jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiyuan Zhang | Shih-Kuang Lee | Sharon C. Glotzer | Rebecca Lindsey
Source: Journal of Chemical Theory and Computation (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer-Grafted Nanoparticles Polymer-grafted nanoparticles (PGNs) serve as highly customizable building blocks for technologically relevant self-assembled nanomaterials. Physics-informed inverse design strategies are crucial for expediting exploration of the massive associated design space by identifying optimal PGN attributes, such as polymer length and grafting density, to meet a target self-assembled structure. However, their success hinges on an accurate description of how PGN interactions vary as a function of both particle positions and physical attributes. We introduce a framework to meet this need. Specifically, we develop an "alchemical" machine-learned interatomic model (ML-IAM) that describes how PGN interactions vary as a function of both inter-PGN distances <i>and</i> tunable PGN attributes, simultaneously. This model is an extension of the physics-informed and explicitly many-bodied ChIMES ML-IAM. The resulting extended ChIMES (X-ChIMES) ML-IAM is trained on potential of mean force (PMF) data for PGNs with varied polymer ligand lengths. We enable efficient training data generation by combining the forward-reverse steered molecular dynamics enhanced sampling approach with a grid-sampling scheme in the HOOMD-blue software package. We demonstrate the efficacy of ChIMES for generating coarse-grained (CG) models for PGNs with fixed design attributes, development of X-ChIMES, and its application for enhancing digital alchemy inverse-design simulations using HOOMD-blue and the ChIMES Calculator. This is the first application of ChIMES in modeling CG systems and coupling with an inverse design method to target nanomaterial self-assembly. Function (biology) ; Inverse ; Software ; Coupling (piping) ; Design of experiments ; Nanoparticle ; Throughput ; Interaction model ; Space (punctuation) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Weiren Zhao (4444675) | Shen Wang (464529) | Yang Li (7082)
Source: Figshare (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Designing Buchwald–HartwigReaction Graph forYield Prediction The Buchwald–Hartwig (B–H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B–H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B–H reaction data set, B–H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an <i>R</i><sup>2</sup> score of 0.971 while maintaining low computational costs. Using a perturbation-based analysis, the model reveals significant insights into the relationship between bases and reaction yields, identifying key characteristics of bases influencing yields, which provides valuable guidance for base selection in the B–H reaction. This work demonstrates the effectiveness of tailored graph representations in advancing GNN applications for chemical reaction modeling, offering a promising tool for predicting reaction yield and identifying key reaction factors. Graph ; Artificial neural network ; Chemical reaction ; Key (lock) ; Reaction conditions ; Yield (engineering) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Asymmetric Hydrogenation and Catalysis
Authors: Esther C. Hessong | Zhengyu Zhang | Tianjiao Lei | Mingjie Xu | Toshihiro Aoki | Timothy J. Rupert
Source: arXiv (Cornell University) (preprint, 2025-09-07)
Field: Materials Science — Materials Chemistry

Modulation of structural short-range order due to chemical patterning in multi-component amorphous interfacial complexions Amorphous interfacial complexions have been shown to restrict grain growth and improve damage tolerance in nanocrystalline alloys, with increased chemical complexity stabilizing the complexions themselves. Here, we investigate local chemical composition and structural short-range order in Cu-rich, multi-component nanocrystalline alloys to understand how dopants self-organize within these amorphous complexions and how local structure is altered. High resolution scanning transmission electron microscopy and elemental analysis are used to study both grain boundaries and interphase boundaries, with chemical partitioning observed for both. Notably, the amorphous-crystalline transition region is observed to be enriched in certain dopant species and depleted of others as compared to the interior of the amorphous complexions. This chemical patterning can be explained in terms of the elemental preference for ordered or disordered grain boundary environments. As only a qualitative measure of structural short-range order can be obtained with nanobeam electron diffraction for these specimens, atomistic simulations with a custom-built machine learning interatomic potential are then used to probe how dopant patterning affects local structural state. Increased grain boundary chemical complexity is found to result in a more disordered complexion structure, with segregation to the amorphous-crystalline transition regions driving changes in local structure that are sensitive to dopant ratios. As a whole, the intimate connection between local chemistry and order in amorphous interfacial complexions is demonstrated, opening the door for microstructural engineering within the amorphous complexions themselves. Nanocrystalline material ; Amorphous solid ; Dopant ; Grain boundary ; Transmission electron microscopy ; Scanning transmission electron microscopy ; Population ; Nanocrystal Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Liquid Crystal Research Advancements
Authors: Sungbi Lee | Eunjung Kim | Nam Ryeol Kim | Cheol-Min Yang | Woong‐Ryeol Yu | Cheol Hee Ahn | Myoung‐Gyu Lee | Wonjin Na
Source: Functional Composites and Structures (article, 2025-12-31)
Field: Materials Science — Polymers and Plastics

Deep-learning-assisted extrusion process design of polypropylene/carbon black sheet with varying viscosity Abstract Minor changes in process variables, such as temperature, processing speed, and cooling rate, can significantly impact the properties of the final product in a sheet extrusion process. As a result, many optimization efforts focus on each of these variables. This study explored a machine learning-assisted process design for a polypropylene/carbon black composite sheet. The extrusion process parameters were selected as input variables, while tensile strength, void content, width, and thickness were measured, resulting in a dataset of 180 entries. A deep learning neural network was employed to identify and propose optimal combinations of process parameters and validate these proposals by comparing predicted values to experimental data. The polymer melt index had a significant effect on tensile strength, which is attributed to the degree of crystallinity. The process optimization led to a 20% increase in the tensile strength of continuous fiber composites, enhancing the matrix’s toughness and improving interfacial load-carrying capacity. Extrusion ; Ultimate tensile strength ; Viscosity ; Process (computing) ; Response surface methodology ; Toughness ; Process variable ; Process design ; Composite number Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Machine Learning in Materials Science
Authors: Gaurav Vishwakarma | Christina Hoffmann
Source: Structural Dynamics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Learning many-body potential energy landscape for analysis of neutron scattering data For the discovery of new materials in the field of energy storage, catalysis, and biological processes, molecular dynamics (MD) simulations are an indispensable computational tool. We can achieve highly accurate representations of the potential energy landscape of diverse molecular systems with ab-initio molecular dynamics (AIMD) simulations, but at the cost of high computational time which limits typical applications to 100s of atoms and time scales of ∼100ps. This is where machine learning potentials can be used to capture the underlying physics from first principles, where electrons are treated quantum mechanically, while still reaching long simulation times relatively cheaply. We introduce a workflow for the analysis of neutron scattering data that trains several deep-learning based many-body potentials and interatomic forces from ab-initio reference calculations. Further, to gauge the accuracy of such potentials with classical MD programs, we use the inelastic neutron scattering (INS) spectra as the performance metric. An INS spectra serves as one of the most stringent tests of theory (such as density functional theory), since the model has to predict not only the correct structure but also the correct vibrational dynamics. We use a genetic algorithm to optimize several hyperparameters used in this workflow. For different molecular samples, we successfully demonstrate that our workflow can replicate the experimental INS spectra. Neutron scattering ; Scattering ; Energy (signal processing) ; Energy landscape ; Neutron ; Computer science ; Physics ; Nuclear physics ; Optics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Quantum, superfluid, helium dynamics
Authors: Nikola Vuković | Milan Ignjatović | Novak Stanojević | Mirjana Stojković | Nikola Basta | Filip Perišić | Aleksandar Milićević | Aleksandar Demić | D. Indjin | J. Radovanović
Source:  (article, 2025-08-01)
Field: Chemistry — Spectroscopy

Terahertz quantum cascade laser modeling for applications in imaging, sensing, and metrology In the talk we present our recent modeling results for THz frequency QCLs including electronic structure and transport calculations of exemplary GaAs-based devices, investigation of alternative material platforms based on wide-bandgap semiconductors, modeling passive mode-locking with saturable absorber, and study of optical feedback effects. Moreover, we employ a machine learning framework for THz QCL design optimization. Methods include a Schr&#246;dinger-Poisson solver for calculating eigenenergies and wave functions, a density matrix carrier transport model, Maxwell-Bloch equations for passive mode-locking and self-mixing dynamics, and a multi-layer perceptron neural network to predict the laser transition figure of merit. Terahertz radiation ; Quantum cascade laser ; Metrology ; Cascade ; Quantum metrology ; Laser ; Computer science ; Optoelectronics ; Quantum ; Physics ; Optics ; Engineering ; Quantum computer ; Quantum network ; Quantum mechanics Spectroscopy and Laser Applications ; Air Quality Monitoring and Forecasting ; Water Quality Monitoring and Analysis
Authors: Hao Tang (44155) | Tianle Yue (16405553) | Ying Li (38224)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Assessing Uncertainty in Machine Learning for PolymerProperty Prediction: A Benchmark Study Machine learning (ML) has emerged as a transformative tool in material science, enabling accelerated discovery and design of novel molecules while reducing experimental costs. Uncertainty quantification (UQ) is crucial for enhancing the reliability of ML predictions, particularly in high-stakes applications, such as functional polymer discovery. In this study, we present a comprehensive evaluation of nine UQ methods in MLensemble, Gaussian Process Regression (GPR), Monte Carlo Dropout (MCD), mean-variance estimation (MVE), Bayesian Neural Network based on Variational Inference (BNN-VI) and Markov Chain Monte Carlo (BNN-MCMC), evidential deep learning (EDL), quantile regression (QR), natural gradient boosting (NGBoost)for predicting key polymer properties, including glass transition temperature (<i>T</i><sub>g</sub>), band gap (<i>E</i><sub>g</sub>), melting temperature (<i>T</i><sub>m</sub>) and decomposition temperature (<i>T</i><sub>d</sub>). The models are assessed using three independent metrics, including prediction accuracy (<i>R</i><sup>2</sup>), Spearman’s rank correlation coefficient and calibration area, offering a robust framework for evaluating both mean predictions and uncertainty estimates. Our analysis spans data sets of four properties, out-of-distribution (OOD) experimental and molecular dynamics (MD)-derived data, high-<i>T</i><sub>g</sub> polymers and diverse polymer types, providing a holistic perspective on model performance. Our findings reveal that optimal UQ method selection is highly context-dependent. Ensemble method consistently excelled for general in-distribution predictions across four properties. For challenging OOD scenarios, BNN-MCMC offered a strong balance of predictive accuracy and reliable UQ. NGBoost emerged as the top-performing method for high-<i>T</i><sub>g</sub> polymers, effectively balancing accuracy and uncertainty characterization, with Ensemble method also providing excellent accuracy in this case. Furthermore, BNN-VI demonstrated superior and consistent performance across the nine distinct polymer classes evaluated. This comprehensive benchmark underscores the critical importance of selecting tailored UQ strategies to enhance the trustworthiness of ML predictions, optimize experimental validation efforts, and ultimately accelerate the discovery of advanced functional polymers. Uncertainty quantification ; Gradient boosting ; Artificial neural network ; Monte Carlo method ; Boosting (machine learning) ; Ensemble learning ; Benchmark (surveying) ; Markov chain Monte Carlo ; Overfitting ; Bayesian inference Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Kausik Roy
Source: ChemRxiv (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Catalyst Selection Reveals Nickel's Advantages Over Palladium in Suzuki-Miyaura Cross-Coupling Benchmarking ; Artificial neural network ; Catalysis ; Palladium ; Feature selection ; Feature (linguistics) ; Chemical reaction engineering Machine Learning in Materials Science ; Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods
Authors: Yuncheng Du
Source: NANO (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

From Synthesis to Machine Learning-Assisted Analysis: Investigating Silver Nanowires Effects on Conductive Films In this work, we synthesize silver nanowires (AgNWs) and investigate how their morphology (e.g., aspect ratio) and concentration can affect the electrical performance of conductive films. To model and predict these effects, we build machine learning (ML) models, including random forest, extreme gradient boosting and kernel ridge regressor, and optimize their hyperparameters using Bayesian optimization. To improve model interpretability and quantify the individual and combined effects of morphology and concentration, we integrate these models with permutation importance analysis. This explainable ML approach provides both accurate prediction and advances understanding of how nanomaterials’ properties affect film performance. Our results lay the foundation for optimizing AgNWs-based film design for electronic devices and provide practical guidelines for tailoring nanomaterial properties. Materials science ; Electrical conductor ; Nanotechnology ; Nanowire ; Composite material Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Nima Karimitari (19675033) | Teerachote Pakornchote (6894650) | Abdulaziz W. Alherz (11590873) | Jacob M. Clary (8533380) | Cooper Tezak (18706741) | Sourin Dey (22558904) | Jianjun Hu (19574) | Derek Vigil-Fowler (3798208) | Ravishankar Sundararaman (1429177) | Charles B. Musgrave (1275567) | Christopher Sutton (1294035)
Source: Figshare (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Δ‑Learningof High-Fidelity ElectronicStructure Using Graph Neural Networks with Modified Node-Level Features In this work, we present a Δ-learning approach for predicting the eigenvalues calculated with the hybrid functional HSE06 (ϵ<sub><i>nk</i></sub><sup><i>HSE</i></sup>) for a set of metal and nitrogen doped graphene catalysts (MNCs) from Perdew-Burke-Ernzerhof (PBE) inputs. The model presented here incorporates electronic scalar features along with structural information in a graph neural network (GNN). In particular, the PBE eigenvalues for different bands and <i>k</i>-points and orbital-resolved projectors are combined with the applied potential as node-level features along with structural information within the Atomistic Line Graph Neural Network (ALIGNN) architecture. These features enable flexibility for systems with electrified interfaces, such as in electrocatalysts and achieves mean absolute error (MAE) of less than 0.1 eV. The machine learning model reported here achieves a strong generalization to left-out adsorbates (MAE = 0.074 eV) and leave-one-chemical-space-out (MAE = 0.08 eV) and completely left-out metals (MAE = 0.072 eV), confirming the robustness of the machine learning (ML) model in predicting ϵ<sub><i>nk</i></sub><sup><i>HSE</i></sup>. Artificial neural network ; Eigenvalues and eigenvectors ; Robustness (evolution) ; Graph ; Electronic structure ; Molecular graph ; Generalization Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Tao Wang | Jianwei Li | Bin Wang | Hao Jin
Source: npj Computational Materials (article, 2025-11-28)
Field: Chemistry — Inorganic Chemistry

PU-learning-guided discovery of synthesizable multiferroic nitride perovskites with altermagnetic order ABN3-type nitride perovskites offer a rich platform for multifunctional materials but remain synthetically elusive. Here, we develop a machine learning (ML)-guided framework to expand the library of nitride perovskites and identify multiferroic candidates. By integrating positive-unlabeled (PU) learning with crystal graph convolutional neural networks (CGCNN), we screen 1465 ABN3 compositions and predict 96 synthesizable compounds. Further symmetry and magnetic filtering yield 4 altermagnetic ferroelectric (AM-FE) perovskites. Among them, CeCrN3 emerges as a promising candidate, exhibiting a bandgap of 0.30 eV, a spontaneous polarization of 0.59 μC/cm2, a high Curie temperature of 650 K, and a low polarization switching barrier of 53 meV, as confirmed by density functional theory (DFT) calculations. In addition, CeCrN3 demonstrates a pronounced bulk photovoltaic effect (BPVE), with the shift current reaching 44 μA/V2 and an injection current reaching 2.4 × 109 A/V2, both of which reverse upon FE switching. These findings not only advance the understanding of nitride perovskites but also provides a validated ML-DFT framework to guide experimental efforts in realizing novel functional materials. Ferroelectricity ; Multiferroics ; Nitride ; Curie temperature ; Polarization (electrochemistry) ; Perovskite (structure) ; Band gap Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Guofan Zhang | X. B. Wang | Qirui Sun | Hongyan Zhao | Jiaao Han | Hong Yang | Hanyu Wang | Chongao Tian | A. L. Kuang | L. J. Lai | Chulong Zhang | Tianhao Li | Rui Liu
Source: ChemRxiv (article, 2025-09-17)
Field: Materials Science — Polymers and Plastics

A Versatile Method for Accurately Predicting Electronic Absorption Spectra of Tetrapyrrole Macrocycles Understanding the UV-Vis-NIR electronic spectra of tetrapyrrole macrocycles (porphyrins, chlorins, and bacteriochlorins) is crucial to rational designs of novel functional molecules and materials for energy conversion, catalysis, bio-imaging, and medical applications. Despite this, no methods have been defined to accurately predict the electronic absorption spectra of tetrapyrrole molecules bypassing the expensive and time-consuming TDDFT calculations and enable high throughput screening for thousands of molecules in a short-time period. Using a combination of computational chemistry, Gouterman four-orbital model, and machine learning methods, we show accurate predictions of the electronic absorption spectra of tetrapyrroles bearing various peripheral substituents across the UV-Vis-NIR spectral regions. Optimized AutoGluon pipelines trained on the full data set achieve high predictive fidelity (R² = 0.985 validation, 0.936 test; mean/median errors < 0.1 eV) and robust transferability across different macrocyclic scaffolds. Together, this work establishes an efficient (cost and time), scalable, and mechanistically grounded framework for predicting tetrapyrrole electronic absorption spectra, offering broad utilities in rational designs of tetrapyrrole-based functional materials for real-world applications. Tetrapyrrole ; Time-dependent density functional theory ; Electronic structure ; Absorption spectroscopy ; Molecule ; Absorption (acoustics) ; Spectral line Conducting polymers and applications ; Analytical Chemistry and Sensors ; Molecular Junctions and Nanostructures
Authors: Ziqi Wang | Yu Liu | Xintong Xu | Jiale Zhang | Zongjin Li | Lei Zheng | Kang Peng
Source: Preprints.org (preprint, 2025-05-26)
Field: Materials Science — Polymers and Plastics

Transfer Learning-Enhanced Prediction of Glass Transition Temperature in Bismaleimide-Based Polyimides The glass transition temperature (Tg) is a pivotal parameter governing the thermal and mechanical properties of bismaleimide-based polyimide (BMI) resins. However, limited experimental data for BMI systems posed significant challenges for predictive modeling. To address this gap, this study introduces a hybrid modeling framework leveraging transfer learning. Specifically, a multilayer perceptron (MLP) deep neural network is pre-trained on a large-scale polymer database and subsequently fine-tuned on a small-sample BMI dataset. Complementing this approach, six interpretable machine learning algorithms—Random Forest, Ridge Regression, K-Nearest Neighbors, Bayesian Regression, Support Vector Regression, and Extreme Gradient Boosting —are employed to construct transparent predictive models. SHapley Additive exPlanations (SHAP) analysis is further utilized to quantify the relative contributions of molecular descriptors to Tg. Results demonstrate that the transfer learning strategy achieves superior predictive accuracy in data-scarce scenarios compared to direct training on BMI dataset. SHAP analysis identified charge distribution inhomogeneity, molecular topology, and molecular surface area properties as the major influences on Tg. This integrated framework not only improves the prediction performance, but also provides feasible insights into molecular structure design, laying a solid foundation for rational engineering of high-performance BMI resins. Glass transition ; Materials science ; Composite material ; Thermodynamics ; Engineering physics ; Physics ; Polymer Synthesis and properties of polymers ; Conducting polymers and applications ; Photopolymerization techniques and applications