jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stephen Kronenberger (19440828) | Arthi Jayaraman (629537)
Source: Figshare (article, 2025-06-23)
Field: Chemistry — Organic Chemistry

Effect ofMethylcellulose Chain Design on Gelationand Fibril Structure Using Coarse-Grained Modeling Aqueous solutions of methylcellulose (MC) undergo thermoreversible gelation making them useful as additives for formulations with desired rheological properties. This gelation occurs at elevated temperatures and concurrently with the formation of stiff, high aspect ratio fibrils composed of several MC chains. The sol–gel transition temperature as well as the fibrillar structure of the gel state can be controlled by the molecular weight of the MC chains, MC concentration, and the design of the MC chains. One such MC design parameter is the degree of substitution (DS) which varies between 0 (cellulose) to 3.0 (fully methylated cellulose chain). The DS of commercially available MC (e.g., International Flavors and Fragrances’ Methocel A) is ∼1.8. The DS as well as the pattern of methylation (i.e., homogeneous or heterogeneous substitution) along the MC backbone can be tailored using variations in the synthesis protocols. To elucidate the effect of these MC chain design parameters on the sol–gel transition and the fibrillar structure of the MC chains, we conducted a multiscale computational study. First, we developed a coarse-grained (CG) model for MC with the model’s bonded parameters informed from atomistic simulations using Boltzmann inversion and nonbonded parameters informed from previous experimental studies of aqueous MC sol–gel phase behavior using surrogate Bayesian optimization. We performed molecular dynamics (MD) simulations of aqueous MC solutions with this CG model and characterized the effect of DS and methylation pattern on the gelation temperature and the MC chains’ fibril structure, specifically fibril diameter and persistence length. Fibril diameters and persistence lengths in our simulated systems agree well with those found from previous small-angle scattering analysis and electron microscopy measurements, providing validation for the results from the CG MD simulations. Using these CG MD simulations, we span a large MC design parameter space and identify these key design rules: (i) Aqueous solutions with MC chains having larger heterogeneity in the DS along the chain backbone, will gel at lower temperatures and assemble into fibrils with a smaller diameter. (ii) As the DS becomes “blockier” along the MC backbone, gelation will occur at lower temperatures, and fibril diameters will decrease. We expect our model development and the above design rules will aid future work focused on synthesis and/or simulation of MC with tailored gel properties for desired applications. Rheology ; Fibril ; Molecular dynamics ; Aqueous solution ; Persistence length ; Homogeneous ; Phase (matter) ; Chain (unit) Surfactants and Colloidal Systems ; Hydrogels: synthesis, properties, applications ; Advanced Cellulose Research Studies
Authors: Ivan V. Grenev | Matvey E. Bobkov | A. Ivanov | Anastasiia I. Uliankina | Aleksandr A. Shubin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Based Predicting of the Equilibrium Cation Distribution in Faujasite-Type Zeolites Faujasite ; Type (biology) ; Distribution (mathematics) ; Mathematics ; Mathematical economics ; Chemistry ; Zeolite ; Geology ; Mathematical analysis ; Catalysis ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ivan V. Grenev | Matvey E. Bobkov | A. Ivanov | Anastasiia I. Uliankina | Aleksandr A. Shubin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning-based predicting of the equilibrium cation distribution in faujasite-type zeolites Faujasite ; Type (biology) ; Distribution (mathematics) ; Mathematical economics ; Chemistry ; Thermodynamics ; Economics ; Mathematics ; Zeolite ; Physics ; Mathematical analysis ; Catalysis ; Geology ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Geochemistry and Geologic Mapping
Authors: Jichen Yang | Han Ma | Qiang Chen
Source: Journal of Physics Conference Series (article, 2025-06-01)
Field: Materials Science — General Materials Science

Research on the design of low thermal expansion sandwich panels for metallic thermal protection structures Abstract Metallic thermal protection structures used in high-Mach-number aircraft face thermal matching issues in high-temperature operating environments. In this paper, a sandwich panel structure with low thermal expansion properties is proposed to address this problem by adopting a concave orthogonal combination design of double materials in the core layer, which is connected to the panel through I-beams. First, the thermal deformation mechanism of the core unit of the structure is analyzed theoretically. Then, an optimization design method based on Bayesian optimization theory is proposed, where both equivalent areal density and structural safety are used as the optimization constraints, and the minimization of thermal deformation is set as the optimization objective. The structure is optimized based on these criteria. The results show that, compared to traditional honeycomb sandwich panels, the proposed structure exhibits better low thermal expansion performance. The proposed optimization method demonstrates good convergence, and the optimized structure reduces the maximum thermal deformation by 28% compared to the traditional honeycomb sandwich panel. Thermal ; Materials science ; Thermal expansion ; Thermal protection ; Sandwich-structured composite ; Structural engineering ; Composite material ; Engineering ; Physics ; Composite number Material Properties and Applications
Authors: Igor A. Rodrigues Lopes | A. Francisca Carvalho Alves | Nathan Klavzer | Thomas Pardoen | P.P. Camanho
Source: Composite Structures (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Pressure-dependent strain gradient plasticity for micro-mechanical analyses of fibre-reinforced polymers A new constitutive model for epoxy resins is proposed to accurately capture the micro-scale strain fields in fibre-reinforced composites. The model extends conventional plasticity by introducing strain-gradient effects through an implicit gradient formulation that accounts for pressure sensitivity and asymmetric tension/compression behaviour–critical features to represent the mechanical response of epoxies at small scales. The formulation is implemented in a commercial finite element platform through user-defined subroutines and solved via an analogy with coupled thermo-mechanical problems that include Helmholtz-type equations. The ability of the model to predict the size-dependent response and to prevent unrealistic overpredictions of strain magnitude is illustrated through the simulation of nanoindentation on neat epoxy and of the local strain field behaviour in a composite. The results are compared with experimental data, which are also used to calibrate the additional constitutive parameters through a composite Bayesian optimization strategy. The proposed framework significantly improves the predictive capabilities of micromechanical models for composite materials by incorporating scale-dependent plasticity mechanisms. Plasticity ; Finite element method ; Epoxy ; Constitutive equation ; Nanoindentation ; Sensitivity (control systems) ; Subroutine ; Composite number Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Elasticity and Material Modeling
Authors: Tyler B. Martin (4099345) | Duncan R. Sutherland (21499743) | Austin McDannald (7301588) | A. Gilad Kusne (6618599) | Peter A. Beaucage (2565211)
Source: Figshare (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

AutonomousSmall-Angle Scattering for AcceleratedSoft Material Formulation Optimization The pace of soft material formulation (re)­development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment while maintaining high standards of performance. To meet this need, we have developed the Autonomous Formulation Lab (AFL), a platform that can automatically prepare and measure the microstructure of liquid formulations using small-angle neutron and X-ray scattering and, soon, a variety of other techniques. Here, we describe the design, philosophy, tuning, and validation of our active learning agent that guides the course of AFL experiments. We show how our extensive <i>in silico</i> tuning results in an efficient agent that is robust to both the number of measurements and signal-to-noise variation. Finally, we experimentally validate our virtually tuned agent by addressing a model formulation problem: replacing a petroleum-derived component with a natural analog. We show that the agent efficiently maps both formulations and how post hoc analysis of the measured data reveals the opportunity for further specialization of the agent. With the tuned and proven active learning agent, our autonomously guided AFL platform will accelerate the pace of discovery of liquid formulations and help speed us toward a greener future. Component (thermodynamics) ; Variety (cybernetics) ; Measure (data warehouse) ; Pace ; Active learning (machine learning) ; Overhead (engineering) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Mathematical Modeling in Engineering
Authors: Ignacio Fdez. Galván (7450859) | Daniel Weßling (22801603) | Roland Lindh (1390196)
Source: Figshare (article, 2025-12-08)
Field: Chemistry — Organic Chemistry

Accelerating SCFOrbital Optimization with S‑GEK/RVO:Efficient Subspace Compression and Robust Convergence We present enhancements to the S-GEK/RVO method for self-consistent field (SCF) orbital optimization, aimed at improving computational efficiency and robustness. Building on a gradient-enhanced Kriging surrogate model and restricted-variance optimization, we introduce three key modifications: (i) a cost-effective subspace expansion using r-GDIIS or BFGS displacement predictions, (ii) a systematic undershoot mitigation strategy in flat energy regions, and (iii) rigorous coordinate and gradient transformations consistent with the exponential parametrization of orbital rotations. Benchmarking across an extensive set of molecular systemsincluding organic molecules, radicals, and transition-metal complexesdemonstrates that the new S-GEK/RVO variants consistently outperform the default (in OpenMolcas) r-GDIIS method in iteration count, convergence reliability, and wall time. These improvements make S-GEK/RVO a competitive alternative for SCF optimization and suggest broader applicability to other orbital optimization and localization problems. Convergence (economics) ; Subspace topology ; Parametrization (atmospheric modeling) ; Set (abstract data type) ; Exponential function ; Field (mathematics) ; Displacement (psychology) ; Optimization problem ; Krylov subspace Synthesis and Properties of Aromatic Compounds ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Takeshi Hara | Akira Kusaba | Yoshihiro Kangawa | Tetsuji Kuboyama | David R. Bowler | Karol Kawka | Paweł Kempisty
Source: UCL Discovery (University College London) (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Exploration of Stable Atomic Configurations in Graphene-like BCN Systems by Density Functional Theory and Bayesian Optimization h-BCN is an intriguing material system where the bandgap varies considerably depending on the atomic configuration, even at a fixed composition. Exploring stable atomic configurations in this system is crucial for discussing the energetic formability and controllability of desirable configurations. In this study, this challenge is tackled by combining first-principles calculations with Bayesian optimization. An encoding method that represents the configurations as vectors, while incorporating information about the local atomic environments and domain knowledge, is proposed for the search. The proposed encoding method proved effective in the search, resulting in the discovery of two interesting and stable semiconductor configurations (zigzag-type and armchair-type stripe configurations). Furthermore, the optimization behavior is discussed through principal component analysis, confirming that the ordered BN network and the C configuration features are well embedded in the search space. While previous studies have investigated a limited number of empirically prepared h-BCN configurations, this study enables the efficient exploration of stable configurations from a vast number of candidates. Our approach is expected to be systematically applicable to various compositions and periodicities, contributing to the design of atomic configurations for semiconductor device applications of h-BCN. Graphene ; Density functional theory ; Bayesian optimization ; Bayesian probability ; Materials science ; Nanotechnology ; Computer science ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Artificial intelligence Graphene research and applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Taichi Hara (46246) | Akira Kusaba (22039487) | Yoshihiro Kangawa (22039490) | Tetsuji Kuboyama (22039493) | David Bowler (22039496) | Karol Kawka (22039499) | Pawel Kempisty (22039502)
Source: Figshare (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Exploration of Stable Atomic Configurations in Graphene-likeBCN Systems by Density Functional Theory and Bayesian Optimization h-BCN is an intriguing material system where the bandgap varies considerably depending on the atomic configuration, even at a fixed composition. Exploring stable atomic configurations in this system is crucial for discussing the energetic formability and controllability of desirable configurations. In this study, this challenge is tackled by combining first-principles calculations with Bayesian optimization. An encoding method that represents the configurations as vectors, while incorporating information about the local atomic environments and domain knowledge, is proposed for the search. The proposed encoding method proved effective in the search, resulting in the discovery of two interesting and stable semiconductor configurations (zigzag-type and armchair-type stripe configurations). Furthermore, the optimization behavior is discussed through principal component analysis, confirming that the ordered BN network and the C configuration features are well embedded in the search space. While previous studies have investigated a limited number of empirically prepared h-BCN configurations, this study enables the efficient exploration of stable configurations from a vast number of candidates. Our approach is expected to be systematically applicable to various compositions and periodicities, contributing to the design of atomic configurations for semiconductor device applications of h-BCN. Controllability ; Bayesian optimization ; Encoding (memory) ; Domain (mathematical analysis) ; Stability (learning theory) ; Component (thermodynamics) ; Bayesian probability ; Density functional theory ; Principal component analysis Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Marie Louise Schubert | Houssam Metni | Jan David Fischbach | Benedikt Zerulla | Marjan Krstić | Ulrich W. Paetzold | Seyedamir Orooji | Olivier J. J. Ronsin | Yasin Ameslon | Jens Harting | Thomas Kirchartz | Sandheep Ravishankar | Chris Dreeßen | Eunchi Kim | Christian Sprau | M. S. Hussein | Alexander Colsmann | Karen Forberich | Klaus Jäger | Pascal Friederich | Carsten Rockstuhl
Source: ArXiv.org (preprint, 2025-12-02)
Field: Materials Science — Materials Chemistry

Towards a fully differentiable digital twin for solar cells Maximizing energy yield (EY) - the total electric energy generated by a solar cell within a year at a specific location - is crucial in photovoltaics (PV), especially for emerging technologies. Computational methods provide the necessary insights and guidance for future research. However, existing simulations typically focus on only isolated aspects of solar cells. This lack of consistency highlights the need for a framework unifying all computational levels, from material to cell properties, for accurate prediction and optimization of EY prediction. To address this challenge, a differentiable digital twin, Sol(Di)$^2$T, is introduced to enable comprehensive end-to-end optimization of solar cells. The workflow starts with material properties and morphological processing parameters, followed by optical and electrical simulations. Finally, climatic conditions and geographic location are incorporated to predict the EY. Each step is either intrinsically differentiable or replaced with a machine-learned surrogate model, enabling not only accurate EY prediction but also gradient-based optimization with respect to input parameters. Consequently, Sol(Di)$^2$T extends EY predictions to previously unexplored conditions. Demonstrated for an organic solar cell, the proposed framework marks a significant step towards tailoring solar cells for specific applications while ensuring maximal performance. Workflow ; Differentiable function ; Consistency (knowledge bases) ; Solar energy ; Photovoltaics ; Photovoltaic system ; Solar cell Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Nanowire Synthesis and Applications
Authors: Utkarsh Bhardwaj | Vinayak Mishra | M. Warrier
Source:  (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Kanad: An HPC Framework for Large–Scale Machine–Learned Interatomic Potentials Machine-learned interatomic potentials (MLIPs) offer near-DFT accuracy at classical molecular dynamics computational cost, yet developing accurate, robust potentials for largescale simulations remains challenging. We present Kanad, an integrated HPC framework addressing the complete MLIP development lifecycle through five specialized modules: configuration generation with complex defect structures, diversity-based selection and outlier detection, automated DFT workflow management across VASP and Quantum ESPRESSO, multi-architecture MLIP training with MPI-enabled hyperparameter optimization, and comprehensive elastic, thermal and defect property validation that scales up to hundreds of cores. Submission, analysis, collection, and visualization are handled through a Python API. Key innovations include multi-objective optimization balancing fitting accuracy against physical properties, unified cross validation training for different MLIP feature descriptors and architectures, unified ZBL integration for close range atomic interactions across MLIP architectures, unified hierarchical active learning that does intelligent configuration selection for fine-tuning, and natural-language interfaces for workflow construction. The framework achieves high HPC utilization through parallel job management, enabling automated development and validation of MLIPs efficiently on a HPC environment. We demonstrate capabilities through SNAP and MTP potentials for niobium and tungsten, achieving sub- $2 \%$ error in elastic constants and accurate radiation damage predictions. The modular, HPCoptimized architecture makes systematic MLIP development accessible across diverse material systems. Workflow ; Python (programming language) ; Hyperparameter ; Visualization ; Interatomic potential ; Supercomputer ; Outlier ; Graphical user interface ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Model Reduction and Neural Networks
Authors: Isaac Onyango | Qiang Zhu
Source: The Journal of Physical Chemistry C (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Direct Deoxygenation of Phenol over Fe-Based Bimetallic Surfaces Using On-the-Fly Surrogate Models We present an accelerated nudged elastic band (NEB) study of phenol direct deoxygenation (DDO) on Fe-based bimetallic surfaces using a recently developed Gaussian process regression (GPR) calculator. Our test calculations demonstrate that the GPR calculator achieves up to 3 times speedup compared to conventional density functional theory calculations while maintaining high accuracy, with energy barrier errors below 0.015 eV. Using GPR-NEB, we systematically examine the DDO mechanism on pure Fe(110) and surfaces modified with Co and Ni in both top and subsurface layers. Our results show that subsurface Co and Ni substitutions preserve favorable thermodynamics and kinetics for both C-O bond cleavage and C-H bond formation, comparable to those on the pure Fe(110) surface. In contrast, top-layer substitutions generally increase the C-O bond cleavage barrier, render the step endothermic, and result in significantly higher reverse reaction rates, making DDO unfavorable on these surfaces. This work demonstrates the effectiveness of GRR-accelerated transition state searches for complex surface reactions and provides insights into rational design of bimetallic catalysts for selective deoxygenation. Deoxygenation ; Bimetallic strip ; Catalysis ; Density functional theory ; Bond cleavage ; Kinetics ; Phenol ; Work (physics) Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Environmental remediation with nanomaterials
Authors: Goswami, Rohit
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Two-dimensional RMSD projections for reaction path visualization and validation Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involves trajectories conventionally plotted in terms of the relative energy to the initial state against a cumulative displacement variable, or the image number. These dimensional reductions obscure structural rearrangements in high dimensions and are often history dependent. This precludes the ability to compare optimization histories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mapping trajectories onto a two-dimensional projection defined by a permutation corrected root mean square deviation from the reactant and product configurations. Energy is represented as an interpolated color-mapped surface constructed from all optimization steps using a gradient-enhanced Gaussian Process with the inverse multiquadric kernel, whose posterior variance contours delineate data-supported regions from extrapolated ones. A rotated coordinate frame decomposes the RMSD plane into reaction progress and orthogonal distance. We show the utility of the framework on a cycloaddition reaction, where a machine-learned potential saddle and density functional theory reference lie on comparable energy contours despite geometric displacements, along with the ratification of the visualization for more complex reactions, a Grignard rearrangement, and a conrotatory bicyclobutane ring opening. Visualization ; Projection (relational algebra) ; Gaussian ; Energy minimization ; Path (computing) ; Permutation (music) ; Intersection (aeronautics) ; Saddle point ; Piecewise Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mohammad S. Khorrami | Pawan Goyal | Jaber Rezaei Mianroodi | Bob Svendsen | Peter Benner | Dierk Raabe
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Physics-Encoded Fourier Neural Operator Approach for Surrogate Modeling of Divergence-Free Stress Fields in Solids Divergence (linguistics) ; Fourier transform ; Operator (biology) ; Physics ; Statistical physics ; Surrogate model ; Stress (linguistics) ; Mathematics ; Mathematical optimization ; Quantum mechanics ; Biology ; Genetics Machine Learning in Materials Science