accuracy in state overlaps requires increasing the number of Monte Carlo samples, and consequently the computational cost, with the number of states. We present a nearly constant sample-size approach, Multi-State Importance Sampling (MSIS), that leverages samples from all states to estimate pairwise overlap. To efficiently evaluate all states for all samples, we introduce Excited Pfaffians. Inspired by Hartree-Fock, this architecture represents many states within a single neural network. Excited Pfaffians also serve as generalized wave functions, allowing a single model to represent multi-state potential energy surfaces. On the carbon dimer, we match the $O(N_s^4)$-scaling natural excited states while training $&gt;200\times$ faster and modeling 50\% more states. Our favorable scaling enables us to be the first to use neural networks to find all distinct energy levels of the beryllium atom. Finally, we demonstrate that a single wave function can represent excited states across various molecules. Excited state ; Wave function ; Scaling ; Monte Carlo method ; Energy (signal processing) ; Artificial neural network ; Pairwise comparison ; Variational Monte Carlo Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Yanghong Zhang | Yan Ma | Jianhua Wu | Youqing Wu | Yong Li | Lei Xu | Zhichao Lou
Source: Forests (article, 2026-02-13)
Field: Materials Science — Surfaces, Coatings and Films

A Review of Low-Temperature Plasma for Surface Engineering of Biomass Materials The inherent surface limitations of biomass materials restrict their use in high-performance composites, highlighting the need for effective surface modification. Low-temperature plasma technology has emerged as a promising solution owing to its rapid processing, localized energy, and environmental friendliness. This review systematically examines recent advances in the surface modification of biomass materials using low-temperature plasma. First, low-temperature plasma systems are classified and described according to working atmosphere, operating pressure, and discharge mode. Subsequently, the physicochemical mechanisms underlying surface modification are thoroughly discussed. The effect of key process parameters—such as aging effect, discharge power, and treatment distance—on modification outcomes is analyzed. The specific applications and effects of low-temperature plasma technology on typical biomass materials, including wood, bamboo, and straw, are summarized. Studies have indicated that low-temperature plasma treatment significantly enhances the surface properties of biomass materials. With further development, low-temperature plasma technology may gradually replace certain conventional modification methods. Future research should focus on elucidating the structure–property relationships between plasma process parameters and material performance and advancing the technology from laboratory to industrial scale. The integration of artificial intelligence and machine learning provides great potential to accelerate the discovery of process–performance correlations through data modeling and intelligent optimization, enabling precise control and smart manufacturing. Surface modification ; Biomass (ecology) ; Process (computing) ; Surface engineering ; Plasma ; Process control Surface Modification and Superhydrophobicity ; Plasma Applications and Diagnostics ; Materials Engineering and Processing
Authors: Xiaqiong Fan | Zhengyan Li | LiJin Shang | Jiaqi Zhang | Bingyue Liu | xiaozhen Ren | Gang Liu | Xiaodong Li | Tiejun Yang | Hongchao Ji
Source: Talanta (article, 2026-01-28)
Field: Chemistry — Analytical Chemistry

DeepHSI: A transferable and expandable hyperspectral framework for industrial plant origin identification: A case study of Pogostemon cablin (Blanco) Benth Hyperspectral imaging ; Pogostemon ; Identification (biology) ; Aroma ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Poyen Chen | Teruyasu Mizoguchi
Source: Materials & Design (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential • Mechanical stress–induced polarization switching and domain wall formation in BaTiO 3 were investigated using a machine learning potential. • Domain wall formation was found to depend on supercell size through the activation energy of polarization switching. • The BaTiO 3 hysteresis loop was successfully reproduced, and stress-induced double hysteresis loops were observed. Ferroelectric materials such as BaTiO 3 exhibit spontaneous polarization that can be reoriented by an external electric field, forming the basis of a wide range of memory, actuator, and sensor applications. However, the polarization switching behavior is strongly influenced by mechanical boundary conditions due to the intrinsic electromechanical coupling in ferroelectrics. In this study, we employ a machine learning interatomic potential to investigate the effect of uniaxial compressive stress on polarization switching and domain wall evolution in the tetragonal phase of BaTiO 3 . This study revealed that a critical stress of approximately 120 MPa, above which 90° polarization switching occurs. Beyond the critical stress, larger supercells exhibit lower activation energies for polarization switching, accompanied by the 180° domain wall formation and reduced constraints from periodic boundary conditions, thereby facilitating domain-wall formation. Moreover, increasing compressive stress leads to a reduction in both the remnant polarization and the coercive field, while a double hysteresis loop emerges at a stress level of 80 MPa. These findings provide atomistic insights into stress-controlled ferroelectric switching and highlight the crucial role of mechanical loading in designing reliable ferroelectric devices. Ferroelectricity ; Tetragonal crystal system ; Polarization (electrochemistry) ; Phase boundary ; Hysteresis ; Domain wall (magnetism) ; Compressive strength Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Sensor and Energy Harvesting Materials
Authors: Timothée Devergne | Vladimir Kostic | Massimiliano Pontil | Michele Parrinello
Source: Proceedings of the National Academy of Sciences (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

The seeds of the future are in the present: A blind exploration of metastable states In this work, we present a type of molecular dynamics simulation that aims at finding, in a blind way, metastable states. Using only data coming from an initial unbiased simulation, and with the help of an appropriately defined loss function, we compute a bias that favors sampling yet unexplored configurational space regions, encouraging the system to leave the initial basin. In our work, we take advantage of what is normally thought to be a defect, namely the difficulty of neural networks to generalize. Contrary to most other enhanced sampling methods, which need previous knowledge of the reactive process, we are able to explore in a blind way metastable states, overcoming otherwise insuperable kinetic bottlenecks. We illustrate the workings of the method with a number of instructive examples. Metastability ; Sampling (signal processing) ; Space (punctuation) ; Kinetic energy ; Sampling bias ; Type (biology) ; Unbiased Estimation Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Leonardo Boledi | Dirk Bosbach | Jenna Poonoosamy
Source: arXiv (Cornell University) (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions The computational cost of geochemical solvers is a challenging matter. For reactive transport simulations, where chemical calculations are performed up to billions of times, it is crucial to reduce the total computational time. Existing publications have explored various machine-learning approaches to determine the most effective data-driven surrogate model. In particular, multilayer perceptrons are widely employed due to their ability to recognize nonlinear relationships. In this work, we focus on the recent Kolmogorov-Arnold networks, where learnable spline-based functions replace classical fixed activation functions. This architecture has achieved higher accuracy with fewer trainable parameters and has become increasingly popular for solving partial differential equations. First, we train a surrogate model based on an existing cement system benchmark. Then, we move to an application case for the geological disposal of nuclear waste, i.e., the determination of radionuclide-bearing solids solubilities. To the best of our knowledge, this work is the first to investigate co-precipitation with radionuclide incorporation using data-driven surrogate models, considering increasing levels of thermodynamic complexity from simple mechanical mixtures to non-ideal solid solutions of binary (Ba,Ra)SO$_4$ and ternary (Sr,Ba,Ra)SO$_4$ systems. On the cement benchmark, we demonstrate that the Kolmogorov-Arnold architecture outperforms multilayer perceptrons in both absolute and relative error metrics, reducing them by 62% and 59%, respectively. On the binary and ternary radium solid solution models, Kolmogorov-Arnold networks maintain median prediction errors near $1\times10^{-3}$. This is the first step toward employing surrogate models to speed up reactive transport simulations and optimize the safety assessment of deep geological waste repositories. Perceptron ; Surrogate model ; Ternary operation ; Work (physics) ; Surrogate data ; Binary number ; Nonlinear system ; Focus (optics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Nuclear reactor physics and engineering
Authors: Hubert Beck | Ondrej Marsalek
Source: arXiv (Cornell University) (preprint, 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: Ruixuan Dong | Xiuqin Liu
Source: Computational Materials Science (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

BPU-Net: a precise grain image segmentation method based on bidirectional skip connections and continuous VI loss Segmentation ; Discriminative model ; Metric (unit) ; Boundary (topology) ; Intersection (aeronautics) ; Benchmark (surveying) ; Generalization ; Feature (linguistics) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Microstructure and mechanical properties ; Additive Manufacturing Materials and Processes
Authors: Francesco Leonardi | Boris Bonev | Kaspar Riesen
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields Machine learning force fields (MLFFs) have become essential for accurate and efficient atomistic modeling. Despite their high accuracy, most existing approaches rely on fixed angular expansions, limiting flexibility in weighting local geometric interactions. We introduce Modular Angular-Radial Attention (MARA), a module that extends spherical attention -- originally developed for SO(3) tasks -- to the molecular domain and SE(3), providing an efficient approximation of equivariant interactions. MARA operates directly on the angular and radial coordinates of neighboring atoms, enabling flexible, geometrically informed, and modular weighting of local environments. Unlike existing attention mechanisms in SE(3)-equivariant architectures, MARA can be integrated in a plug-and-play manner into models such as MACE without architectural modifications. Across molecular benchmarks, MARA improves energy and force predictions, reduces high-error events, and enhances robustness. These results demonstrate that continuous spherical attention is an effective and generalizable geometric operator that increases the expressiveness, stability, and reliability of atomistic models. Modular design ; Weighting ; Operator (biology) ; Flexibility (engineering) ; Limiting ; Domain (mathematical analysis) ; Force field (fiction) Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Crystallography and molecular interactions
Authors: Siddharth Sriram | Elten Polukhov | Felix Fritzen | Marc‐André Keip
Source: Mechanics of Materials (article, 2026-02-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of magnetoelectric composites using convolutional neural networks Poling ; Piezoelectricity ; Homogenization (climate) ; Ferroelectricity ; Polarization (electrochemistry) ; Boundary value problem ; Representative elementary volume ; Multiphysics ; Finite element method ; Modulus Multiferroics and related materials ; Composite Material Mechanics ; Neural Networks and Applications
Authors: Francesco Leonardi | Boris Bonev | Kaspar Riesen
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields Machine learning force fields (MLFFs) have become essential for accurate and efficient atomistic modeling. Despite their high accuracy, most existing approaches rely on fixed angular expansions, limiting flexibility in weighting local geometric interactions. We introduce Modular Angular-Radial Attention (MARA), a module that extends spherical attention -- originally developed for SO(3) tasks -- to the molecular domain and SE(3), providing an efficient approximation of equivariant interactions. MARA operates directly on the angular and radial coordinates of neighboring atoms, enabling flexible, geometrically informed, and modular weighting of local environments. Unlike existing attention mechanisms in SE(3)-equivariant architectures, MARA can be integrated in a plug-and-play manner into models such as MACE without architectural modifications. Across molecular benchmarks, MARA improves energy and force predictions, reduces high-error events, and enhances robustness. These results demonstrate that continuous spherical attention is an effective and generalizable geometric operator that increases the expressiveness, stability, and reliability of atomistic models. Modular design ; Weighting ; Operator (biology) ; Flexibility (engineering) ; Limiting ; Domain (mathematical analysis) ; Force field (fiction) Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Crystallography and molecular interactions
Authors: Dillon G. Frost | Johann Bouchet | Mihai‐Cosmin Marinica | Clovis Lapointe | Jean-Bernard Maillet | Luca Messina
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Fully anharmonic calculations of the free energy of migration of point defects in UO2 and PuO2 Calculating diffusion rates of point defects in materials typically relies on the harmonic approximation to estimate migration free energies. However, anharmonic effects can have a large impact on diffusion properties, and explicitly accounting for them is usually computationally demanding and difficult to achieve in practice. In this work, we investigate the role of anharmonic effects on defect migration in UO2 and PuO2 using the potential of average force integration (PAFI) method. Fully anharmonic migration free energies are computed for several cation and anion defect types, using the Cooper-Rushton-Grimes (CRG) potential and a recently developed machine learning spectral neighbour analysis potential (SNAP) for UO2. Results are systematically compared to harmonic estimates based on attempt frequencies and the Debye approximation. Our results reveal that the validity of the harmonic approximation strongly depends on the defect type and the underlying potential, with significant deviations observed in several cases. In particular, defect migration barriers are found to decrease strongly with increasing temperature (up to 1 eV between 0 and 1200 K), and anharmonic contributions can substantially modify migration entropies and, consequently, diffusion coefficients. Comparing defect migration in UO2 and PuO2 using the CRG potential reveals that PuO2 has lower migration enthalpies at 0~K for all considered defects, but this is compensated by higher attempt frequencies, resulting in similar overall jump frequencies in UO2 and PuO2. These findings provide insight into the limitations of commonly used approximations and highlight the importance of anharmonic effects for predictive modeling of diffusion in nuclear fuels as well as in other classes of materials. Anharmonicity ; Diffusion ; Crystallographic defect ; Harmonic ; Point (geometry) ; Debye ; Jump Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; High Temperature Alloys and Creep
Authors: Liangfeng Chen | Siwei Yang | Xiangqian Cui | Ziheng Liu | Hang Wang | Yongqiang Li | Peng He | Mengdi Wang | Guqiao Ding
Source: Responsive materials (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges Abstract Carbon‐based quantum dots (CQDs) have emerged as a versatile class of fluorescent nanomaterials with broad applications in optoelectronics, sensing, and biomedicine; however, their intrinsic structural and chemical complexity poses significant challenges to mechanistic understanding and rational regulation. Machine learning (ML) provides a powerful approach for analyzing complex experimental datasets, uncovering hidden correlations, and enabling insights beyond conventional empirical methodologies. This review summarizes recent ML‐driven advances in CQDs research, with a particular emphasis on fluorescence mechanisms, regulation strategies, and application‐relevant performance optimization, while critically examining fundamental challenges related to interpretability, generalizability, and data reliability. Finally, perspectives on future ML‐assisted frameworks for advancing CQDs toward practical applications are provided. Class (philosophy) ; Mechanism (biology) ; Quantum ; Quantum dot Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications