jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammadreza Karamad | Aditya Biswas
Source: The Journal of Chemical Physics (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

A machine learning and explainable AI framework for adsorption energy prediction via effective representation of the adsorbate chemical environment Adsorption energies, which capture the interactions between adsorbates and solid surfaces, are central to heterogeneous catalysis. Machine learning (ML) offers a powerful approach for rapidly and accurately predicting adsorption energies from computational data, thereby accelerating catalyst screening. The effectiveness of ML models depends on accurately representing the chemical environments of atoms, incorporating both geometric and electronic properties that influence adsorbate-surface interactions. In this study, we present an ML framework that leverages advanced electronic structure descriptors via Gaussian Multipole (GMP) featurization. GMP approximates electron density using Gaussian basis functions, providing a novel representation of elemental identity. Combined with robust geometric features, our model predicts CO and H binding energies (ΔECO∗ and ΔEH∗) on multimetallic alloys, achieving mean absolute errors of 0.07 eV for ΔECO∗ and 0.06 eV for ΔEH∗. To interpret the model's predictions, we applied Shapley additive explanations, a post hoc explainable artificial intelligence (XAI) method. The analysis revealed that GMP features associated with adsorbates and their first-nearest neighbors (FNNs) played the most significant role in determining binding energies, while features from second-nearest neighbors had minimal influence. In addition, broader elemental properties such as boiling point, group number, and atomic number were found to be more predictive of adsorption behavior than conventional features, such as electronegativity. Clustering and t-SNE analyses showed that similar FNN environments yield consistent binding energies, supporting the model's ability to generalize. Overall, this work demonstrates that integrating electronic structure features with explainable AI improves both predictive accuracy and interpretability, offering a powerful strategy for accelerated catalyst screening and rational catalyst design. Representation (politics) ; Adsorption ; Artificial neural network ; Cluster analysis ; Gaussian ; Binding energy ; Gaussian process ; Electronic structure ; Basis (linear algebra) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Chun Zhou | Hannu‐Pekka Komsa
Source: ArXiv.org (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys High-entropy alloys (HEAs) and their two-dimensional counterparts (2D-HEAs) have recently attracted attention due to their tunable properties and catalytic potential, yet their chemical complexity makes direct density functional theory (DFT) calculations computationally prohibitive. The complexity also makes training of machine-learning interatomic potentials (MLIPs) challenging, but this could possibly be overcome by employing universal MLIPs as starting point. In this work, we investigate the applicability of universal MLIP models for 2D transition metal sulfide HEAs and develop effective fine-tuning strategies. Training structures are systematically generated and selected, and the performance of universal and fine-tuned models are benchmarked against DFT. We find that all universal MLIPs employed in this work yield unsatisfactory mixing energies without fine-tuning. Applied to the experimentally synthesized (Mo,Ta,Nb,W,V)S$_2$ system, fine-tuned models based on enumerated structures can achieve near-DFT accuracy in predicting mixing energies while enabling Monte-Carlo simulations and random structure sampling at scales inaccessible to DFT. Mixing (physics) ; Work (physics) ; Density functional theory ; Interatomic potential ; Yield (engineering) ; Computational complexity theory Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: L. Lind | Hilda Sandström | P. Rinke
Source: The Journal of Chemical Physics (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

An interpretable molecular descriptor for machine learning predictions in atmospheric science The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are particularly affected because they often rely on dictionary-based descriptors tied to specific molecular substructures, which currently fail to capture the full range of organic atmospheric compounds, including large, highly oxidized molecules common in the atmosphere. We introduce ATMOMACCS, an interpretable descriptor combining the 166 binary keys of the MACCS fingerprint with motifs inspired by the SIMPOL method for estimating saturation vapor pressures. We show that ATMOMACCS outperforms the RDKit topological fingerprint in kernel ridge regression models, improving predictions of saturation vapor pressures (7%, 8%, 29%, and 43% error reduction), equilibrium partition coefficients (5% and 9% error reduction), glass transition temperatures (22% error reduction), and enthalpies of vaporization (61% error reduction) on six datasets with atmospheric compounds. Feature analysis shows that saturation vapor pressure and partition coefficients are governed by carbon number and oxygen-related features, whereas other phase-transition properties (e.g., enthalpy of vaporization and glass transition temperature) depend on carbon-hydrogen bond types and the presence of heteroatoms other than oxygen. This highlights the generalizability of ATMOMACCS across different datasets and properties as an interpretable molecular descriptor. Molecular descriptor ; Generalizability theory ; Vapor pressure ; Vaporization ; Binary number ; Random forest ; Enthalpy of vaporization ; Feature (linguistics) ; Saturation (graph theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Ibnu Jihad | M. Qamar | Fahhad H. Alharbi | Zain H. Yamani
Source: International Journal of Hydrogen Energy (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Exploring compositional-only descriptors in machine learning screening of hydrogen evolution reaction catalysts Valence (chemistry) ; Robustness (evolution) ; Feature selection ; Gibbs free energy ; Graph ; Feature (linguistics) ; Classifier (UML) ; Hydrogen Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Nisryne El Massafi | Othman El Kssiri | Kawtar Zerhouni | Mohamed Naji | Abdessamad Faik | Anass Sibari
Source: ACS Applied Materials & Interfaces (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Ionic Conductivity and Binding Energy in Doped Li <sub>2</sub> ZrCl <sub>6</sub> Halides for Solid-State Electrolyte All-solid-state batteries are attracting significant attention, particularly the development of solid electrolytes. Among potential candidates, ternary halide compounds have emerged as promising materials for fast ion conduction. In this study, we integrate supervised machine learning (ML) models with density functional theory (DFT) calculations to accelerate the discovery of fast-ion-conducting halides. A database of halide compounds was constructed from the literature and merged with the existing Liverpool inorganic solid-state electrolyte database to predict ionic conductivity near practical operating temperatures. In addition, a database of over 12,000 DFT calculations was employed for the prediction of the binding energy. The model demonstrated excellent predictive performance for both properties (MAE = 0.6012, <i>R</i><sup>2</sup> = 0.8062 for conductivity; MAE = 0.0314, <i>R</i><sup>2</sup> = 0.9957 for binding energy). Using the trained model with interpretable elemental features, including Element Fraction, Element Property descriptors, and the number of atoms, we explored divalent and trivalent substitutions in the Li<sub>2+2×<i>x</i></sub>Zr<sub>1-</sub><i><sub>x</sub></i>Me<sup>2+</sup><i><sub>x</sub></i>Cl<sub>6</sub> and Li<sub>2+</sub><i><sub>x</sub></i>Zr<sub>1-</sub><i><sub>x</sub></i>Me<sup>3+</sup><i><sub>x</sub></i>Cl<sub>6</sub> systems (where Me = Mg, Ca, Mn, Zn, Al, Sc, Ga, In, Y, etc.). Finally, dopant elements such as Ti<sup>3+</sup>and Sn<sup>2+</sup> are proposed to enhance the ionic conductivity and structural stability of the halide family. The results highlight the potential of machine learning to efficiently identify new compositions for solid-state battery electrolytes, illustrating the importance of efficient machine-learning approaches using easily obtainable descriptors to speed up the development of new solid-state electrolytes. Halide ; Ternary operation ; Ionic conductivity ; Density functional theory ; Binding energy ; Ionic bonding ; Conductivity ; Electrolyte Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Ali Nabi Duman | Youcef Djoudi | Skyler Phillips | Almaz S. Jalilov
Source: ACS Omega (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Predicting Carbon Dot Photoluminescence: A Comparative Machine Learning Study on Systematic Synthesis Data The design of carbon dots (CDs) with tailored optical properties is a significant challenge in materials science, often hindered by complex synthetic protocols and nonlinear synthesis-property relationships. To accelerate this process, we present a data-driven approach leveraging machine learning to predict the photoluminescent emission of CDs from their synthesis parameters. A systematic experimental data set was utilized, comprising 407 CD syntheses prepared from p-benzoquinone and ethylenediamine across different solvents. We performed a rigorous comparative analysis of state-of-the-art ensemble learning algorithms: Random Forest, XGBoost, and CatBoost. The results demonstrate that CatBoost provides superior predictive accuracy, achieving a mean cross-validation coefficient of determination (<i>R</i> <sup>2</sup>) of approximately 0.98, outperforming other models. These findings highlight the efficacy of gradient boosting algorithms, particularly CatBoost, in modeling systematic chemical data and provide a validated computational tool to guide the efficient, on-demand synthesis of functional nanomaterials. Random forest ; Gradient boosting ; Boosting (machine learning) ; Training set ; Ensemble learning ; Support vector machine ; Group method of data handling ; Experimental data Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Tao Jian | Zengjian Zhao | W. M. Ye | Xiang Cai | Hongjun Lin | Jiaheng Teng | Bisheng Li | Leihong Zhao | Banglin Chen
Source: Nano Energy (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Advances in machine learning (ML)-driven design of two-dimensional materials for energy and environmental applications Interpretability ; Energy (signal processing) ; Rational design ; Field (mathematics) ; Sustainable energy ; Deep learning ; Perspective (graphical) ; Key (lock) ; Efficient energy use Machine Learning in Materials Science ; 2D Materials and Applications ; Covalent Organic Framework Applications
Authors: Qi Jiang | Shaobo Chen | Jianxing Liu | Hua Tong | Zhongjun Xu
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted descriptor identification for catalysts in catalytic ozonation of VOCs The catalytic ozonation method is a low-energy degradation technology for volatile organic compounds (VOCs). Developing efficient catalysts is crucial for advancing this technology. Up to 100 relevant works have been published to date, but analyzing the collective inferences through conventional literature searches is a challenging task. A machine learning (ML) framework was presented to create a database for catalytic ozonation of VOCs and to predict catalyst performance from experimental descriptors. Notably, the data mining process collected 577 data points with 21 descriptors. The VOC conversion rate was successfully predicted by the GBDT. Under constrained conditions, Shapley Additive exPlanations (SHAP) analysis revealed that Fe/MnO<sub><i>x</i></sub> and Ce/MnO<sub><i>x</i></sub> are the most promising catalysts for the catalytic ozonation. Two series of catalysts were prepared using different methods and evaluated for the catalytic ozonation of <i>p</i>-xylene and 1,2-dichloroethane (1,2-DCE) under constrained conditions, which validated the accuracy of the SHAP analysis. This work highlights ML as a practical tool for efficient design of catalysts, and provides appropriate descriptors for ML-assisted development of catalysts for catalytic ozonation of VOCs. The protocol allows for the prediction of promising promoter combinations for catalysts under various constrained conditions, providing guidance for catalyst design and development. Catalysis ; Process (computing) ; Degradation (telecommunications) ; Work (physics) ; Identification (biology) ; Molecular descriptor Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Pol Benítez | Cibrán Rico Lopez | Edgardo Saucedo | Teruyasu Mizoguchi | Claudio Cazorla
Source: Advanced Intelligent Discovery (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost. However, the performance of ML models critically depends on the quality, size, and diversity of the training dataset. In materials science, this dependence is particularly important for learning from low‐symmetry atomistic configurations that capture thermal excitations, structural defects, and chemical disorder, features that are ubiquitous in real materials but underrepresented in most datasets. The absence of systematic strategies for generating representative training data may therefore limit the predictive power of ML models in technologically critical fields such as energy conversion and photonics. In this work, we assess the effectiveness of graph neural network (GNN) models trained on two fundamentally different types of datasets: one composed of randomly generated atomic configurations and another constructed using physically informed sampling based on lattice vibrations. As a case study, we address the challenging task of predicting electronic and mechanical properties of a prototypical family of optoelectronic materials under realistic finite‐temperature conditions. We find that the phonons‐informed model consistently outperforms the randomly trained counterpart, despite relying on fewer data points. Explainability analyses further reveal that high‐performing models assign greater weight to chemically meaningful bonds that control property variations, underscoring the importance of physically guided data generation. Overall, this work demonstrates that larger datasets do not necessarily yield better GNN predictive models and introduces a simple and general strategy for efficiently constructing high‐quality training data in materials informatics. Property (philosophy) ; Artificial neural network ; Predictive modelling ; Predictive power ; Ensemble learning ; Training set ; Task (project management) ; Material properties ; Experimental data ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: Shoeb Athar | Philippe Jund
Source: arXiv (Cornell University) (preprint, 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: Haiyan Liu | Penghua Zhu | Chenyu Tan
Source: Frontiers in Materials (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Deep learning-based image classification for microstructural analysis in computational materials science Introduction Recently, the integration of deep learning techniques and computational materials science has catalyzed significant advances in the microstructural analysis of materials, particularly through the lens of multiscale, high-dimensional imaging data. However, conventional models often fall short in capturing the intricate topology and spatial variability that define realistic microstructural patterns, limiting their ability to inform material property predictions, inverse design, and structural synthesis. Methods To overcome these challenges, we introduce an innovative deep learning framework designed for microstructural image classification and representation learning, incorporating physical, geometric, and topological constraints directly into the training process. Our method, centered on the structured generative model MorphoTensor, introduces hierarchical tensorial embeddings that retain directionality, anisotropy, and spatial locality—features crucial for realistic material modeling. We further incorporate a Topology-Aware Latent Refinement strategy, which couples persistent homology with differentiable approximations of Betti numbers to enforce topological consistency and augment microstructural diversity. Unlike existing data-driven pipelines, our framework seamlessly integrates statistical encoding, topologicalization, and latent manifold alignment within a unified architecture, ensuring robustness across diverse datasets including phase-field simulations and real microscopy data. Results and Discussion Empirical evaluations on benchmark and experimental datasets demonstrate that our method significantly outperforms standard convolutional and autoencoding baselines in accuracy, stability, and generalization. Moreover, our approach aligns closely with the ongoing efforts in the broader computational materials and mechanics communities to build interpretable, physically informed, and adaptable deep learning systems. These contributions illustrate the potential of structured deep generative modeling as a foundational tool for advancing intelligent microstructure analysis and design in materials informatics. Generative grammar ; Deep learning ; Robustness (evolution) ; Benchmark (surveying) ; Consistency (knowledge bases) ; Differentiable function ; Convolutional neural network ; Representation (politics) ; Topological data analysis ; Generative model Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Model Reduction and Neural Networks
Authors: M. Maksuda Khanam | Saleena Younus | Laila Mousafi Alasal | M. Khabir Uddin | Julhash U. Kazi
Source: ChemRxiv (article, 2026-03-03)
Field: Chemistry — Analytical Chemistry

Evaluation-driven preprocessing and interpretable machine learning for large-scale FTIR polymer classification in microplastics research Fourier Transform Infrared (FTIR) spectroscopy is widely used for polymer identification in microplastics research. However, raw spectra are often affected by noise, baseline drift, and other acquisition-related artifacts that are not chemically meaningful. Since preprocessing steps can strongly influence multivariate analysis and machine learning (ML) classification, we developed xpectrass, an open-source framework designed for systematic evaluation of FTIR preprocessing, exploratory analysis, and interpretable ML. A combined dataset of 12,214 spectra covering multiple polymer classes was used to compare different denoising, baseline correction, and normalization strategies. A preprocessing workflow based on wavelet denoising, adaptive smoothness penalized least squares (asPLS) baseline correction, and either Standard Normal Variate (SNV) or Spectral Moments normalization showed stable performance across datasets and supported harmonization between studies. Unsupervised methods such as PCA, t-SNE, and UMAP separated most polymer classes, although partial overlap remained among chemically similar polyolefins. We further evaluated 41 ML model configurations and present detailed results for a representative high-performing model based on XGBoost. Model interpretation using SHapley Additive exPlanations (SHAP) associated predictions with specific wavenumbers, facilitating chemical interpretability. In summary, xpectrass provides a structured and scalable workflow that links heterogeneous FTIR spectra to reproducible polymer classification in microplastics research. Normalization (sociology) ; Microplastics ; Preprocessor ; Pattern recognition (psychology) ; Data pre-processing ; Partial least squares regression ; Fourier transform ; Analyte ; Naive Bayes classifier Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Microplastics and Plastic Pollution
Authors: Jan G Rittig | Manuel Dahmen | Martin Grohe | Philippe Schwaller | Alexander Mitsos
Source: Current Opinion in Chemical Engineering (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Molecular machine learning in chemical process design Process (computing) ; Process design ; Machine design ; Chemical process ; Work in process ; Molecular machine Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods