jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongshun Chen | Ryan Zhou | Rujing Zha | Zihan Chen | Wenpan Li | Rowan Rolark | John Patrick Reidy | Jian Cao | Ping Guo | David C. Dunand | Horacio D. Espinosa
Source: arXiv (Cornell University) (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space Hihg-temperature creep characterization of structural alloys traditionally relies on serial uniaxial tests, which are highly inefficient for exploring the large search space of alloy compositions and for material discovery. Here, we introduce a machine-learning-assisted, high-throughput framework for creep law identification based on a dimple array bulge instrument (DABI) configuration, which enables parallel creep testing of 25 dimples, each fabricated from a different alloy, in a single experiment. Full-field surface displacements of dimples undergoing time-dependent creep-induced bulging under inert gas pressure are measured by 3D digital image correlation. We train a recurrent neural network (RNN) as a surrogate model, mapping creep parameters and loading conditions to the time-dependent deformation response of DABI. Coupling this surrogate with a particle swarm optimization scheme enables rapid and global inverse identification with sparsity regularization of creep parameters from experiment displacement-time histories. In addition, we propose a phenomenological creep law with a time-dependent stress exponent that captures the sigmoidal primary creep observed in wrought INCONEL 625 and extracts its temperature dependence from DABI test at multiple temperatures. Furthermore, we employ a general creep law combining several conventional forms together with regularized inversion to identify the creep laws for 47 additional Fe-, Ni-, and Co-rich alloys and to automatically select the dominant functional form for each alloy. This workflow combined with DABI experiment provides a quantitative, high-throughput creep characterization platform that is compatible with data mining, composition-property modeling, and nonlinear structural optimization with creep behavior across a large alloy design space. Creep ; Nonlinear system ; Characterization (materials science) ; Inverse problem ; Stress (linguistics) ; Robustness (evolution) ; Viscoplasticity Machine Learning in Materials Science ; High Temperature Alloys and Creep ; High Entropy Alloys Studies
Authors: Hyukjun Lim | Seokhyun Choung | Jinuk Moon | Jeong Woo Han
Source: npj Computational Materials (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Angular relational knowledge distillation of machine learning interatomic potentials for scalable catalyst exploration State-of-the-art equivariant Graph Neural Networks (GNNs) achieve DFT-level accuracy for molecular simulations but remain computationally prohibitive for high-throughput screening and long-timescale dynamics. Knowledge distillation (KD) offers a promising solution, yet its application to GNNs remains nascent compared to the mature methodologies developed for language and vision models. The few existing approaches rely on atom-wise feature matching and have struggled to achieve the accuracy necessary for capturing the relational physics underlying the potential energy surface (PES). We introduce Angular Relational Knowledge (ARK) distillation, a framework that distills relational knowledge from pretrained GNNs by modeling each interatomic interaction as a relational vector. Through a contrastive objective, ARK guides compact student models to preserve the geometric structure of the teacher’s learned PES rather than isolated node features. On the OC20, OMat24, and SPICE benchmarks, our ARK-trained student consistently outperforms baselines in energy and force prediction, achieving faithful physical knowledge transfer at a fraction of the computational cost. In a practical high-throughput catalyst screening application, the distilled model achieves an 11.9 × acceleration while preserving chemical coherency. In a large-scale oxygen reduction reaction catalyst screening of over 580,329 structures, ARK reduces the computational cost from 59.4 CPU-years to 11.6 GPU-hours while successfully identifying 30 DFT-confirmed catalyst candidates. Notably, the screening reveals a high prevalence of tellurium-containing materials among the top candidates, corroborating recent experimental reports and demonstrating ARK’s capacity for identifying computationally promising candidates in underexplored spaces. Scalability ; Distillation ; Relational database ; Artificial neural network ; Matching (statistics) ; Invariant (physics) ; Feature (linguistics) ; Statistical relational learning Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Suman Bhasker-Ranganath | Filippo Balzaretti | Johannes Voss
Source: Journal of Chemical Information and Modeling (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Optimizing Prediction of Chemical Bonds in Interfacial Dynamics through Local Uncertainty Estimates with Neural Network Ensembles We present a framework for data-efficient training of machine-learning interatomic potentials for interfacial chemistry, especially heterogeneous catalytic systems. We establish strategies for density functional theory training data generation consisting of procedurally generated bulk, surface, and gas-phase atomic geometries, as well as moderately randomized structures. We show how ensembles of neural network machine-learning interatomic potentials trained on different splits of these training structures yield reliable uncertainty estimates at the atomic node energy level. Our models can thus identify which atomic sites and chemical bonds in a system lead to uncertainties in the predicted potential energy surface. Using hydrogen interacting with platinum as a test case, we find that the atomic uncertainty estimates identify both unphysical bonding scenarios and physically relevant interactions that are underrepresented in the original training data, such as surface diffusion, bond breaking, and bond formation. Building on these insights, we propose local uncertainty-informed strategies that flag outliers via statistical correlations, thereby improving active learning efficiency and enhancing the reliability of neural network-based potentials for extended-scale reactive dynamics. Artificial neural network ; Reliability (semiconductor) ; Outlier ; Molecular dynamics ; Node (physics) ; Chemical bond ; Hydrogen bond ; Energy (signal processing) ; Yield (engineering) ; Error bar Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Abdussalam Almabrouk Ahmaid | Abdulhakim Ali Jongher
Source: ChemRxiv (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

The Role of Valence Electrons in Predicting Semiconductor Band Gaps: A Machine Learning Approach The transition to sustainable energy requires the development of efficient solar cell materials. The experimental band gap (Eg) is a critical property that dictates a material's light absorption capabilities, but its experimental determination is often a time-consuming and resource-intensive process. This study explores the potential of a machine learning-based approach to rapidly and accurately predict the experimental band gaps of semiconductor materials. Using data from the Matbench dataset, a Random Forest Regressor model was trained on a set of computationally-derived elemental properties (Magpie features). The model was able to predict the experimental band gap with a Mean Absolute Error (MAE) of 0.4469 eV and an R² score of 0.6656, demonstrating a robust correlation between elemental properties and the final material band gap. Furthermore, a comparative experiment with an XGBoost model was conducted to evaluate performance improvement. The XGBoost model achieved a lower MAE (0.22 eV) and RMSE (0.28 eV), confirming its superior generalization capability while validating the robustness of the valence electron-based approach. The analysis of feature importance revealed that the mean number of p-valence electrons was the most influential feature in the model's predictions, underscoring the critical role of elemental electronic structure. This research provides a rapid and effective method for screening promising new materials, accelerating the discovery of high-performance materials for renewable energy applications. Robustness (evolution) ; Random forest ; Band gap ; Semiconductor ; Valence (chemistry) ; Feature (linguistics) ; Hybrid functional ; Electron Machine Learning in Materials Science ; Ga2O3 and related materials ; Heusler alloys: electronic and magnetic properties
Authors: Shoeb Athar | Philippe Jund
Source: ArXiv.org (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials The discovery of high-performance thermoelectric (TE) materials for advancing green energy harvesting from waste heat is an urgent need in the context of looming energy crisis and climate change. The rapid advancement of machine learning (ML) has accelerated the design of thermoelectric (TE) materials, yet a persistent "gap" remains between high-accuracy computational predictions and their successful experimental validation. While ML models frequently report impressive test scores (R^2 values of 0.90-0.98) for complex TE properties (zT, power factor, and electrical/thermal conductivity), only a handful of these predictions have culminated in the experimental discovery of new high-zT materials. In this review, we identify and discuss that the primary obstacles are poor model generalizability-stemming from the "small-data" problem, sampling biases in cross-validation, and inadequate structural representation-alongside the critical challenge of thermodynamic phase stability. Moreover, we argue that standard randomized validation often overestimates model performance by ignoring "hidden hierarchies" and clustering within chemical families. Finally, to bridge this gap between ML-predictions and experimental realization, we advocate for advanced validation strategies like PCA-based sampling and a synergetic active learning loop that integrates ML "fast filters" for stability (e.g., GNoME) with high-throughput combinatorial thin-film synthesis to rapidly map stable, high-zT compositional spaces. Context (archaeology) ; Cluster analysis ; Energy (signal processing) ; Stability (learning theory) ; Bridge (graph theory) ; Thermoelectric effect ; Thermoelectric materials Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials
Authors: Hongshun Chen | Ryan Zhou | Rujing Zha | Zihan Chen | Wenpan Li | Rowan Rolark | John Patrick Reidy | Jian Cao | Ping Guo | David C. Dunand | Horacio D. Espinosa
Source: ArXiv.org (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space Hihg-temperature creep characterization of structural alloys traditionally relies on serial uniaxial tests, which are highly inefficient for exploring the large search space of alloy compositions and for material discovery. Here, we introduce a machine-learning-assisted, high-throughput framework for creep law identification based on a dimple array bulge instrument (DABI) configuration, which enables parallel creep testing of 25 dimples, each fabricated from a different alloy, in a single experiment. Full-field surface displacements of dimples undergoing time-dependent creep-induced bulging under inert gas pressure are measured by 3D digital image correlation. We train a recurrent neural network (RNN) as a surrogate model, mapping creep parameters and loading conditions to the time-dependent deformation response of DABI. Coupling this surrogate with a particle swarm optimization scheme enables rapid and global inverse identification with sparsity regularization of creep parameters from experiment displacement-time histories. In addition, we propose a phenomenological creep law with a time-dependent stress exponent that captures the sigmoidal primary creep observed in wrought INCONEL 625 and extracts its temperature dependence from DABI test at multiple temperatures. Furthermore, we employ a general creep law combining several conventional forms together with regularized inversion to identify the creep laws for 47 additional Fe-, Ni-, and Co-rich alloys and to automatically select the dominant functional form for each alloy. This workflow combined with DABI experiment provides a quantitative, high-throughput creep characterization platform that is compatible with data mining, composition-property modeling, and nonlinear structural optimization with creep behavior across a large alloy design space. Creep ; Nonlinear system ; Characterization (materials science) ; Inverse problem ; Stress (linguistics) ; Robustness (evolution) ; Viscoplasticity Machine Learning in Materials Science ; High Temperature Alloys and Creep ; High Entropy Alloys Studies
Authors: Berna Alemdag | Azra Kocaarslan | Gözde Kabay
Source: Scientific Reports (article, 2026-02-09)
Field: Chemistry — Organic Chemistry

Leveraging molecular descriptors and explainable machine learning for monomer conversion prediction in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization This study presents a molecular descriptor-based machine learning (ML) architecture for predicting monomer conversion in photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization systems. Unlike traditional polymer informatics approaches that treat polymers as single units or use one-hot encoding for reaction components, we decompose each PET-RAFT system into its individual parts: monomer, RAFT agent, and photocatalyst. Next, each element was separately encoded using 2D molecular descriptors derived from SMILES. Using a literature-sourced dataset of 152 PET-RAFT systems, we systematically trained (with fivefold cross-validation, CV) and evaluated 10 ML algorithms. CatBoost showed greater stability across CV-folds (SD = ± 0.07) and was identified as the top performer for monomer conversion prediction (R2 = 0.84; RMSE = 10.04 pps; MAE = 8.16 pps). SHapley Additive exPlanations (SHAP) analysis revealed mechanistically interpretable structure–property-performance relationships, highlighting that monomer topological complexity, electronic polarization, and molecular weight together account for over 60% of the model’s predictive power. External validation confirmed CatBoost’s ability to generalize to unseen (meth)acrylates and (meth)acrylamides (MAE = 8.03), with comparable performance to that of the training set. In practice, the learned descriptor-conversion mapping enables fast in silico screening and component ranking, highlighting actionable descriptor ranges and potentially accelerating design-build-test cycles for high-conversion PET-RAFT. Monomer ; Polymerization ; Component (thermodynamics) ; In silico ; Polymer ; Chain transfer ; Transfer of learning ; Photoinduced electron transfer Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; Click Chemistry and Applications
Authors: Berna Alemdag | Azra Kocaarslan | Gözde Kabay
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Leveraging molecular descriptors and explainable machine learning for monomer conversion prediction in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization This study presents a molecular descriptor-based machine learning (ML) architecture for predicting monomer conversion in photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization systems. Unlike traditional polymer informatics approaches that treat polymers as single units or use one-hot encoding for reaction components, we decompose each PET-RAFT system into its individual parts: monomer, RAFT agent, and photocatalyst. Next, each element was separately encoded using 2D molecular descriptors derived from SMILES. Using a literature-sourced dataset of 152 PET-RAFT systems, we systematically trained (with fivefold cross-validation, CV) and evaluated 10 ML algorithms. CatBoost showed greater stability across CV-folds (SD = ± 0.07) and was identified as the top performer for monomer conversion prediction (R2 = 0.84; RMSE = 10.04 pps; MAE = 8.16 pps). SHapley Additive exPlanations (SHAP) analysis revealed mechanistically interpretable structure–property-performance relationships, highlighting that monomer topological complexity, electronic polarization, and molecular weight together account for over 60% of the model’s predictive power. External validation confirmed CatBoost’s ability to generalize to unseen (meth)acrylates and (meth)acrylamides (MAE = 8.03), with comparable performance to that of the training set. In practice, the learned descriptor-conversion mapping enables fast in silico screening and component ranking, highlighting actionable descriptor ranges and potentially accelerating design-build-test cycles for high-conversion PET-RAFT. Monomer ; Polymerization ; Component (thermodynamics) ; Chain transfer ; Polymer ; Photoinduced electron transfer ; In silico ; Transfer of learning Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; Click Chemistry and Applications
Authors: Yuhang Wang | Dongchao Guo | Tao Yang | Quan Qian | Xiaotong Liu | Siqi Shi
Source: Computational Materials Today (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

A review of topological descriptors for amorphous materials complementing graph neural networks Amorphous systems lack long-range order, making their properties strongly dependent on short-range order (SRO) and medium-range order (MRO) structural motifs and their network connectivity. While graph neural networks (GNNs) excel for crystalline materials, their performance on amorphous systems is limited by graph construction ambiguities, message-passing bottlenecks, data scarcity, and transferability issues. This review surveys traditional descriptors and then focuses on topological approaches: complex network descriptors, persistent homology, rigidity theory (also known as topological constraint theory), and graph limit metrics. We summarize how rings, voids, and percolating backbones correlate with mechanics, transport, and thermodynamics across metallic glasses, oxide glasses, polymers, and organic semiconductors, among others, highlighting their potential to inform the exploration and design of amorphous energy materials. Finally, we provide a shortlist of topological variables with potential for property modeling and set out three directions in datasets, unified topology modeling and topology aware GNN architectures. Artificial neural network ; Graph ; Topology (electrical circuits) ; Topological data analysis ; Amorphous solid ; Context (archaeology) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topological and Geometric Data Analysis
Authors: Duyu Chen | Yao Xuan | Héctor D. Ceniceros | Glenn H. Fredrickson
Source: The Journal of Chemical Physics (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated density-explicit polymer field theory simulations Recently, the density-explicit framework, which describes the thermodynamic properties of multicomponent polymer systems as functionals of both auxiliary and density fields, has attracted growing interest in polymer field theory simulations. This is due to the flexibility of the framework in accommodating various forms of intermolecular potentials, and in particular, the easy generalization to many-body interactions beyond pair potentials. However, numerical simulations based on the formalism are generally more expensive owing to the increased number of fields, compared to the conventional auxiliary field framework. In this work, we develop deep neural networks with efficient low-dimensional and largely local feature representations that are applicable across different spatial resolutions and dimensions to accelerate polymer field theory simulations based on this framework. Our results may serve as a stepping stone toward accurate and efficient prediction of the phase behavior of complex block copolymer mesophases, as well as a blueprint for developing machine learning-assisted field theoretic simulation tools for the computational study of polymers and soft matter systems more broadly. Field (mathematics) ; Field theory (psychology) ; Formalism (music) ; Artificial neural network ; Flexibility (engineering) ; Polymer ; Lattice (music) ; Complex system ; Granularity Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Ana Vitória Ferreira Deleigo | Gabrielle Coelho Lelis | Maria Luisa Braunger | Stefano Casalini | Yasmin Watanabe | Gabriel R. Schleder | Wilson T. Fonseca | Rafael Furlan de Oliveira
Source: Small (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Ultrasensitive MicroRNA Detection Combining Reduced Graphene Oxide Electrolyte‐Gated Transistors and Machine Learning MicroRNAs (miRNAs) are promising biomarkers for disease diagnosis, but conventional detection methods such as reverse transcription polymerase chain reaction (RT-PCR) require complex instrumentation and reagents, limiting their suitability for portable diagnostics. Here, we report an ultrasensitive and selective biosensor that integrates DNA-functionalized reduced graphene oxide (rGO), electrolyte-gated transistors (EGTs), and machine learning (ML) for miRNA detection. The platform targets the miR-34 family (miR-34a, miR-34b, and miR-34c), which is associated with cancer and neurological disorders. The biosensor discriminates perfectly matched from mismatched sequences over a wide dynamic range (0.1-1000 amol L<sup>-1</sup>) with an ultralow limit of detection of 0.098 amol L<sup>-1</sup>. ML enables multidimensional analysis of EGT transfer curves and extraction of physically meaningful features from high-dimensional data. This approach advances point-of-care technologies for highly sensitive and selective miRNA detection, with strong potential for portable molecular diagnostics. Graphene ; Biosensor ; Detection limit ; Limiting ; microRNA ; Transistor ; Oxide ; Dynamic range Graphene research and applications ; Advanced biosensing and bioanalysis techniques ; Nanopore and Nanochannel Transport Studies