jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Wu | Cameron Hargreaves | Arpit Mishra | Gian-Marco Rignanese
Source: ArXiv.org (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Machine Learning for Electrode Materials: Property Prediction via Composition In this work, we benchmark three leading Machine Learning (ML) frameworks-MODNet, CrabNet, and a random forest model based on Magpie feature-for predicting properties of battery electrode materials using the Materials Project Battery Explorer dataset. We evaluate these models based on predictive accuracy, visualize numerical features using two-dimensional embeddings, and quantify performance using standard metrics. Our results demonstrate that CrabNet consistently outperforms the other models across all tests. To validate these findings, we employ robust statistical methods: bootstrap resampling and two cross-validation (CV) strategies (leave one cluster out and stratified 5-fold CV), comparing each model against a control baseline. In addition, we apply unsupervised clustering on MODNet-derived features using t-SNE and DBSCAN, revealing coherent material groupings without prior labels. This analysis confirms the robustness of the evaluated models and underscores the potential of ML-driven approaches for accelerating the electrode materials discovery. However, our study also identifies practical limitations and quantifies challenges associated with integrating ML models into materials science workflows. Despite these constraints, our findings suggest that ML models are highly effective for early-stage compositional screening in the battery industry. This work provides a foundation for future research on ML applications in materials discovery. Cluster analysis ; Robustness (evolution) ; Random forest ; Resampling ; Benchmark (surveying) ; Predictive modelling ; Property (philosophy) ; Mixture model ; Unsupervised learning Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Hao Wu | Cameron Hargreaves | Arpit Mishra | Gian-Marco Rignanese
Source: Open MIND (preprint, 2026-03-08)
Field: Materials Science — Materials Chemistry

Machine Learning for Electrode Materials: Property Prediction via Composition In this work, we benchmark three leading Machine Learning (ML) frameworks-MODNet, CrabNet, and a random forest model based on Magpie feature-for predicting properties of battery electrode materials using the Materials Project Battery Explorer dataset. We evaluate these models based on predictive accuracy, visualize numerical features using two-dimensional embeddings, and quantify performance using standard metrics. Our results demonstrate that CrabNet consistently outperforms the other models across all tests. To validate these findings, we employ robust statistical methods: bootstrap resampling and two cross-validation (CV) strategies (leave one cluster out and stratified 5-fold CV), comparing each model against a control baseline. In addition, we apply unsupervised clustering on MODNet-derived features using t-SNE and DBSCAN, revealing coherent material groupings without prior labels. This analysis confirms the robustness of the evaluated models and underscores the potential of ML-driven approaches for accelerating the electrode materials discovery. However, our study also identifies practical limitations and quantifies challenges associated with integrating ML models into materials science workflows. Despite these constraints, our findings suggest that ML models are highly effective for early-stage compositional screening in the battery industry. This work provides a foundation for future research on ML applications in materials discovery. Cluster analysis ; Robustness (evolution) ; Random forest ; Resampling ; Benchmark (surveying) ; Predictive modelling ; Property (philosophy) ; Mixture model ; Unsupervised learning Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Tongzhao Gong | Weiye Hao | Yun Chen | Bolv XIAO | Xing-Qiu Chen | D. X. Li
Source: Journal of Materials Research and Technology (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Machine learning based phase-field simulation framework for multi-component alloy solidification The computational efficiency of the phase-field method in simulating multi-component alloy solidification is strictly limited by the iterative solution of nonlinear quasi-equilibrium thermodynamic equations. To overcome this bottleneck, this study develops a machine learning-based phase-field (MLPF) framework. We systematically evaluated several machine learning models to determine the optimal surrogate for directly predicting quasi-equilibrium thermodynamic data (QETD). The neural network (NN) model was identified as the most effective solution, offering an optimal balance between predictive accuracy, model size, and inference speed. By integrating the surrogate model into the multi-phase-field solver, simulations of Al–Zn–Mg–Cu–Zr alloy solidification were conducted. The MLPF framework achieved a computational acceleration of approximately 300 times for millimeter-scale domains while maintaining high physical fidelity relative to conventional methods. Furthermore, the simulation elucidated the coupling mechanism between cooling rate and microsegregation. While high cooling rates induce severe solute pile-up in the liquid due to diffusion limitations, they simultaneously promote solute trapping within the solid matrix. This mechanism effectively alleviates terminal solidification segregation, providing new insights into the non-equilibrium solidification of complex aluminum alloys. Alloy ; Coupling (piping) ; Artificial neural network ; Inference ; Nonlinear system ; Diffusion ; Mechanism (biology) Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Donghee Chang | Amir Taqieddin | Forrest A. L. Laskowski
Source: Chemistry of Materials (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Performance-Based Selection of Machine Learning Interatomic Potentials for Studying Solid-State Electrolytes High-throughput, accurate prediction of solid-state electrolyte (SSE) properties is essential for advancing all-solid-state batteries (ASSBs). While density functional theory (DFT) can achieve high accuracy, the structural complexity of inorganic superionic conductors makes screening vast compositional spaces computationally prohibitive. Machine learning interatomic potentials (MLIPs) offer comparable accuracy but with orders-of-magnitude lower computational resourcing demands. In this study, we benchmark several pretrained MLIPs on the chemically and structurally complex Li6PS5Cl argyrodite electrolyte, a prototypical SSE. Models are validated against DFT results for structural, energetic, and dynamic properties, and then applied to study the effect of atomic disorder on lithium-ionic transport. Through these studies, three key findings emerge: (1) universal accuracy of MLIP models does not necessarily extend to subtle configurational or compositional changes; (2) nonconservative frameworks, where forces and energies are predicted separately, often fail to capture dynamic behavior; (3) conservative frameworks, which enforce energy-force consistency, better represent physical laws and show superior generalizability beyond the training set. These results provide practical guidance for MLIP model selection by researchers in the ASSB field, and those beyond who investigate highly disordered structures. Generalizability theory ; Benchmark (surveying) ; Density functional theory ; Electrolyte ; Selection (genetic algorithm) ; Key (lock) ; Generalization ; Feature selection Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Enda Xiao | Terumasa Tadano
Source: npj Computational Materials (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Accurate screening of functional materials with machine-learning potential and transfer-learned regressions: Heusler alloy benchmark Abstract We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. As a test case, quaternary and all- d Heusler compounds were screened for stable compounds with large magnetocrystalline anisotropy energy ( E aniso ). Structure optimization and evaluation of formation energy and energy above the convex hull were performed using the eSEN-30M-OAM interatomic potential, while local magnetic moments, phonon stability, magnetic stability, and E aniso were predicted by eSEN models trained on our DxMag Heusler database. A frozen transfer learning strategy was employed to improve accuracy. Candidate compounds identified by the ML-HTP workflow were validated with density functional theory, confirming high predictive precision. We also benchmark the performance of different uMLIPs, discuss the fidelity of local magnetic moment prediction, and demonstrate generalization to unseen elements via transfer learning from a universal interatomic potential. Workflow ; Magnetocrystalline anisotropy ; Benchmark (surveying) ; Density functional theory ; Magnetic moment ; k-nearest neighbors algorithm ; Generalization ; Energy minimization ; Alloy Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Inorganic Chemistry and Materials
Authors: Muhammad Ishtiaq | Yeon-Ju Lee | A. Geetha Bhavani | Sung-Gyu Kang | N.S. Reddy
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multi-Algorithm Machine Learning Framework for Predicting Crystal Structures of Lithium Manganese Silicate Cathodes Using DFT Data Lithium manganese silicate (Li-Mn-Si-O) cathodes are key components of lithium-ion batteries, and their physical and mechanical properties are strongly influenced by their underlying crystal structures. In this study, a range of machine learning (ML) algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory (DFT) data obtained from the Materials Project database. The dataset comprised 211 compositions characterized by key descriptors, including formation energy, energy above the hull, bandgap, atomic site number, density, and unit cell volume. These features were utilized to classify the materials into monoclinic (0) and triclinic (1) crystal systems. A comprehensive comparison of various classification algorithms including Decision Tree, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbor, Stochastic Gradient Descent, Gaussian Naïve Bayes, Gaussian Process, and Artificial Neural Network (ANN) was conducted. Among these, the optimized ANN architecture (6–14-14-14-1) exhibited the highest predictive performance, achieving an accuracy of 95.3%, a Matthews correlation coefficient (MCC) of 0.894, and an F-score of 0.963, demonstrating excellent consistency with DFT-predicted crystal structures. Meanwhile, Random Forest and Gaussian Process models also exhibited reliable and consistent predictive capability, indicating their potential as complementary approaches, particularly when data are limited or computational efficiency is required. This comparative framework provides valuable insights into model selection for crystal system classification in complex cathode materials. Triclinic crystal system ; Support vector machine ; Monoclinic crystal system ; Crystal structure ; Artificial neural network ; Crystal (programming language) ; Crystal structure prediction ; Random forest ; Gaussian process Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Jinlong LI | Hao WANG | Huayun Geng
Source: SHILAP Revista de lepidopterología (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Review of Machine Learning Potentials in the Study of Materials Properties With the rapid advancement of artificial intelligence (AI) technologies and hardware capabilities, AI has gradually become a revolutionary tool driving transformative changes across multiple scientific research domains. In the field of materials science, machine learning methods are significant in high-throughput materials design and property prediction. Over the past decade, machine learning-based approaches for constructing interatomic potentials have been widely applied in the study of material properties, and are providing crucial support for the theoretical design of novel materials and in-depth understanding of their underlying microscopic mechanisms. This article reviews the development of machine learning potentials, and introduces their fundamental workflows. The principles of mainstream methods and their applications in materials property research are outlined. Moreover, recent progress in emerging universal potential models is briefly discussed, then concludes with an analysis of current challenges and future research directions. Transformative learning ; Field (mathematics) ; Property (philosophy) ; Mainstream ; Applications of artificial intelligence Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Boron and Carbon Nanomaterials Research
Authors: Umesh Arampath | Bryant Pero | David Stewart | David Demirjian
Source: ChemRxiv (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

When Do Simple Models Win? Machine Learning Architectures for UV Absorption Prediction and Insights for General Molecular Property Prediction What determines which machine learning architecture wins for molecular property prediction? We address this question through a controlled comparison of five models spanning four architecture families: fingerprint-based ensembles (Random Forest, XGBoost), a directed message-passing graph neural network (GNN), a bidirectional gated recurrent network (BiGRU), and a pretrained Transformer, using UV absorption wavelength (λ max) prediction as a testbed across 18,755 solute-solvent pairs with stratified 5-fold cross-validation and two external datasets totaling over 40,000 molecules. The answer depends on the task. For screening within known chemical space, RF with Morgan fingerprints is optimal: it matches the GNN (RMSE = 31.34 ± 1.82 vs. 31.69 ± 3.18 nm, p = 0.77), trains in 15 minutes on a CPU with no hyperparameter tuning, and outperforms all sequence-based models. For exploring novel scaffolds, deep learning is superior: experimental validation on 16 novel UV-absorbing 1 compounds shows that the GNN (MAE = 26.2 nm), Transformer (26.3 nm), and Bi-GRU (28.6 nm) all outperform RF (38.5 nm), as learned representations generalize where fixed fingerprints cannot. In either case, encoding solvent identity improves all models: a simple SMILES/fingerprint concatenation strategy yields 6-8% RMSE reductions across fingerprint, graph, and recurrent architectures (p < 0.02), without domain-specific descriptor engineering. Interpretability analysis confirms that both RF feature importance and BiGRU gradient saliency independently highlight the same chromophore motifs, and that architectures with local inductive bias systematically outperform global-attention Transformers on this locally determined property. These findings provide general guidance for molecular property prediction: the largest or most complex model is not always the best choice. Instead, optimal model selection depends on the locality of the target property and the relationship between target compounds and available training data. When the target property is governed by local molecular features and the prediction task is interpolation within known chemical space, simple fingerprint-based models offer an efficient and accurate alternative to deep learning. Interpretability ; Hyperparameter ; Inductive bias ; Testbed ; Pattern recognition (psychology) ; Normalization (sociology) ; Preprocessor ; Artificial neural network ; Feature learning ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Rahul Kumar Sharma | Harpriya Minhas | Biswarup Pathak
Source: The Journal of Physical Chemistry Letters (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Unraveling Scaling Relationships in Dual-Atom Catalysts with Electronic Descriptors: A Machine Learning Investigation for OER/ORR Activity Dual-atom catalysts (DACs) have emerged as a new frontier in heterogeneous catalysis, offering improved stability and superior performance in key electrocatalytic reactions. However, identifying optimal multimetallic DACs combination for a multistep reaction is challenging due to the vast chemical space. Herein, we develop a machine learning (ML) framework to expedite the screening of DACs, which consist of a heterometallic dimer embedded in the surface layer of a metal host, for improved oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) performance. We encode the solid-state-derived d-band descriptors to accurately train the ML model and effectively capture the nonmonotonic bifunctional activity on DACs, without requiring expensive DFT calculations. Interestingly, we identify the nonscaling behavior of these DACs, with CoPd and CoCu dimer exhibiting superior OER and ORR activity. Furthermore, we employ the surface charging method to evaluate the potential-dependent activity and reveal the nonlinear relationship between catalytic activity and electrode potential. Overall, this study established the pivotal role of d-states in governing the catalytic performance and offers a practical pathway to accelerate the discovery of next-generation electrocatalysts for fuel cell applications. Catalysis ; Bifunctional ; Oxygen reduction reaction ; Stability (learning theory) ; Component (thermodynamics) ; Scaling ; Fuel cells ; Nonlinear system Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xingchi Liu | Cheng Fu | Yutong Song | Haomin Wu | Yewei Ding | Jing Ma | Yuanhui Ji
Source: Crystal Growth & Design (article, 2026-02-13)
Field: Chemistry — Physical and Theoretical Chemistry

Accelerating Rational Crystal Habit Design via Interpretable Mechanism-Guided Machine Learning Framework The pharmaceutical crystal habit significantly influences downstream processing and product quality. However, traditional experimental screening methods rely heavily on empirical knowledge and trial-and-error experimentation, demanding substantial resources and time. Here we present an interpretable machine learning framework integrating molecular descriptors with quantum chemical (QC) or thermodynamic (HSP, PC-SAFT) parameters to predict crystal habits. Using a curated database of 418 entries covering 153 APIs and 41 solvents, the XGBoost model demonstrated superior performance among seven algorithms. Models augmented with QC or thermodynamic descriptors significantly improved prediction, achieving AUC > 0.88. SHAP analysis revealed dipole moments and solubility parameter differences as key determinants of crystal habit, enhancing interpretability. External validation on six compounds in seven solvents confirmed that models with additional mechanistic descriptors consistently outperformed the molecular-only baseline, with the Molecular+QC model showing the best overall performance. This proposal paves the way to the rational design of crystal engineering strategies for habit prediction and control, and offers a general framework that can readily be extended to other crystallographic applications, as well as broader domains, including drug discovery and materials science. Crystal habit ; Crystal (programming language) ; Crystal structure prediction ; Rational design ; Molecular descriptor ; Quantitative structure–activity relationship ; Key (lock) ; Product (mathematics) Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: Jiangyuan Feng | Lixiang Qian | Yuechao Yao | Rui Zhang | Jun Zhang | Yucun Zhou | Liang Zhang | Liangfei Xu | Jianqiu Li
Source: Chinese Chemical Letters (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Proton and oxygen vacancy mobility in BaCeO₃: Insights from machine learning-driven molecular dynamics Proton ; Molecular dynamics ; Proton transport ; Density functional theory ; Oxygen ; Conductivity ; Thermal conduction ; Ionic conductivity ; Ionic bonding Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Kamrun N. Keya | Amirreza Daghighi | Gerardo M. Casanola-Martin | Wenjie Xia | Bakhtiyor Rasulev
Source: Discover Materials (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Comparative evaluation of machine Learning-based QSPR techniques for predicting polymer glass transition temperature The glass transition temperature ($$\:{T}_{g}$$) is a pivotal parameter for amorphous polymers, influencing their behavior under different thermal properties. Assessing $$\:{T}_{g}$$ is crucial for evaluating material performance across varying temperatures. Our research integrates machine learning with cheminformatics framework to analyze $$\:{T}_{g}$$ values across a broad polymer dataset. This framework improves a comprehensive understanding of the quantitative correlations between the polymers’ structural attributes and their $$\:{T}_{g}$$. Utilizing a dataset of 250 polymers, we constructed a series of Machine Learning-based Quantitative Structure-Property Relationship (ML-QSPR) models. The initial ML-QSPR used Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods to identify an optimal set of molecular descriptors. Subsequently, various non-linear machine learning methods, including Support Vector Regression (SVR), Gaussian Process Regression (GPR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were used for comparative purposes and predictive analysis. The results demonstrate that MLP, driven by seventeen (17) selected descriptors, yields the most accurate ML model, with training and external validation $$\:{R}^{2}\:$$values of 0.82 and 0.79, respectively. This model predicts with good performance the $$\:{T}_{g}$$ values of the polymers under study, showing the role of specific structural descriptors in refining polymer property predictions. Support vector machine ; Glass transition ; Quantitative structure–activity relationship ; Linear regression ; Regression ; Random forest ; Regression analysis ; Process (computing) ; Perceptron ; Kriging Machine Learning in Materials Science ; Material Dynamics and Properties ; Computational Drug Discovery Methods
Authors: Bohang Lv | Min Liu | Zehua Li | Binrui Xu | Jincan Zhang | Liwen Zhang
Source: Computational Materials Science (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Perovskite band gap prediction based on multi-feature crystal graph convolutional neural network Band gap ; Perovskite (structure) ; Convolutional neural network ; Graph ; Crystal (programming language) ; Artificial neural network Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties