jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefanos Pertigkiozoglou | Mircea Petrache | Shubhendu Trivedi | Kostas Daniilidis
Source: arXiv (Cornell University) (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics that can hinder learning. Prior work addresses these limitations by relaxing strict equivariance during training, but typically relies on prespecified, explicit, or implicit target levels of relaxation for each network layer, which are task-dependent and costly to tune. We propose Recurrent Equivariant Constraint Modulation (RECM), a layer-wise constraint modulation mechanism that learns appropriate relaxation levels solely from the training signal and the symmetry properties of each layer's input-target distribution, without requiring any prior knowledge about the task-dependent target relaxation level. We demonstrate that under the proposed RECM update, the relaxation level of each layer provably converges to a value upper-bounded by its symmetry gap, namely the degree to which its input-target distribution deviates from exact symmetry. Consequently, layers processing symmetric distributions recover full equivariance, while those with approximate symmetries retain sufficient flexibility to learn non-symmetric solutions when warranted by the data. Empirically, RECM outperforms prior methods across diverse exact and approximate equivariant tasks, including the challenging molecular conformer generation on the GEOM-Drugs dataset. Equivariant map ; Relaxation (psychology) ; Constraint (computer-aided design) ; Symmetry (geometry) ; Homogeneous space ; Flexibility (engineering) ; Distribution (mathematics) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Domain Adaptation and Few-Shot Learning
Authors: Stefanos Pertigkiozoglou | Mircea Petrache | Shubhendu Trivedi | Kostas Daniilidis
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics that can hinder learning. Prior work addresses these limitations by relaxing strict equivariance during training, but typically relies on prespecified, explicit, or implicit target levels of relaxation for each network layer, which are task-dependent and costly to tune. We propose Recurrent Equivariant Constraint Modulation (RECM), a layer-wise constraint modulation mechanism that learns appropriate relaxation levels solely from the training signal and the symmetry properties of each layer's input-target distribution, without requiring any prior knowledge about the task-dependent target relaxation level. We demonstrate that under the proposed RECM update, the relaxation level of each layer provably converges to a value upper-bounded by its symmetry gap, namely the degree to which its input-target distribution deviates from exact symmetry. Consequently, layers processing symmetric distributions recover full equivariance, while those with approximate symmetries retain sufficient flexibility to learn non-symmetric solutions when warranted by the data. Empirically, RECM outperforms prior methods across diverse exact and approximate equivariant tasks, including the challenging molecular conformer generation on the GEOM-Drugs dataset. Equivariant map ; Relaxation (psychology) ; Constraint (computer-aided design) ; Symmetry (geometry) ; Homogeneous space ; Flexibility (engineering) ; Distribution (mathematics) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Domain Adaptation and Few-Shot Learning
Authors: Daniil Kargin | Yunpeng Lu
Source: The Journal of Chemical Physics (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

An 8-dimensional symmetry-adapted neural network potential energy surface for H2 dissociative chemisorption on hexagonal boron nitride <i>ortho</i> site with explicit surface atom motion We developed an 8D, site-specific, reduced-dimensionality potential energy surface (PES) for H2 dissociation at the ortho site of hexagonal BN, treating two surface atom degrees of freedom explicitly. To our knowledge, this is the first PES for gas-phase dissociation on a covalent surface with explicit treatment of surface atom motion and symmetry in a reduced-dimensionality approach. The PES was fitted to 16 164 PBE+D3 DFT data points with a mean absolute error of 17.00 meV using a symmetry-aware neural network incorporating atom permutational invariance and lattice space group symmetry. Convergence was validated via 2D site vibrational Schrödinger equation integration and classical MD trajectory prediction uncertainty analysis. Comparison of site-specific vibrations with the surface phonon spectrum revealed the limitations of the phonon approximation for covalent PES generation using EAM-thermal averaging methods. Classical trajectory analysis reveals that H-H bond elongation beyond 1.5 Å occurs at incident energies as low as 2.25 eV, which is below the 2.664 eV chemisorption barrier, suggesting BN surface mobility facilitates pre-dissociation. Our 8D model reproduces the barrier with 17% error (0.387 eV) compared to the full 108-dimensional model, while providing at least four orders of magnitude computational speedup compared to other surface models, enabling wavepacket dynamics calculations. This methodology provides a framework for future quantum dynamics studies on covalent 2D materials, using site vibrations to construct efficient discrete-variable representation basis sets. Potential energy surface ; Chemisorption ; Phonon ; Dissociation (chemistry) ; Molecular dynamics ; Surface phonon ; Covalent bond ; Molecular vibration ; Atom (system on chip) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Graphene research and applications
Authors: Artem Chuprov | Egor E. Nuzhin | Alexey A. Tsukanov | Nikolai V. Brilliantov
Source: arXiv (Cornell University) (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

Fast and accurate quasi-atom method for simultaneous atomistic and continuum simulation of solids We report a novel hybrid method of simultaneous atomistic simulation of solids in critical regions (contacts surfaces, cracks areas, etc.), along with continuum modeling of other parts. The continuum is treated in terms of quasi-atoms of different size, comprising composite medium. The parameters of interaction potential between the quasi-atoms are optimized to match elastic properties of the composite medium to those of the atomic one. The optimization method coincides conceptually with the online Machine Learning (ML) methods, making it computationally very efficient. Such an approach allows a straightforward application of standard software packages for molecular dynamics (MD), supplemented by the ML-based optimizer. The new method is applied to model systems with a simple, pairwise Lennard-Jones potential, as well with multi-body Tersoff potential, describing covalent bonds. Using LAMMPS software we simulate collision of particles of different size. Comparing simulation results, obtained by the novel method, with full-atomic simulations, we demonstrate its accuracy, validity and overwhelming superiority in computational speed. Furthermore, we compare our method with other hybrid methods, specifically, with the closest one -- AtC (Atomic to Continuum) method. We demonstrate a significant superiority of our approach in computational speed and implementation convenience. Finally, we discuss a possible extension of the method for modeling other phenomena. Molecular dynamics ; Pairwise comparison ; Software ; Work (physics) ; Basis (linear algebra) ; Composite number Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Chemical Physics Studies
Authors: Pan Liu | Yasuaki Watanabe | Yunlong Wu | Mitsuo Yamashita | Sakae Izumi | Xiangyu Zhong | Jun Chai | Qin-hao Zhang | Fa-He Cao | Yutaka Watanabe | Tetsuo Shoji
Source: Corrosion Science (article, 2026-02-03)
Field: Materials Science — Metals and Alloys

The dual and decoupled roles of ultra-low sulfur content in the stress corrosion cracking of a 17–4PH martensitic steel in geothermal environments Hydrogen embrittlement ; Corrosion ; Sulfur ; Sulfide ; Stress corrosion cracking ; Martensite ; Hydrogen ; Diffusion ; Polysulfide Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Petroleum Processing and Analysis
Authors: Katre Worth | Tiia Plamus | Jüri Majak
Source: Composite Structures (article, 2026-04-03)
Field: Materials Science — Polymers and Plastics

Design optimization of nonwovens made from mechanically recycled textile fibres Textile ; Flexural strength ; Ultimate tensile strength ; Abrasion (mechanical) ; Polylactic acid ; Flexural rigidity ; Bagasse ; Flexibility (engineering) Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Fiber-reinforced polymer composites
Authors: Jihyun Kim | JiYong Yoon | G. KIM | Kijeong Nam | Jaewon Heo | In Soo Kim | Dongjoon Rhee | Donghee Son | J. S. Kang
Source: Advanced Optical Materials (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Multilevel Optical Programming of Intrinsic Vacancies in Solution‐Processed MoS <sub>2</sub> Films for Retinomorphic Color Differentiation ABSTRACT Solution processing provides a scalable route to assemble 2D building blocks into large‐area films with low intersheet resistance, enabling scalable device integration. However, chalcogen vacancies introduced during processing often cause unintentional doping, and passivation strategies relying on strong reagents and controlled atmospheres increase process complexity. Here, rather than eliminating vacancies typically regarded as detrimental, we exploit vacancy‐localized states in solution‐processed MoS 2 as essential defects to realize retinomorphic device arrays in which sensing and memory are co‐localized within a two‐terminal device. Trapping of photoexcited carriers at these vacancy‐localized states enhances photogating and induces persistent photoconductivity, writing retentive conductance states without the need for additional trapping layers, floating gates, or complex heterostructures. The stored state is reversible upon oxygen exposure, which promotes de‐trapping and restores the dark baseline. Additionally, wavelength‐selective conductance modulation enables multilevel accumulation of color‐encoded weights for RGB differentiation. Increasing optical pulse number progressively enlarges inter‐color conductance contrast; when integrated with a convolutional neural network framework, the encoded states enable color recognition accuracy up to 94%. By exploiting a defect landscape inherent to solution‐processed 2D films, this work establishes a scalable materials platform that simultaneously integrates sensing and memory at the pixel level while reducing processing complexity. Scalability ; Conductance ; RGB color model ; Modulation (music) ; Passivation ; Process (computing) 2D Materials and Applications ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Ying Fang
Source: Monatshefte für Chemie - Chemical Monthly (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Advancements and prospects of carbon quantum dots-based sensors for organophosphorus pesticides detection Quantum sensor ; Carbon quantum dots ; Miniaturization ; Adaptability ; Quantum dot ; Debugging Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Qiwen Zhang | Haitao Wang
Source: Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Prediction of mechanical properties of cold-rolled steel under imbalanced sample conditions Deep learning-based prediction of the mechanical properties of cold-rolled strip steel has generally overlooked the issue of data imbalance that exists in real-world production environments. Moreover, the production process of cold-rolled steel is highly nonlinear, with its mechanical properties influenced by multi-scale couplings between chemical compositions and process parameters. Existing studies have insufficiently explored the complex interactions between these factors and their impact on mechanical behavior. To address this issue, this paper proposes a composite loss function that integrates effective label density and per-sample error, aiming to mitigate the effects of data imbalance in steel rolling tasks. Additionally, a Multi-scale Gated Attention-Driven Network (MGAD-Net) based on the composite loss is introduced for predicting the mechanical properties of cold-rolled strip steel. Dilated convolutions are employed for hierarchical extraction of heterogeneous process-composition features. A sparse parallel self-attention mechanism is also applied to precisely model the cross-process interaction effects in the rolling procedure, enabling comprehensive extraction of complex feature relationships. Compared with existing networks, the proposed MGAD-Net achieves prediction accuracies of 99.45%, 97.89%, and 99.02% for yield strength, tensile strength, and elongation, respectively, with corresponding improvements in R 2 scores of 1.46%, 1.31%, and 3.82%. Process (computing) ; Composite number ; Function (biology) ; Artificial neural network ; Yield (engineering) ; Strip steel ; Extraction (chemistry) ; Sample (material) ; Ultimate tensile strength Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Gabriela Theis Marchan | Toheeb O. Balogun | Kyle Territo | Debojyoti Das | Teslim Olayiwola | Revati Kumar | José A. Romagnoli
Source: Computational Materials Science (article, 2026-01-28)
Field: Chemistry — Organic Chemistry

Toward harnessing AI for surfactant chemistry: Prediction of critical micelle concentration Pulmonary surfactant ; Solvation ; Micelle ; Cluster analysis ; Complement (music) ; Graph ; Molecular dynamics ; Feature (linguistics) Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization ; Molecular spectroscopy and chirality
Authors: Ryan Liu | Eric Qu | Tobias Kreiman | Samuel M. Blau | Aditi S. Krishnapriyan
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we introduce the Bond Smoothness Characterization Test (BSCT). This efficient benchmark probes the PES via controlled bond deformations and detects non-smoothness, including discontinuities, artificial minima, and spurious forces, both near and far from equilibrium. We show that BSCT correlates strongly with MD stability while requiring a fraction of the cost of MD. To demonstrate how BSCT can guide iterative model design, we utilize an unconstrained Transformer backbone as a testbed, illustrating how refinements such as a new differentiable $k$-nearest neighbors algorithm and temperature-controlled attention reduce artifacts identified by our metric. By optimizing model design systematically based on BSCT, the resulting MLIP simultaneously achieves a low conventional E/F regression error, stable MD simulations, and robust atomistic property predictions. Our results establish BSCT as both a validation metric and as an "in-the-loop" model design proxy that alerts MLIP developers to physical challenges that cannot be efficiently evaluated by current MLIP benchmarks. Spurious relationship ; Smoothness ; Differentiable function ; Energy minimization ; Metric (unit) ; Benchmark (surveying) ; Potential energy ; Potential energy surface ; Interatomic potential ; Energy (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Shujun Cai | Jianfeng Fan | Jiahui Wang | Yabiao Ma | Yuanyuan Xiao | Siyuan Cheng | G. N. Lv | Xiaoxing Zeng | Jianbin Xu | Linlin Ren | Yagang Yao | Xiaoliang Zeng | Rong Sun
Source: Small (article, 2026-01-17)
Field: Materials Science — Materials Chemistry

Yield‐Stress Composite Fluids With High Thermal Conductivity and Tunable Rheology via Hierarchical Filler Architecture Yield-stress fluid can reversibly transform between the rigidity of a solid and the fluidity of a liquid, providing mechanical adaptability for its reprocessing and long-term stability. For thermal management applications, however, this adaptability must be coupled with high thermal conductivity-an objective hindered by the conventional trade-off between viscosity, and yield stress. Here, we propose a hierarchical filler architecture strategy by incorporating aluminum nitride particles of multiple sizes into a polydimethylsiloxane (PDMS) matrix to construct a multi-level dynamic network. Small particles fill the interstices between larger ones, creating dense, percolated thermal pathways while enabling reversible network breakdown and reconstruction under shear. This design achieves an unprecedented combination of low viscosity (731.3 Pa s at 10 s<sup>-1</sup>), tunable yield stress (from 37.3 to 802.3 Pa), and high thermal conductivity (12.0 W m<sup>-1</sup> K<sup>-1</sup>) in a yield-stress composite fluid. Comprehensive rheological characterization reveals excellent thixotropic recovery and mechanical robustness, while physics-informed neural network (PINN) modeling. When yielding stress fluid is used as a thermal interface material, the fluidity of liquid-like form can achieve efficient distribution, and the rigidity of solid-like form can achieve long-term reliability. These findings provide a new approach for designing advanced yield-stress fluids with controllable rheological properties and excellent thermal performance for high-performance thermal management applications. Rheology ; Thermal conductivity ; Thixotropy ; Viscoelasticity ; Thermal ; Composite number ; Polydimethylsiloxane ; Viscosity Thermal properties of materials ; Polymer Foaming and Composites ; Rheology and Fluid Dynamics Studies
Authors: Ke Jia | Duo Shen | Yanfang Zhao | Huihui Liu | Caiqiao Xiong | Xiangfeng Chen | Zongxiu Nie
Source: TrAC Trends in Analytical Chemistry (article, 2026-01-28)
Field: Chemistry — Spectroscopy

MALDI mass spectrometry in metabolic diagnostics: Advances in biofluid and spatial metabolic analysis Metabolic disease ; Profiling (computer programming) ; Mass spectrometry ; Metabolomics ; Metabolic pathway ; Proteomics ; Mass spectrometry imaging Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Bacterial Identification and Susceptibility Testing
Authors: Samuel A. Appiah | Matthew A. McDonald
Source: Crystal Growth & Design (article, 2026-03-16)
Field: Chemistry — Physical and Theoretical Chemistry

Property-Driven Cocrystal Discovery via Gaussian Processes and Active Learning Cocrystallization can be used to tune key drug properties, such as aqueous solubility, without altering molecular structure; however, the space of possible coformers is enormous and design rules are empirical. We present a Bayesian optimization framework that couples Gaussian process (GP) classification and regression that can accelerate cocrystal discovery and solubility enhancement. Starting from 6338 literature-derived binary coformer pairs, vector fingerprints that combine 2D structural information (fragment and MQN fingerprints) with low-cost shape and polarity descriptors were engineered for cocrystal prediction. A GP classifier, trained on an actively constructed training set of ∼1000 coformer pairs selected by uncertainty sampling achieves up to 94% accuracy and Matthews correlation coefficient of 0.79 on a test set of >5000 unseen pairs. Property-driven coformer selection was formulated as a Bayesian optimization problem, using a machine learning model as a surrogate for aqueous solubility and Tanimoto-similarity to guide campaigns across several discovery scenarios. In simulations, the framework rapidly identifies highly soluble cocrystals, typically recovering top-5 candidates after fewer than 10 evaluations. Finally, we validate the workflow experimentally with 12 pharmaceutical and pharmaceutical-like compounds, discovering two new cocrystals, resveratrol + praziquantel and purin-6-amine + thiazole-4-carboxylic acid, with markedly enhanced aqueous solubility. These results demonstrate a practical, data-efficient route to Bayesian optimization for cocrystal design. Cocrystal ; Gaussian process ; Bayesian probability ; Support vector machine ; Set (abstract data type) ; Gaussian ; Workflow ; Active learning (machine learning) ; Chemical space Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Alex Tai | Jason Ogbebor | Rodrigo Cassimiro de Freitas Freitas
Source: arXiv (Cornell University) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering Liquid metals are central to energy-storage and nuclear technologies, yet quantitative knowledge of their thermophysical properties remains limited. While atomistic simulations offer a route to computing liquid properties directly from atomic motion, the most accurate approach, ab initio molecular dynamics (AIMD), is computationally costly and restricted to short time and length scales. Machine learning interatomic potentials (MLPs) offer AIMD accuracy at far lower cost, but their application to liquids is limited by training datasets that inadequately sample atomic configurations, leading to unphysical force predictions and unstable trajectories. Here we introduce a physically motivated dataset-engineering strategy that constructs liquidlike training data synthetically rather than relying on AIMD configurations. The method exploits the established icosahedral short-range order of metallic liquids, twelvefold, near-close-packed local coordination, and generates "synthetic-liquid" structures by systematic perturbation of crystalline references. MLPs trained on these datasets close the sampling gaps that lead to unphysical predictions, remain numerically stable across temperatures, and reproduce experimental liquid densities, diffusivities, and melting temperatures for multiple elemental metals. The framework links atomic-scale sampling to long-term MD stability and provides a practical route to predictive modeling of liquid-phase thermophysical behavior beyond the limits of direct AIMD. Stability (learning theory) ; Interatomic potential ; Molecular dynamics ; Icosahedral symmetry ; Perturbation (astronomy) ; Perturbation theory (quantum mechanics) ; Ab initio ; Sample (material) ; Sampling (signal processing) Machine Learning in Materials Science ; Material Dynamics and Properties ; Block Copolymer Self-Assembly