jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongtao Guo | Shuai Li | Shu Li
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Applications of Machine Learning in Polymer Materials: Property Prediction, Material Design, and Systematic Processes This paper reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic technologies such as molecular descriptors and feature representation, data standardization and cleaning, and records a number of high-quality polymer databases. Subsequently, it elaborates on the key role of machine learning in polymer property prediction and material design, covering the specific applications of algorithms such as traditional machine learning, deep learning, and transfer learning; further, it deeply expounds on data-driven design strategies, such as reverse design, high-throughput virtual screening, and multi-objective optimization. The paper also systematically introduces the complete process of constructing high-reliability machine learning models and summarizes effective experimental verification, model evaluation, and optimization methods. Finally, it summarizes the current technical challenges in research, such as data quality and model generalization ability, and looks forward to future development trends including multi-scale modeling, physics—informed machine learning, standardized data sharing, and interpretable machine learning. Standardization ; Property (philosophy) ; Process (computing) ; Field (mathematics) ; Key (lock) ; Generalization ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Block Copolymer Self-Assembly
Authors: Siqi Zhan | Hengheng Zhao | Haotian Wang | Zhanjie Liu | Qian Li | Weifeng Zhang | Qingsong Zhao | Liqun Zhang | Jun Liu
Source: Advanced Science (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials Designing and developing high-performance polymeric materials with machine learning has become a major trend in materials science. However, conventional data-driven neural network models often suffer from the "black-box" problem, lacking physical constraints and interpretability, and struggle to achieve accurate predictions with limited experimental data. In this work, a Physics-Embedded Neural Network (PENN) is proposed, which incorporates the Yeoh hyperelastic constitutive model into the network architecture to embed physical laws directly into the learning process, thereby improving physical plausibility and interpretability. The model was first pre-trained on large-scale molecular dynamics (MD) simulation data to capture the intrinsic correlations between polymer structure and mechanical behavior. It was then fine-tuned with a small amount of experimental data through a transfer learning strategy that calibrates the stress magnitude from high-strain-rate simulations to the experimental scale, effectively bridging the gap between simulation and experiment. Uncertainty quantification was further employed to validate the robustness of the predictions. Beyond accurate prediction, PENN enables performance-driven inverse design by mapping target properties to candidate compositional regions, transforming predictive modeling into a practical tool for guiding polymer development. Bridging (networking) ; Robustness (evolution) ; Artificial neural network ; Experimental data ; Hyperelastic material ; Inverse ; Deep learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Jingyu Feng | Yao Qin | Tao Yang | J. Fang | Yiyi Zhang | Guifa Huang | Xiang Xiao | Dechao Chen | Shuangliang Zhao | Zengxi Wei
Source: Journal of Chemical Information and Modeling (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Polymerization Performance for Metallocene Catalysts via a Dual-Path Neural Network and Local Feature Learning Metallocene catalysts, distinguished by their well-defined active centers and tunable coordination geometries, are pivotal in the homopolymerization of propylene to produce polypropylene with tailored properties. However, the rational design of such catalysts remains challenging due to the complex coupling between ligand structures and polymerization conditions. Conventional trial-and-error approaches are inefficient, while existing machine learning (ML) models often overlook critical ligand descriptors, limiting their generalization for industrial use. To address this, we developed a hybrid ML framework that integrates both reaction parameters and catalyst structural features. A dual-path neural network processes numerical and categorical inputs separately to avoid feature semantic distortion, enabling accurate predictions of catalyst activity (<i>R</i><sup>2</sup> = 0.9201) and number-average molecular weight (<i>R</i><sup>2</sup> = 0.9133). For the narrow molecular weight distribution typical of metallocene-derived polypropylene─a characteristic leading to compact, locally correlated data─a k-nearest neighbor regression model achieved superior performance (<i>R</i><sup>2</sup> = 0.9766) by effectively capturing local sample relationships. Both models outperformed eight other benchmark ML algorithms across all metrics. This work provides a robust, interpretable computational strategy for linking catalyst chemistry to polymer properties, offering a practical tool for the targeted design and scalable application of high-performance polypropylene materials. Artificial neural network ; Catalysis ; Polymerization ; Metallocene ; Feature (linguistics) ; Polypropylene ; Generalization ; Benchmark (surveying) ; Scalability Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Xinran Gao
Source:  (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Research on machine-learning-based prediction models for carrier mobility in semiconductor materials Carrier mobility is a fundamental parameter for semiconductor devices. While discovering high-mobility materials is desirable, experimental and ab initio calculation methods are traditionally too slow and cost-intensive. Materials informatics relies on machine learning (ML) to provide an appealing high-throughput alternative. This study developed and tested machine learning (ML) models such as Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Artificial Neural Network (ANN) to predict room-temperature electron mobility in inorganic semiconductors. Using a curated dataset of 480 materials, a 25-dimendional (25-D) feature vector (structural, electronic, chemical, phonon related) was constructed. The GBDT model performed the best, achieving an R² of 0.915 and RMSE of 0.28 (on a log scale) on the test set. Feature importance analysis shows electron effective mass, band gap, and Debye temperature are the most influential descriptors of solid-state physics. This work shows that ML can be a good tool to discovering new semiconductors and provides insights into developing better ones in the future. Predictive modelling ; Semiconductor materials ; Mathematical model ; Semiconductor ; Model validation Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Silicon and Solar Cell Technologies
Authors: Sophie Jaffard | Ivo F. Sbalzarini
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Chemical Reaction Networks Learn Better than Spiking Neural Networks We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the deterministic mass-action kinetics formulation of chemical reaction networks. Specifically, we prove that a certain reaction network without hidden layers can learn a classification task previously proved to be achievable by a spiking neural network with hidden layers. We provide analytical regret bounds for the global behavior of the network and analyze its asymptotic behavior and Vapnik-Chervonenkis dimension. In a numerical experiment, we confirm the learning capacity of the proposed chemical reaction network for classifying handwritten digits in pixel images, and we show that it solves the task more accurately and efficiently than a spiking neural network with hidden layers. This provides a motivation for machine learning in chemical computers and a mathematical explanation for how biological cells might exhibit more efficient learning behavior within biochemical reaction networks than neuronal networks. Spiking neural network ; Artificial neural network ; Task (project management) ; Regret ; Random neural network ; Biological neural network Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Advanced Memory and Neural Computing
Authors: Outhman Abbassi | Hicham Labrim | Soumia Ziti | Mohammed Benaissa
Source: Structural Chemistry (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

MGCSP-DL: a deep learning approach for material graph crystal lattice prediction Crystal structure prediction ; Bravais lattice ; Orthorhombic crystal system ; Deep learning ; Lattice (music) ; Crystal (programming language) ; Artificial neural network ; Crystal structure Machine Learning in Materials Science ; Advanced Graph Neural Networks ; X-ray Diffraction in Crystallography
Authors: Yijun Li | Yutuo Wang | Wangyang Li | Yuan Liu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Low-data-driven Prediction of Chemical Reactions involving PVA: A Quantum Chemistry-inspired Multiscale Deep Learning Framework Process (computing) ; Chemical reaction ; Deep learning ; Chemical reaction engineering ; Quantum chemical ; Polymer ; Key (lock) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Catalysis and Oxidation Reactions
Authors: Yogesh Yadav | Shweta Yadav | Vivek Vijay | Ambesh Dixit
Source: Energy Storage (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Crystal System Classification of Phosphate Cathode Materials Using Machine Learning for Advanced Lithium‐Ion Battery ABSTRACT We employed machine learning classification algorithms to predict the crystal systems, specifically triclinic, monoclinic, and orthorhombic, associated with Li–P–TM–O; (TM = Mn, Fe Co, Ni, V) based‐phosphate cathodes. Feature evaluation revealed that cathode properties depend on the crystal structure, and optimized classification strategies yield better predictability. Gradient Boosting Machines, Extremely Randomized Trees, and Random Forest‐based ensemble machine learning algorithms have demonstrated the best predictive capabilities for crystal systems in the Monte Carlo cross‐validation test. Additionally, sequential forward selection (SFS) is performed to find the utmost critical features influencing the accuracy of prediction for different machine learning models, with Volume, Band gap, and Sites as input features. The ensemble machine learning algorithms with 80.69% for Random Forest, 78.96% for Extremely Randomized Tree, and 80.40% for Gradient Boosting Machine approaches lead to the maximum accuracy toward crystallographic classification with stability, and the predicted materials can be the possible cathodes for lithium ion batteries. More importantly, the present approach can be extended to other groups of materials. Gradient boosting ; Boosting (machine learning) ; Ensemble learning ; Support vector machine ; Feature selection ; Random forest ; Crystal (programming language) ; Statistical classification Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Michael Kilgour | Mark E. Tuckerman | Jutta Rogal
Source: Journal of Chemical Information and Modeling (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

MXtalTools: A Toolkit for Machine Learning on Molecular Crystals We present MXtalTools, a flexible Python package for the data-driven modeling of molecular crystals, facilitating machine learning studies of the molecular solid state. MXtalTools comprises several classes of utilities: (1) synthesis, collation, and curation of molecule and crystal data sets, (2) integrated workflows for model training and inference, (3) crystal parametrization and representation, (4) crystal structure sampling and optimization, (5) end-to-end differentiable crystal sampling, construction, and analysis. Our modular functions can be integrated into existing workflows or combined and used to build novel modeling pipelines. MXtalTools leverages CUDA acceleration to enable high-throughput crystal modeling. The Python code is available open-source on our GitHub page, with detailed documentation on ReadTheDocs. Python (programming language) ; Workflow ; Documentation ; Modular design ; Crystal (programming language) ; Source code ; Crystal structure prediction ; CUDA Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Sophie Jaffard | Ivo F. Sbalzarini
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Chemical Reaction Networks Learn Better than Spiking Neural Networks We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the deterministic mass-action kinetics formulation of chemical reaction networks. Specifically, we prove that a certain reaction network without hidden layers can learn a classification task previously proved to be achievable by a spiking neural network with hidden layers. We provide analytical regret bounds for the global behavior of the network and analyze its asymptotic behavior and Vapnik-Chervonenkis dimension. In a numerical experiment, we confirm the learning capacity of the proposed chemical reaction network for classifying handwritten digits in pixel images, and we show that it solves the task more accurately and efficiently than a spiking neural network with hidden layers. This provides a motivation for machine learning in chemical computers and a mathematical explanation for how biological cells might exhibit more efficient learning behavior within biochemical reaction networks than neuronal networks. Spiking neural network ; Artificial neural network ; Task (project management) ; Regret ; Random neural network ; Biological neural network Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Advanced Memory and Neural Computing
Authors: Zhikun Zhang | GiovanniMaria Piccini
Source: Nature Communications (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Exploring chemistry and catalysis by biasing skewed distributions via deep learning The automated discovery of chemical and catalytic reactions remains a major challenge in computational chemistry, particularly in complex systems where conventional methods struggle to identify optimal searching directions. Here, we propose Loxodynamics, a machine-learning-driven approach for reaction exploration via biased molecular dynamics. By leveraging the skewness of local probability distributions, Loxodynamics dynamically determines low-energy barrier directions, efficiently guiding the system toward metastable states. The core of our framework is Skewencoder, an Autoencoder augmented with a skewness-based loss function that extracts reaction coordinates from minimal sampling data. Through iterative sample-and-search cycles, the system adaptively maps the free energy surface, capturing finite-temperature effects critical to complex reactive environments. We validate our method across model potentials, gas-phase reactions (S<sub>N</sub> 2 and Diels-Alder), and catalytic alcohol dehydration in acidic chabazite under operando conditions. Loxodynamics provides a systematic framework for reaction discovery that addresses key limitations of conventional techniques, notably by allowing acceleration without elevated temperatures or a priori knowledge of collective variables. Autoencoder ; A priori and a posteriori ; Metastability ; Deep learning ; Reaction coordinate ; Function (biology) ; Key (lock) ; Chabazite Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Origins and Evolution of Life
Authors: Yan Zhou | Yue Zhao | Jiayi Li | Chunfa Miao | Hongru Zhang | Jianguang Qi | Yinglong Wang | Huairong Zhou | Yang Hu
Source: Polymer (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Molecular descriptor-driven machine learning for predicting polymer glass transition temperature Glass transition ; Support vector machine ; Polymer ; Random forest ; Artificial neural network ; Fingerprint (computing) ; Molecular descriptor ; Deep learning Machine Learning in Materials Science ; Polymer crystallization and properties ; Polymer composites and self-healing
Authors: Jiechen Guo | Yifan Chai | Cancan Hong | Hongtai Liu | Lijing Xie | Tianle Wang | Jingpeng Chen | Ge Song | Zonglin Yi | Fangyuan Su
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Prediction of the low-temperature properties of electrolyte solvents for lithium-ion batteries <i>via</i> machine learning Electrolytes with low melting points (MPs), high boiling points (BPs), and high dielectric constants (<i>ε</i>) can effectively mitigate performance degradation in lithium-ion batteries (LIBs) under low-temperature conditions. However, the lack of systematic experimental data on electrolyte properties poses a significant challenge to traditional design approaches. To address this limitation, we developed a machine learning workflow that integrates data acquisition using large language models, model construction, and interpretability analysis, aiming to predict key molecular properties, with a focus on MPs, BPs and <i>ε</i>. We constructed a multi-source database, LiElectroDB, that contains over 150 000 electrolyte molecules relevant to LIBs. The prediction models demonstrate strong performance across all three properties, achieving an <i>R</i><sup>2</sup> of 0.8864 and a root mean square error (RMSE) of 23.3 K for the MP, a coefficient of determination (<i>R</i><sup>2</sup>) of 0.9608 and an RMSE of 14.3 K for the BP using the XGBoost algorithm, and an <i>R</i><sup>2</sup> of 0.8718 and a RMSE of 6.7 for <i>ε</i> using an artificial neural network. To further uncover structure-property relationships, t-SNE and SHAP are employed to analyze the molecular features contributing to thermal behavior at a microscopic level. Finally, by integrating molecular neighborhood search with high-throughput screening, nine candidate molecules are identified as promising low-temperature electrolytes for LIBs. This work provides an efficient and generalizable framework for the design of low-temperature electrolytes in LIBs. Interpretability ; Mean squared error ; Electrolyte ; Workflow ; Boiling point ; Dielectric ; Root mean square ; Work (physics) ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Zihan Yan | Shengjie Tang | Yizhou Zhu
Source: ArXiv.org (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures. We show that rigid SSE frameworks favor efficient learning, prioritizing data quality over quantity. Crucially, force RMSE does not reliably predict transport performance. By analyzing locality and benchmarking frameworks, we provide practical guidelines to accelerate the development of next-generation solid-state batteries. Benchmarking ; Perspective (graphical) ; Quality (philosophy) ; Training (meteorology) ; Locality ; Field (mathematics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: A. Packia Antony Amalan | N Renugadevi
Source: Emerging Materials Research (article, 2026-02-23)
Field: Materials Science — Biomaterials

Prediction of mechanical properties of magnesium alloys through machine learning: a review The present work reviews recent research on the mechanical properties of magnesium (Mg)-based materials using machine learning (ML) and deep learning methods. Magnesium-based materials are one of the important lightweight and biodegradable materials due to their favourable characteristics. However, preparation of magnesium-based materials is a challenging task owing to their inflammable nature and consumes time and energy as well. The mechanical and chemical properties are highly dependent upon material composition, purity, and process parameters. The purpose of this review is to enhance the process of understanding the material properties from traditional techniques to artificial intelligence with higher accuracy. Both supervised and unsupervised ML approaches are used to predict properties of the model. Support vector machine, regression, and decision tree are commonly used supervised learning algorithms with higher accuracy and minimum error (MAE, RMSE). Similarly, principal component analysis, and clustering are commonly used unsupervised learning algorithms. In addition, deep learning methods such as artificial neural network, convolutional neural network are paid significant contributions to material characterisation. Hence, this paper aims to review magnesium alloy-based works with respect to four categories, namely microstructure, mechanical properties, corrosion and wear characterisation. Artificial neural network ; Magnesium ; Cluster analysis ; Process (computing) ; Convolutional neural network ; Decision tree ; Material properties ; Unsupervised learning ; Component (thermodynamics) Magnesium Alloys: Properties and Applications ; Machine Learning in Materials Science ; Aluminum Alloys Composites Properties