jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akshat Shirish Zalte | Hao‐Wei Pang | Anna C. Doner | William H. Green
Source: ChemRxiv (preprint, 2025-03-05)
Field: Materials Science — Materials Chemistry

RIGR: Resonance Invariant Graph Representation for Molecular Property Prediction Graph neural networks, which rely on Lewis structure representations, have emerged as a powerful tool for predicting molecular and reaction properties. However, a key limitation arises with molecules exhibiting resonance, where multiple valid Lewis structures represent the same species. This causes inconsistent predictions for the same molecule based on the chosen resonance form in common property prediction frameworks like Chemprop, which implements a directed message-passing neural network (D-MPNN) architecture on the input molecular graph. To address this issue of resonance variance, we introduce the Resonance Invariant Graph Representation (RIGR) of molecules that ensures, by construction, that all resonance structures are mapped to a single representation, eliminating the need to choose from or generate multiple resonance structures. Implemented with the D-MPNN architecture, RIGR is evaluated on a large dataset with resonance-exhibiting radicals and closed-shell molecules, comparing it against the Chemprop featurizer. Using 60% fewer features, RIGR demonstrates comparable or superior prediction performance. Alternative approaches, such as data augmentation with resonance forms, are assessed, and their limitations are explored. Available open-source as an optional featurization scheme in Chemprop, RIGR is benchmarked across a wide range of property prediction tasks, showcasing its potential as a general graph featurizer beyond resonance handling. Computer science ; Resonance (particle physics) ; Invariant (physics) ; Graph ; Representation (politics) ; Molecular graph ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Atomic physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Amin, Abdelrahman | Ibrahim, Hamdy | Awad, Ibrahim
Source: UTC Scholar (University of Tennessee at Chattanooga) (article, 2025-10-16)
Field: Materials Science — Biomaterials

Innovative Machine Learning Approach for Predicting the In Vitro MAO Coated Magnesium-Based Substrate The increasing number of orthopedic surgeries due to various reasons has driven demand for improved implant materials beyond conventional Ti–Ni alloys, which can cause stress shielding and other complications. Magnesium-based implants have gained attention due to their biocompatibility, bone-like mechanical properties, and controllable degradation rates using different approaches. Micro-arc oxidation (MAO) offers an effective means to enhance corrosion resistance by forming a protective ceramic layer, with process parameters significantly influencing degradation behavior. This study presents a machine learning approach to predict the in vitro corrosion performance of MAO-coated magnesium implants, enabling efficient optimization of process parameters and improved implant design. Corrosion ; Substrate (aquarium) ; Degradation (telecommunications) ; Ceramic ; Coating ; Process (computing) Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty
Authors: Artem Bezrukov | А. И. Галеева | Aleksandr S. Krupin | Yu. G. Galyametdinov
Source: Nanomaterials (article, 2025-11-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intermolecular Organization of a Lyotropic Liquid Crystal and Carbon Dot Composite in Microfluidic Channels: Surface and Dynamic Effects Composites of lyotropic liquid crystals with biocompatible luminescent nanoparticles represent multifunctional materials with high potential for application in molecular diagnostics and biomedicine. Their integration with microfluidics is a new and scarcely studied approach that offers unique opportunities for tuning properties of such nanomaterials and simulating the biological environment of their application. This paper analyzes the impact of the governing microfluidic factors, including wall effects and flow dynamics, on the intermolecular structure and optical properties of the mesogenic luminescent nanocomposite of tetraethylene glycol monododecyl ether and carbon dots. The nanoscale and microscale surface structure of microchannel walls was found to be the dominating factor for additional near-wall ordering of the intrinsic lamellar structure of the composite. A combination of controlled shear stress with heating-cooling cycles allowed for a gradual and reversible transformation of multilamellar vesicles into the axial lamellar structure, and provided the composite with anisotropic luminescence capabilities according to the study of the luminescent behavior of carbon dots. The collected experimental datasets, comprising hundreds of texture images, allowed for training the neural network for subsequent accurate recognition of the composite nanoscale organization and dynamic properties in straight and serpentine microchannels. The results will contribute to developing AI-powered microfluidic chips with integrated biocompatible nanocomposite materials for testing drug delivery systems and simulating biological capillary environment in organ-on-chip prototypes. Lyotropic ; Nanocomposite ; Microfluidics ; Lamellar structure ; Composite number ; Liquid crystal ; Luminescence ; Carbon nanotube Liquid Crystal Research Advancements ; Pickering emulsions and particle stabilization ; Advanced Materials and Mechanics
Authors: Saira Saeed Mirza | Yanuar Simon
Source: Clean energy and sustainability (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Performance Evaluation of Machine Learning Algorithms for Predicting Organic Photovoltaic Efficiency This study forecasts the power conversion efficiency (PCE) of organic solar cells using data from experiments with donors and non-fullerene acceptor materials. We built a dataset that includes both numerical and categorical features by using standard scaling and one-hot encoding. We developed and compared several machine learning (ML) models, including multilayer perceptron, random forest, XGBoost, multiple linear regression, and partial least squares. The modified XGBoost model performed best, achieving a root mean squared error (RMSE) of 0.564, a mean absolute error (MAE) of 0.446, and a coefficient of determination (R2) of 0.980 on the test set. We also assessed the model’s ability to generalize and its reliability by examining learning curve trends, calibration curve analysis, and residual distribution. Plots of feature correlation and permutation importance showed that ionization potential and electron affinity were key predictors. The results demonstrate that with proper tuning, gradient boosting methods can provide highly accurate and easy-to-understand predictions of organic solar cell efficiency. This work establishes a repeatable machine learning process to quickly screen and thoughtfully design high-efficiency photovoltaic materials. Categorical variable ; Gradient boosting ; Photovoltaic system ; Boosting (machine learning) ; Random forest ; Residual ; Mean squared error ; Resampling ; Mean absolute percentage error Machine Learning in Materials Science ; Photovoltaic System Optimization Techniques ; Organic Electronics and Photovoltaics
Authors: Prassana Chandan | Amiya Prakash Das | Shakti Swaroop Choudhury | Ratna Kumar Annabattula
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

A DEM-driven machine learning framework for abrasive wear prediction Particle-induced wear is a critical concern in bulk material handling systems, where abrasive interactions accelerate equipment degradation, increase maintenance needs, and raise operational costs. The Discrete Element Method (DEM) and Archard's wear model are widely adopted for predicting particle-surface wear processes. However, DEM is computationally prohibitive for real-time design and predictive maintenance, often requiring hours to days for a single parametric analysis. We propose a DEM-machine learning (ML) framework to address this limitation that combines physics-based simulations with data-driven efficiency. A dataset of 200 DEM simulations is generated by systematically varying particle size, material, and contacting plate geometric parameters. A few ML models -- linear regression, Lasso and Ridge regularization, decision trees, and a genetic algorithm-optimized artificial neural network (GA-ANN) -- were trained and evaluated. Feature selection revealed that Archard's wear constant, particle size, plate angle, and impingement velocity are the dominant predictors of wear. While linear models offered interpretability, their accuracy was limited. The GA-ANN achieved the highest performance $(R^2 = 0.91)$, effectively capturing nonlinear wear dynamics while reducing computational cost by orders of magnitude. This study demonstrates that physics-informed ML provides a scalable pathway for accurate, real-time wear prediction, enabling predictive maintenance and optimized design in bulk material handling industries. Artificial neural network ; Abrasive ; Parametric statistics ; Nonlinear system ; Feature selection ; Feature (linguistics) ; Tool wear ; Particle (ecology) ; Finite element method Metal Alloys Wear and Properties ; Erosion and Abrasive Machining ; Lubricants and Their Additives
Authors: Francesc Sabanés Zariquiey | Stephen E. Farr | Stefan H. Doerr | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2025-01-03)
Field: Materials Science — Materials Chemistry

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we validate relative binding free energy (RBFE) accuracy using neural network potentials (NNPs) for the ligands. We utilize a novel NNP model, AceFF 1.0, based on the TensorNet architecture for small molecules that broadens the applicability to diverse drug-like compounds, including all important chemical elements and supporting charged molecules. Using established benchmarks, we show overall improved accuracy and correlation in binding affinity predictions compared with GAFF2 for molecular mechanics and ANI2-x for NNPs. Slightly less accuracy but comparable correlations with OPLS4. We also show that we can run the NNP simulations at 2 fs timestep, at least two times larger than previous NNP models, providing significant speed gains. The results show promise for further evolutions of free energy calculations using NNPs while demonstrating its practical use already with the current generation. The code and NNP model are publicly available for research use. Artificial neural network ; Energy (signal processing) ; Deep neural networks ; Computer science ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Satya Kokonda
Source:  (article, 2025-09-06)
Field: Chemistry — Inorganic Chemistry

Computational Design of Complex Metal-Organic Frameworks Using Machine Learning Many crucial processes remain too complex for computational modeling, requiring extensive experimentation to identify promising materials. This work presents a generalizable methodology for application-specific material design, using photocatalysis as a case study. Metal-Organic Frameworks (MOFs), known for their tunable structures are used in a variety of these ‘unmodellable’ applications. Reinforcement learning generated 60,000 MOFs optimized for CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> Heat of Adsorption. A predictor funnel system was developed to iteratively eliminate lowscoring candidates, reducing the pool to 6,329 and improving computational efficiency by 355%. Trained Crystal Graph Convolutional Neural Network (CGCNN) models predicted material properties used to construct a multi-objective fitness function incorporating stability, catalytic efficiency, material cost, sustainability, adsorption performance and industrial usage criteria. This function enables computational modeling of photocatalytic performance and identified two especially promising MOFs, each meeting extensive synthesis criteria. A Cr-based MOF achieved a photocatalytic score 239% higher than the control, and a Mnbased MOF outperformed the best control across all relevant metrics, demonstrating robustness even under fitness function variability. In total, $\mathbf{1 0}$ MOFs exceeded the control’s performance by over 100%. Analysis revealed key structural features, such as the positive influence of metal cluster N131, enabling future targeted exploration. This work demonstrates the potential to design viable materials for previously unmodellable processes, including drug delivery and photothermal therapy. Robustness (evolution) ; Reinforcement learning ; Artificial neural network ; Computational model ; Graph ; Convolutional neural network ; Function (biology) ; Cluster analysis Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Daniel Hedman
Source: arXiv (Cornell University) (preprint, 2025-12-31)
Field: Materials Science — Materials Chemistry

Vapor-solid-solid growth of single-walled carbon nanotubes Single-walled carbon nanotubes (SWCNTs) are promising for nanoscale electronics and photonics, but practical deployment requires chirality control. Most catalytic chemical vapor deposition (CCVD) growth of SWCNTs proceeds on liquid metal nanoparticles via a vapor-liquid-solid (VLS) mechanism and yields broad chirality distributions, whereas improved selectivity has been reported for high-melting-point crystalline catalysts, suggesting vapor-solid-solid (VSS) growth. However, the atomistic mechanism and kinetic of VSS SWCNT growth remain unclear. Here it is shown, using machine-learning interatomic potential-driven molecular dynamics on rhenium nanoparticles, that VSS growth is diffusion-limited and governed by facet-dependent surface carbon transport coupled to carbon-driven facet reconfiguration without catalyst melting. Surface diffusion is up to $\sim 50\times$ slower than carbon diffusion in liquid iron, imposing a strict upper bound on sustainable carbon supply and producing a narrow growth window: insufficient transport drives carbon accumulation and multiple nucleation, whereas higher temperatures favor graphitic encapsulation. In contrast to defect-free VLS growth, defects persist, indicating slow defect healing, and equilibrium simulations reveal suppressed edge configurational entropy with stabilization of zigzag-rich, Klein-decorated edges. Together, these results establish facet evolution and surface diffusion as joint regulators of diffusion-limited VSS growth and motivate stringent control of temperature and carbon supply. Carbon nanotube ; Surface diffusion ; Carbon fibers ; Chemical vapor deposition ; Catalysis ; Molecular dynamics ; Diffusion ; Chirality (physics) ; Nanoparticle Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Zhipeng Luo | Peng Cai | Wenhui Chen | Jiaxing Chen | Shujun Cai | H. Huang | Zixin Liu | Hefeng Zhang | Yifu Huang
Source: Journal of Applied Polymer Science (article, 2025-10-20)
Field: Materials Science — Polymers and Plastics

Ultra‐Low Doping of Frozen/Thawed Polyvinyl Alcohol Ionogels With Benzimidazole‐Linked <scp>2D</scp> Polymer Nanosheets for Enhanced Photoluminescence ABSTRACT Applying a nonchemical approach of polymer crystallizing for altering micro‐conformations of a nonconjugated linear polymer can promisingly achieve enhanced photoluminescence without sacrificing mechanical properties of the matrix. Herein, to pursue an excellent nucleating agent for tuning the crystallization micro‐conformation, a new type of two‐dimensional polymer nanosheet (2DP) with benzimidazole‐linked structure, with high specific surface area and unique shape morphology, was synthesized for frozen/thawed (F/T) polyvinyl alcohol (PVA)‐based ionogel, which could provide an efficient way to significantly enhance the fluorescence of PVA by inducing crystalline conformation without changing chain topology. Notably, incorporating a mere ~0.05 wt.% of 2DP elevated the quantum yield of the PVA ionogel from 9.2% to an impressive 85.7%. Besides, at a higher 2DP doping of ~0.7 wt.%, a distinct afterglow effect originating from 2DP was also observed. It was revealed for the first time that the F/T cycle during the preparation of the PVA ionogel could effectively increase the photoluminescent intensity, mainly ascribed to PVA but not 2DP. The fluorescence enhancement within the ionogel could be predicted by quantum chemistry calculations and a deep learning model. This new ionogel shows great potential as smart luminescent polymer for flexible lampshade. Photoluminescence ; Polyvinyl alcohol ; Polymer ; Quantum yield ; Doping ; Nanosheet Conducting polymers and applications ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Zhouping Xin | Yang Zhong | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2025-01-03)
Field: Materials Science — Materials Chemistry

Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix Accurately calculating energies and atomic forces with linear-scaling methods is a crucial approach to accelerating and improving molecular dynamics simulations. In this paper, we introduce HamGNN-DM, a machine learning model designed to predict atomic forces and energies using local density matrices in molecular dynamics simulations. This approach achieves efficient predictions with a time complexity of O(n), making it highly suitable for large-scale systems. Experiments in different systems demonstrate that HamGNN-DM achieves DFT-level precision in predicting the atomic forces in different system sizes, which is vital for the molecular dynamics. Furthermore, this method provides valuable electronic structure information throughout the dynamics and exhibits robust performance. Scaling ; Linear scale ; Matrix (chemical analysis) ; Physics ; Statistical physics ; Classical mechanics ; Computer science ; Theoretical physics ; Materials science ; Mathematics ; Geometry ; Geography Machine Learning in Materials Science
Authors: Chi-Wei Chu (21554179) | Chun-Ming Lu (110664) | Wing Kiu Yeung (11906265)
Source: Figshare (article, 2025-06-16)
Field: Materials Science — Biomaterials

SystematicEvaluation of Atrous Spatial Pyramid Poolingin U‑Net for Pore Segmentation in Plasma Electrolytic OxidationCoatings Plasma Electrolytic Oxidation (PEO) coatings enhance the physical and chemical properties of metallic substrates, including corrosion resistance, wear resistance, and thermal stability. These enhancements are strongly influenced by the porous surface morphology of the coatings, which affects the ion transport, stress distribution, and permeability. Accurate quantification of pore structures is essential for understanding interfacial structure–property relationships, yet traditional image segmentation methods often fail to capture the complexity of PEO surfaces in SEM images. This study presents a deep learning-based segmentation framework using U-Net architectures integrated with Atrous Spatial Pyramid Pooling (ASPP) to improve multiscale feature extraction. The performance impact of ASPP placement within different parts of U-Net was systematically evaluated. Results show that modifications to the bridge and decoder paths have the greatest impact on segmentation performance, with a combined modification applying ASPP in both achieving the highest F1 score (0.9360) and the highest IoU (0.8798). Statistical analysis using 5-fold cross-validation, bootstrap confidence intervals, and paired <i>t</i>-tests confirmed that only the bridge-modified model (<i>B</i><sub>1×1</sub>) significantly outperformed the baseline (<i>p</i> < 0.05). The proposed approach enables high-fidelity pore segmentation and supports advanced microstructural analysis of PEO coatings. By facilitating accurate morphological quantification, it contributes to the understanding of structure–property relationships in interfacial materials and offers a robust tool for future materials characterization workflows. Pooling ; Pyramid (geometry) ; Segmentation ; Porosity ; Feature (linguistics) ; Pattern recognition (psychology) ; Image segmentation Magnesium Alloys: Properties and Applications ; High-Temperature Coating Behaviors ; Nanoporous metals and alloys
Authors: Sudheer Ganisetti | Tao Du | N. M. Anoop Krishnan | Morten M. Smedskjær
Source: Physical Review Materials (article, 2025-10-03)
Field: Materials Science — Ceramics and Composites

On-the-fly machine learning force fields for alkali silicate glasses Molecular dynamics simulations can provide structural information of glass materials with atomic resolution and for very short time scales. In turn, this requires accurate and effective force fields. Here, focusing on alkali silicate glasses, we develop transferable force fields that are applicable across a wide range of temperatures and compositions. Using on-the-fly machine learning routines, these force fields produce energy and force predictions with accuracy comparable to that of density functional theory. The potential energy landscape is described using structural descriptors that incorporate both radial and angular information within a reduced-dimensional descriptor space. We then employ these accurate machine learning force fields (MLFFs) to prepare silica and binary alkali $({\mathrm{Li}}_{2}\mathrm{O},$ ${\mathrm{Na}}_{2}\mathrm{O},$ ${\mathrm{K}}_{2}\mathrm{O})$ silicate glass samples, allowing us to study their structures. We assess the composition dependence of the structural characteristics of the glasses by computing several key parameters, including neutron structure factor, pair distribution function, angular distribution function, coordination number, and ${Q}^{n}$ speciation. The trained MLFFs accurately reproduce the splitting of the first sharp diffraction peak in potassium silicate glasses, consistent with experimental findings, and reveal that its structural origin is tied to Si-K interactions. Overall, our research demonstrates the power of combining advanced computational techniques with classical structural analysis methods to enhance our structural understanding of complex glass systems. Silicate ; Potassium silicate ; Force field (fiction) ; Neutron scattering ; Alkali metal ; Silicate glass ; Neutron diffraction ; Binary number Glass properties and applications ; Material Dynamics and Properties ; X-ray Diffraction in Crystallography