jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nikhil Vadera | Saakshi Dhanekar
Source: Microchemical Journal (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Near room temperature Chemiresistive NOx detection using nano-porous silicon and silicon nanowires with machine learning classification Silicon ; Porous silicon ; Nanowire ; Detection limit ; NOx ; Selectivity ; Sensitivity (control systems) ; X-ray photoelectron spectroscopy Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Amin Sakic | Ronald Schnitzer | David Holec
Source: Open MIND (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

DFT and MLIP study of solute segregation to coherent and semi-coherent α-Fe/Fe$_3$C interfaces Solute segregation to interfaces significantly impacts material behavior. A large majority of theoretical works focus on grain boundaries and coherent interfaces. Studies on semi-coherent interfaces are usually prohibited by the structural complexity, yielding models beyond the practical capability of density functional theory (DFT), or chemical complexity, restricted by the availability of (classical) interatomic potentials. This work investigates solute segregation to the coherent and semi-coherent $α$-Fe/Fe$_3$C interface in pearlite and its effect on mechanical properties using novel universal machine learning interatomic potentials (uMLIPs). DFT calculated solution enthalpies, segregation energetics, and changes in cohesion at the coherent interface are used to benchmark several state-of-the-art uMLIPs. We find that the GRACE-2L-OAM and GRACE-2L-OMAT models most accurately reproduce the quantum-mechanical predictions. While Cu has the strongest segregation energy of $\approx$ -0.3 eV to the coherent interface among the investigated tramp and trace elements, all of them, As, Cr, Cu, Mo, Ni, P, Sb, and Sn, exhibit significantly more negative segregation values reaching below $\approx$ -1.5 eV in the presence of the misfit dislocation at the semi-coherent interface. The deepest traps are identified in the vicinity of the dislocation core, although the spatial distribution of segregation energies differs markedly among the solute species. The cohesion of the coherent interface is strongly reduced by Sb, Sn, P, and As, and only mildly by Cu, whereas Ni shows a negligible effect, and Cr and Mo slightly enhance cohesion. In contrast, all investigated solutes (except for P) tend to embrittle the semi-coherent interface, with Sn and, especially, Sb having the strongest impact in tensile tests performed in the out-of-plane direction. Abstract shortened for ArXiv. Grain boundary ; Dislocation ; Work (physics) ; Cohesion (chemistry) ; Density functional theory ; Coherent potential approximation Machine Learning in Materials Science ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Neelesh Yadav | Anand Sharma | Divyesh Suvedi | Sonali Khanal | Shivam Sharma | Rachna Verma | Dinesh Kumar | Lukas Peter | Gholamreza Abdi
Source: Journal of Stored Products Research (article, 2026-01-28)
Field: Chemistry — Analytical Chemistry

Smart sensor and artificial intelligence technologies for postharvest loss reduction and food safety in nutri-cereals Postharvest ; Food spoilage ; Food safety ; Reduction (mathematics) ; Food supply ; Food processing ; Intelligent sensor Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Terry C. W. Lam | Niamh O'Neill | Christoph Schran | Lars L. Schaaf
Source: Open MIND (preprint, 2026-02-09)
Field: Materials Science — Materials Chemistry

Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials The accuracy of machine learning interatomic potentials suffers from reference data that contains numerical noise. Often originating from unconverged or inconsistent electronic-structure calculations, this noise is challenging to identify. Existing mitigation strategies such as manual filtering or iterative refinement of outliers, require either substantial expert effort or multiple expensive retraining cycles, making them difficult to scale to large datasets. Here, we introduce an on-the-fly outlier detection scheme that automatically down-weights noisy samples, without requiring additional reference calculations. By tracking the loss distribution via an exponential moving average, this unsupervised method identifies outliers throughout a single training run. We show that this approach prevents overfitting and matches the performance of iterative refinement baselines with significantly reduced overhead. The method's effectiveness is demonstrated by recovering accurate physical observables for liquid water from unconverged reference data, including diffusion coefficients. Furthermore, we validate its scalability by training a foundation model for organic chemistry on the SPICE dataset, where it reduces energy errors by a factor of three. This framework provides a simple, automated solution for training robust models on imperfect datasets across dataset sizes. Overfitting ; Outlier ; Scalability ; Extrapolation ; Noise (video) ; Robustness (evolution) ; Training (meteorology) ; Kernel (algebra) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Henning Windeck | Daniel Willimetz | Andreas Erlebach | Christopher J. Heard | Lukáš Grajciar | Fabian Berger | Joachim Sauer
Source: edoc Publication server (Humboldt University of Berlin) (article, 2026-01-14)
Field: Chemistry — Inorganic Chemistry

Water Adsorption at Pairs of Proximate Brønsted Acid Sites in Zeolites We model water adsorption at pairs of proximate Brønsted acid sites (BASs) in zeolites H-MFI, H-FAU, and H-CHA. We use machine-learning potentials to explore the potential energy surface, combined with quantum mechanical methods for chemically accurate energies of selected structures. We identify BAS pairs that adsorb water cooperatively, forming an H-bonded chain that connects the two BASs and provides additional stabilization. The formation of such a water bridge requires at least two molecules, making the adsorption of the second water molecule stronger than the first, e.g., by 20 and 44 kJ mol–1 for an Al9–Al10 and an Al4–Al6 pair, respectively, in H-MFI, and by 11 kJ mol–1 for H-FAU. The magnitude of this extra-stabilization depends on the relative alignment of the BASs. Both Al pairs separated by just one SiO4 tetrahedron (next-nearest neighbor sites) and pairs across a 10-membered ring are included. The increase of the heat of adsorption with the water loading per BAS contrasts with the decrease obtained for isolated BASs and aligns with observations in some experiments. Adsorption ; Molecule ; Tetrahedron ; Zeolite ; Proximate Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Ammonia Synthesis and Nitrogen Reduction
Authors: James R. Chelikowsky | C. Z. Wang | Timothy Liao | Zhao Tang | Qi Zhang | Weiyi Xia
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-Driven and Quantum-Informed Searches for Rare-Earth Free Magnets: Applications to Fe-Co-X (X=B, C, P, Si, S) Compounds Magnetocrystalline anisotropy ; Magnet ; Ferromagnetism ; Curie temperature ; Dysprosium ; Ternary operation ; Density functional theory ; Curie ; Intermetallic Magnetic Properties of Alloys ; Heusler alloys: electronic and magnetic properties ; Magnetic properties of thin films
Authors: Aniruddha Bora | Isabel Alvarez | Julie Chalfant | Chryssostomos Chryssostomidis
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation Coolant ; Artificial neural network ; Heat sink ; MOSFET ; Curse of dimensionality ; Overheating (electricity) ; Thermal ; Heat flux ; Inverse Thermal properties of materials ; Heat Transfer and Optimization ; Silicon Carbide Semiconductor Technologies
Authors: Jesco Talies | Eric Breitbarth | David Melching
Source: Machine Learning Science and Technology (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Trustworthy AI-based crack-tip segmentation using domain-guided explanations Abstract Ensuring the trustworthiness and robustness of deep learning models remains a fundamental challenge, particularly in high-stakes scientific applications. In this study, we present a framework called attention-guided training that combines explainable artificial intelligence techniques with quantitative evaluation and domain-specific priors to guide model attention. We demonstrate that domain-specific feedback on model explanations during training can enhance the model’s generalization capabilities. We validate our approach on the task of semantic crack tip segmentation in digital image correlation data, which is a key application in the fracture mechanical characterization of materials. By aligning model attention with physically meaningful stress fields, such as those described by Williams’ analytical solution, attention-guided training ensures that the model focuses on physically relevant regions. This finally leads to improved generalization and more faithful explanations. Generalization ; Robustness (evolution) ; Prior probability ; Segmentation ; Task (project management) ; Key (lock) ; Trustworthiness Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Domain Adaptation and Few-Shot Learning
Authors: Chen Liu | Jingyun Guan | Yue Li | Bin Wang | Haiyan Zhang | Guohua Liu
Source: Microchemical Journal (article, 2026-01-02)
Field: Materials Science — Biomaterials

Electrochemical detection of pediatric cardiac biomarkers via peptide self-organization and cofactor-mediated signal transduction Peptide ; Analyte ; Electrochemistry ; Biomarker ; Biosensor ; Natriuretic peptide ; Cyclic voltammetry Supramolecular Self-Assembly in Materials ; Connective tissue disorders research ; Supramolecular Chemistry and Complexes
Authors: Hua Tong | Hui-Ling Kuang | Chengwei Wu | Yujia Ceng | Xue-Kun Chen | Wuxing Zhou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Al-Doping Effects on Ga2O3 Thermal Transport: Neural Network Potential-Based NEMD Thermal conductivity ; Phonon ; Doping ; Thermal ; Impurity ; Thermal management of electronic devices and systems ; Thermal conduction ; Molecular dynamics Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Thermal properties of materials
Authors: Yizhi Song | Renxi Liu | Chunyi Zhang | Yifan Li | Biswajit Santra | Mohan Chen | Michael L. Klein | Xifan Wu
Source: arXiv (Cornell University) (preprint, 2026-03-29)
Field: Materials Science — Materials Chemistry

Understanding the Density Maximum of Water with Machine Learned Potentials After melting, at ambient pressure, the density of water continues to increase with temperature until it reaches a maximum around 4 °C. For nearly a century, this phenomenon has been qualitatively attributed to a mixture of ordered and disordered structures. Herein, we employ a deep neural network to train a machine learned (ML) interatomic potential for water using electronic structure data from advanced density functional theory. Notably, molecular dynamics simulations with the ML potential reproduce both the experimental water density anomaly and the thermal expansion coefficient. Detailed structural analysis of the computed hydrogen-bond network reveals that the density anomaly arises from an emergent liquid structure that retains nearly ideal tetrahedral coordination at short range but collapses at intermediate range. Our findings point to a more delicate mechanism causing the density maximum than the conventional picture, emphasizing the collective roles of structural orderings at different length scales. Maximum density ; Anomaly (physics) ; Tetrahedron ; Liquid water ; Thermal ; Range (aeronautics) ; Molecular dynamics ; Point (geometry) ; Ideal (ethics) Machine Learning in Materials Science ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Denan Li | Chris Ahart | Shi Liu
Source: ArXiv.org (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Disentangling the Discrepancy Between Theoretical and Experimental Curie Temperatures in Ferroelectric PbTiO$_3$ Accurately predicting the Curie temperature ($T_c$) of ferroelectrics from first principles remains a major challenge, as theoretical estimates often fall significantly below experimental values. In this work, we investigate the origin of these discrepancies in the prototypical ferroelectric PbTiO$_3$ by performing extensive constant-pressure ab initio molecular dynamics (AIMD) simulations and benchmarking them against classical molecular dynamics (MD) using machine learning force fields (MLFFs) derived from first-principles data. Our results show that the underestimation of $T_c$ primarily stems from the limitations of the exchange-correlation functional, rather than inaccuracies in the MLFF fitting. We uncover a critical interplay between finite-size effects and the range of interatomic interactions: although short-range MLFFs appear to yield better agreement with experimental $T_c$, this improvement results from a fortuitous cancellation of errors. Incorporating explicit long-range interactions improves accuracy for larger supercells but ultimately leads to lower predicted $T_c$ values. These findings highlight that accurate finite-temperature predictions require not only high-quality training data and sufficiently large simulation cells, but also the explicit treatment of long-range interactions and improved exchange-correlation functionals. Curie temperature ; Ferroelectricity ; Molecular dynamics ; Interatomic potential ; Ab initio ; Range (aeronautics) ; Curie Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Zhou Jiang | Mingming Xu | Jiayu Sun | Jinsong Leng
Source: Composite Structures (article, 2026-01-28)
Field: Materials Science — Polymers and Plastics

Rapid mechanical prediction of woven ceramic fabrics via a neural network surrogate model based on the parameterized unit cell Morphing ; Surrogate model ; Parametric statistics ; Artificial neural network ; Ceramic ; Ultimate tensile strength ; Scalability ; Shear (geology) Textile materials and evaluations ; Structural Analysis and Optimization ; Advanced Sensor and Energy Harvesting Materials
Authors: Denan Li | Chris Ahart | Shi Liu
Source: arXiv (Cornell University) (preprint, 2026-01-19)
Field: Materials Science — Materials Chemistry

Disentangling the Discrepancy Between Theoretical and Experimental Curie Temperatures in Ferroelectric PbTiO$_3$ Accurately predicting the Curie temperature ($T_c$) of ferroelectrics from first principles remains a major challenge, as theoretical estimates often fall significantly below experimental values. In this work, we investigate the origin of these discrepancies in the prototypical ferroelectric PbTiO$_3$ by performing extensive constant-pressure ab initio molecular dynamics (AIMD) simulations and benchmarking them against classical molecular dynamics (MD) using machine learning force fields (MLFFs) derived from first-principles data. Our results show that the underestimation of $T_c$ primarily stems from the limitations of the exchange-correlation functional, rather than inaccuracies in the MLFF fitting. We uncover a critical interplay between finite-size effects and the range of interatomic interactions: although short-range MLFFs appear to yield better agreement with experimental $T_c$, this improvement results from a fortuitous cancellation of errors. Incorporating explicit long-range interactions improves accuracy for larger supercells but ultimately leads to lower predicted $T_c$ values. These findings highlight that accurate finite-temperature predictions require not only high-quality training data and sufficiently large simulation cells, but also the explicit treatment of long-range interactions and improved exchange-correlation functionals. Curie temperature ; Curie temperature ; Ferroelectricity ; Ferroelectricity ; Molecular dynamics ; Molecular dynamics ; Interatomic potential ; Interatomic potential ; Ab initio ; Ab initio Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Vikas Jindal | Abhishek  Kumar Thakur
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Thermodynamically Constrained Machine-Learning Surrogates for Mapping Phase-Diagram Existence Domains in Ti–X Binary Systems CALPHAD ; Binary number ; Topology (electrical circuits) ; Parameter space ; Gaussian ; Network topology ; Gaussian process Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Edan T. Marcial | L. N. Chaudhary | Olesya Gorbunova | Aleksey N. Kolmogorov
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Performance of universal machine learning potentials in global optimization Rapid development of universal machine learning potentials (uMLPs) and expansion of training data sets are reshaping the state of the art in atomistic simulation, highlighting the need for concurrent systematic benchmarking of their capabilities. Global optimization is among the most demanding uMLP applications because unconstrained exploration includes probing motifs not present in reference sets. We examined the latest generation of uMLPs in unconstrained evolutionary searches to assess whether these models can consistently predict complex crystal structure ground states across diverse inorganic systems. Our findings demonstrate that the considered M3GNet, MACE, SevenNet, EquiformerV2, MatterSim, GRACE, eSEN, Orb-v3, and PET-MAD models span a wide performance range, from near ab initio to essentially non-predictive, in their ability to resolve competing phases within low-energy basins. Additional tests on hcp-Zn, MB$_4$ (M = Cr, Mn, and Fe), and LiB$_{y}$ ($y\approx 0.9$) ground states reveal that several uMLPs capture fine energy differences arising from subtle electronic structure features. Benchmarking ; Global optimization ; Energy minimization ; Ab initio ; State (computer science) ; Optimization problem Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Quantum many-body systems
Authors: Guosong Zhu | Xiaoyuan Li | Wei Pan | Wei Zhou | Heng Luo | Yue Hu | Xiaomeng Fan | Yang Li
Source: Journal of Materials Science Materials in Electronics (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic parameter prediction and multi-band absorption design of ZnO@YCO composite materials based on an SSA-GRNN-PSO data-driven framework Reflection loss ; Particle swarm optimization ; Bandwidth (computing) ; Composite number ; Electromagnetic radiation ; Inverse ; Crystallinity ; Reflection (computer programming) ; Absorption (acoustics) Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Irem ONER ALP | Bilgehan Barıs ONER | Ecem HELVACI | Cevat ACIKEL
Source: Journal of the European Optical Society Rapid Publications (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Negative-refractive-index behavior in a periodic photonic metamaterial: unit-cell topology tailoring for broadband response This paper examines how systematic control of unit-cell topology governs dispersion characteristics and bandwidth stability in configured periodic structures with numerical simulations. Distinct from the existing literature, the study targets a negative refractive index over a broad frequency range, while neural network–based optimization is used as a supporting tool to efficiently guide for bandwidth maximization. Comprehensive analysis is employed to optimize bandwidth effectively and validate the applicability of the resulting low-dispersion periodic structures. For the optimized unit cell, the third photonic band spans a wavelength range 1388–1631 nm (after model enhancement 1429–1578 nm) covering the S, C, and L optical communication bands, demonstrating broadband and stable dispersive behavior beyond a narrow resonance regime. The obtained results are thus expected to enhance the performance of negative-index based photonic components used in optical and photonic communication systems, compact antenna architectures, and advanced material engineering. Broadband ; Photonics ; Bandwidth (computing) ; Topology (electrical circuits) ; Optical communication ; Dispersion (optics) ; Refractive index ; Wavelength Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Xiaochuang Di | Zhong Zeng | Junyu Lu | Shenquan Yang | Xiaoke Shen | Yang Chen | huawei zou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sequence-Aware Inverse Design of Monolithic Gradient Microwave Absorbers: Overcoming Combinatorial Complexity via Transformer-Bayesian Synergy Interpretability ; Microwave ; Inverse ; Reflection (computer programming) ; Electrical impedance ; Discretization ; Absorption (acoustics) ; Artificial neural network ; Bayesian optimization Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Edan T. Marcial | L. N. Chaudhary | Olesya Gorbunova | Aleksey N. Kolmogorov
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Performance of universal machine learning potentials in global optimization Rapid development of universal machine learning potentials (uMLPs) and expansion of training data sets are reshaping the state of the art in atomistic simulation, highlighting the need for concurrent systematic benchmarking of their capabilities. Global optimization is among the most demanding uMLP applications because unconstrained exploration includes probing motifs not present in reference sets. We examined the latest generation of uMLPs in unconstrained evolutionary searches to assess whether these models can consistently predict complex crystal structure ground states across diverse inorganic systems. Our findings demonstrate that the considered M3GNet, MACE, SevenNet, EquiformerV2, MatterSim, GRACE, eSEN, Orb-v3, and PET-MAD models span a wide performance range, from near ab initio to essentially non-predictive, in their ability to resolve competing phases within low-energy basins. Additional tests on hcp-Zn, MB$_4$ (M = Cr, Mn, and Fe), and LiB$_{y}$ ($y\approx 0.9$) ground states reveal that several uMLPs capture fine energy differences arising from subtle electronic structure features. Benchmarking ; Global optimization ; Energy minimization ; Ab initio ; State (computer science) ; Optimization problem Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Quantum many-body systems