jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashok K. Verma | Junaid Jami | Amrita Bhattacharya
Source: ArXiv.org (preprint, 2025-07-02)
Field: Materials Science — Materials Chemistry

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and design of next-generation magnetic materials. In this work, a refined descriptor is proposed that significantly improves the prediction of two critical magnetic properties -- magnetic ordering (Ferromagnetic vs. Ferrimagnetic) and magnetic moment per atom -- using only the structural information of materials. Unlike previous models limited to Mn-based or lanthanide-transition metal compounds, the present approach generalizes across a diverse dataset of 5741 stable, binary and ternary, ferromagnetic and ferrimagnetic compounds sourced from the Materials Project. Leveraging an enriched elemental vector representation and advanced feature engineering, including nonlinear terms and reduced matrix sparsity, the LightGBM-based model achieves an accuracy of 82.4% for magnetic ordering classification and balanced recall across FM and FiM classes, addressing a key limitation in prior studies. The model predicts magnetic moment per atom with a correlation coefficient of 0.93, surpassing the Hund's matrix and orbital field matrix descriptors. Additionally, it accurately estimates formation energy per atom, enabling assessment of both magnetic behavior and material stability. This generalized and computationally efficient framework offers a robust tool for high-throughput screening of magnetic materials with tailored properties. Ferrimagnetism ; Magnetic moment ; Moment (physics) ; Ferromagnetism ; Support vector machine ; Magnetic domain ; Magnetic field ; Magnetism ; Representation (politics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Yuanyuan Kong | Zhenghui Li | Pengcheng Shi | Xiaoshuang Li | Xiaohua Zhang | Xiyuan Feng
Source: Journal of Materials Chemistry A (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

High-throughput screening of Janus t-phase TMXY semiconducting materials for thermoelectric applications aided by machine learning Integrating high-throughput DFT and ML, we screen 2D Janus t-TMXY monolayers, identifying dynamically stable candidates: ligand asymmetry tunes transport properties. These show superior thermoelectric performance, with energy conversion potential. Janus ; Thermoelectric effect ; Energy (signal processing) ; Thermoelectric cooling ; Asymmetry Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Covalent Organic Framework Applications
Authors: Mahmut Furkan Kalkan | İbrahim Halil Kalkan | Necip Fazıl Yilmaz | Derya Dışpınar | Cem Kahruman | Abdulcabbar Yavuz
Source: Integrating materials and manufacturing innovation (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Automatic and Rapid Measurement in Artificial Intelligence-Aided Microstructure Analysis: A Deep Learning Approach Applied to AlSi9 Alloys Deep learning ; Segmentation ; Microstructure ; Eutectic system ; Pattern recognition (psychology) ; Automation Machine Learning in Materials Science ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Bing Zheng | Liying Cui | Qing Li | Yanchang Zhang | Jianyu Zhang | Zhe Wang | Jiankang Chen
Source: Research Square (preprint, 2025-08-13)
Field: Materials Science — Polymers and Plastics

WITHDRAWN: Machine Learning-Assisted High-Throughput Screening of Transparent Organic Light-Emitting Diodes Anode Materials Anode ; Throughput ; OLED ; Optoelectronics ; Materials science ; Diode ; Computer science ; Nanotechnology ; Telecommunications ; Electrode ; Chemistry ; Wireless Conducting polymers and applications ; Organic Light-Emitting Diodes Research ; Machine Learning in Materials Science
Authors: Ming Ji | Yongmei Zhang | Xiaolong Pei | Yuhong Zhao | Gaolong Zhang | Hua Hou
Source: Journal of Alloys and Compounds (article, 2025-11-23)
Field: Materials Science — Biomaterials

Data-driven optimization strategy for the mechanical properties of ZM5 alloy Ultimate tensile strength ; Alloy ; Support vector machine ; Gradient boosting ; Bayesian optimization ; Regression analysis ; Artificial neural network ; Linear regression Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Boron and Carbon Nanomaterials Research
Authors: Shengde Zhang (6401279) | Zihao Wang (845023) | Hanyu Gao (1236072) | Teng Zhou (2087974)
Source: Figshare (article, 2025-07-14)
Field: Chemistry — Inorganic Chemistry

A Multitask Learning Model for Predicting VariousTypes of Metal–Organic Framework Stability This study introduced a MOF stability prediction neural network (MOFSNN), a multitask learning architecture to predict various stability metrics of MOFs, including thermal, solvent removal, water, acid, base, and boiling water stabilities. Based on the crystal graph convolutional neural network (CGCNN), MOFSNN incorporates a lattice parameter embedding layer and task-specific attention layers to enhance the prediction accuracy and robustness. Using a data set compiled from literature, the MOFSNN model was demonstrated to outperform the traditional machine learning models and the original CGCNN model, especially in tasks with limited data. Uncertainty analysis using latent space variance (LSV) and latent space entropy (LSE) proved to effectively control prediction reliability. Moreover, the model’s ability to extrapolate to unseen materials further validated its great potential for MOF stability prediction. This study highlights the efficacy of multitask learning in leveraging correlations among different stability metrics, advancing MOF stability prediction and laying a solid foundation for material discovery. Stability (learning theory) ; Multi-task learning ; Embedding ; Artificial neural network ; Entropy (arrow of time) ; Convolutional neural network ; Graph ; Perceptron Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: V. A. Polukhin | S. Kh. Estemirova
Source: Russian Metallurgy (Metally) (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Amorphous High-Entropy Alloys as Innovative Materials for Hydrogen Energy: State of the Art and Development Prospects Amorphous high-entropy alloys (AHEAs) represent a promising class of hydrogen energy materials, since they combine technically significant hydrogen capacity, fast sorption/desorption kinetics, and resistance to cyclic loading. These unique properties are caused by their complex chemical composition, which includes several hydride-forming elements, and the absence of a long-range order in their atomic structure, which creates favorable hydrogen diffusion conditions. This review systematizes current understanding of the fundamental properties of AHEAs. Special attention is paid to the structural features of amorphous alloys, the hydrogen distribution in their matrix, the influence of chemical composition on hydrogen capacity and structural stability, and the conditions for the formation of a thermodynamically stable amorphous state. The mechanisms of interaction between an amorphous structure and hydrogen and the kinetics of hydrogenation and dehydrogenation processes are considered separately. The principles of rational composition selection with allowance for the role of hydride-forming elements and thermodynamic parameters are discussed; they can be used to reveal the most promising multicomponent systems. AHEA synthesis methods, such as gas-phase technologies, melt quenching, and mechanochemical synthesis, are described in detail; modern approaches using plasma and additive technologies are also touched upon. The review covers modern approaches to theoretical modeling and machine learning (ML) for predicting the phase composition and hydrogen capacity of AHEAs, and attention is paid to promising directions in the development of hydrogen storage materials, where the integration of computer modeling, ML, and experimental investigations is considered to be the key to predicting the sorption properties of AHEAs and to selecting their compositions. In conclusion, the necessity of an interdisciplinary approach to integrate these materials into real hydrogen energy devices is emphasized. Amorphous solid ; Hydrogen ; Hydrogen storage ; Dehydrogenation ; Amorphous metal ; Diffusion ; Phase (matter) Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Catalysts for Methane Reforming
Authors: S. Han | Sandip De
Source: ChemRxiv (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated evolutionary Monte Carlo for rapid phase exploration of compositionally complex materials in reactive environments Monte Carlo method ; Catalysis ; Stability (learning theory) ; Phase (matter) ; Space exploration ; Kinetic Monte Carlo ; Statistical learning Machine Learning in Materials Science ; High Entropy Alloys Studies ; Catalysis and Oxidation Reactions
Authors: A. E. Zorin | K.V. Rochev | Polina Kozhevnikova | V.E. Kuntsev | А А Павлов
Source: Journal of Materials Engineering and Performance (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

Using Machine Learning for Non-destructive Evaluation of Mechanical Properties of Steel Structures Artificial neural network ; Ultimate tensile strength ; Multilayer perceptron ; Deformation (meteorology) ; Fracture (geology) ; Reduction (mathematics) ; Universal testing machine ; Tensile testing Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Surface Treatment and Coatings
Authors: Zhiwen ZHAN | Tao Yang | Xueliang Liu | Yuwei Zhou | Zhou Li‐ping
Source: Journal of Fuel Chemistry and Technology (article, 2025-02-19)
Field: Chemistry — Analytical Chemistry

Machine learning combined with molecular SMILES-based features to predict the heat of formation of carbocations in oil processing <styled-content style="width:10cm;float:right;margin-left:12pt;"><inline-graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="./online/2024-C015-Abstract.jpg"/></styled-content>碳正离子是油品加工等多种化学反应中的关键中间体，其热力学生成焓性质在计算反应焓变、反应能垒、反应速率常数和理解反应机理等方面至关重要。实验上制备和获取碳正离子非常困难，无法直接测量其生成焓性质。目前，主流的做法是通过基团加和法估算，或者通过高精度量子化学方法计算。前者是经验性估算方法，计算快速但误差较大，后者通常基于第一性原理方法，计算精准但计算量巨大。本研究提出一种利用机器学习结合分子SMILES特征进行碳正离子生成焓预测的新方法，实现了以较低计算成本快速准确地预测油品碳正离子的生成焓性质。构建了156个油品烃类碳正离子数据集，利用SMILES特征提取体系信息，考察并利用多个机器学习方法进行模型的训练和构建，获得一个基于支持向量机回归的预测模型，其在训练集和测试集上的决定系数<italic>R</italic><sup>2</sup>达到了0.957和0.966，同时预测的平均绝对误差MAE为2.21 kcal/mol。机器学习结合SMILES特征不仅为油品碳正离子的生成焓预测提供了一种高效实用的策略，还为相关反应过程的热力学和动力学研究开辟了新途径。 Chemistry ; Carbocation ; Medicinal chemistry Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Menghui Wang | Jiashun Shi | Tiantian Zou | Xiao-wen Lin | J. Z. Jiang | Xianfeng Ma | Lin Chen
Source: Journal of Nuclear Materials (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Analysis of ZrCr2-induced performance degradation via high-throughput phase-field simulation and machine learning: The role of mechanical and structural factors Brittleness ; Coating ; Intermetallic ; Artificial neural network ; Nonlinear system ; Surface finish ; Cladding (metalworking) ; Surface roughness Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Additive Manufacturing Materials and Processes
Authors: Faguo Hou | Hong‐Hui Wu | Dexin Zhu | Jinyong Zhang | Liudong Hou | Shuize Wang | Guilin Wu | Junheng Gao | Jing Ma | Xinping Mao
Source: npj Materials Degradation (article, 2025-03-24)
Field: Materials Science — Metals and Alloys

Publisher Correction: Interpretable material descriptors for critical pitting temperature in austenitic stainless steel via machine learning Austenite ; Materials science ; Metallurgy ; Artificial intelligence ; Computer science ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Subramanian K. R. S. Sankaranarayanan | Suvo Banik | Abhishek Aggarwal | Sukriti Manna | P.S. Dutta
Source: Research Square (preprint, 2025-08-18)
Field: Materials Science — Materials Chemistry

Generalized Machine Learning Potential Models for Elemental Nanoclusters Nanoclusters ; Computer science ; Artificial intelligence ; Nanotechnology ; Machine learning ; Materials science Machine Learning in Materials Science
Authors: Shi, Runhan | Chen, Letian | Yu, Gufeng | Yang, Yang
Source: Open MIND (preprint, 2025-11-09)
Field: Materials Science — Materials Chemistry

Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets Chemical reaction prediction remains a fundamental challenge in organic chemistry, where existing machine learning models face two critical limitations: sensitivity to input permutations (molecule/atom orderings) and inadequate modeling of substructural interactions governing reactivity. These shortcomings lead to inconsistent predictions and poor generalization to real-world scenarios. To address these challenges, we propose ReaDISH, a novel reaction prediction model that learns permutation-invariant representations while incorporating interaction-aware features. It introduces two innovations: (1) symmetric difference shingle encoding, which extends the differential reaction fingerprint (DRFP) by representing shingles as continuous high-dimensional embeddings, capturing structural changes while eliminating order sensitivity; and (2) geometry-structure interaction attention, a mechanism that models intra- and inter-molecular interactions at the shingle level. Extensive experiments demonstrate that ReaDISH improves reaction prediction performance across diverse benchmarks. It shows enhanced robustness with an average improvement of 8.76% on R$^2$ under permutation perturbations. Generalization ; Robustness (evolution) ; Sensitivity (control systems) ; Permutation (music) ; Face (sociological concept) ; Mathematical model ; Embedding Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Chunhui Xie | Haoke Qiu | Lu Liu | Yang You | Hongfei Li | Yunqi Li | Zhao‐Yan Sun | Jiaping Lin | Lijia An
Source: SmartMat (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Outside Back Cover: Volume 6 Issue 1 Outside back cover image: Machine learning approaches in the understanding, discovery and optimization of polymer materials. A comprehensive review for the applications of machine learning in the understanding, discovery and optimization of polymer materials. Machine learning based models can facilitate mechanism exploration, targets prediction, high throughput screening, rational and inverse design. (https://onlinelibrary.wiley.com/doi/full/10.1002/smm2.1320) Cover (algebra) ; Volume (thermodynamics) ; Engineering ; Physics ; Mechanical engineering Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Shi, Runhan | Chen, Letian | Yu, Gufeng | Yang, Yang
Source: arXiv (Cornell University) (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets Chemical reaction prediction remains a fundamental challenge in organic chemistry, where existing machine learning models face two critical limitations: sensitivity to input permutations (molecule/atom orderings) and inadequate modeling of substructural interactions governing reactivity. These shortcomings lead to inconsistent predictions and poor generalization to real-world scenarios. To address these challenges, we propose ReaDISH, a novel reaction prediction model that learns permutation-invariant representations while incorporating interaction-aware features. It introduces two innovations: (1) symmetric difference shingle encoding, which extends the differential reaction fingerprint (DRFP) by representing shingles as continuous high-dimensional embeddings, capturing structural changes while eliminating order sensitivity; and (2) geometry-structure interaction attention, a mechanism that models intra- and inter-molecular interactions at the shingle level. Extensive experiments demonstrate that ReaDISH improves reaction prediction performance across diverse benchmarks. It shows enhanced robustness with an average improvement of 8.76% on R$^2$ under permutation perturbations. Generalization ; Robustness (evolution) ; Sensitivity (control systems) ; Permutation (music) ; Face (sociological concept) ; Mathematical model ; Embedding Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Gaolei Xu | Luming Shen | Chengfeng Wang | Yanfu Chai | Zubin Chen | Qing Guo | Xukai Ren | Zhiyu Chang | Gang Zhu | Qinghang Wang
Source: Journal of Materials Engineering and Performance (article, 2025-09-24)
Field: Materials Science — Metals and Alloys

High-Performance Alloy Design of 10Cr Ferrite Steel: An Integrated Approach of Single-Objective Optimization and Machine Learning Support vector machine ; Random forest ; Cluster analysis ; Gradient boosting ; Ultimate tensile strength ; Regression analysis ; Multivariate adaptive regression splines ; Boosting (machine learning) ; Predictive modelling Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: N. K. Thakre | Eswararao Boddepalli | Ajmeera Kiran | Monika Gorkhe | Sajiv G | S. Manochitra
Source:  (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Multi-Scale Modeling of Next-Generation Energy Storage Technologies Incorporating Quantum Materials for Improved Performance The advancement of next-generation energy storage technologies is essential to meet the growing demands for efficient, high-capacity, and sustainable power systems. Traditional battery modeling techniques–such as purely physics-based or data-driven models–often fall short in capturing quantum-scale phenomena and fail to scale across different simulation layers. These limitations hinder the accurate prediction of material behavior under dynamic operational conditions. This study proposes a Quantum-Inspired Multi-Scale Modeling Framework that integrates quantum embedding techniques, atomistic simulations, continuum modeling, and machine learning surrogates to enhance battery design and analysis. The objective is to develop a unified, scalable approach capable of simulating complex material interactions from electronic to device levels. Novelty lies in coupling Density Matrix Embedding Theory (DMET), molecular dynamics (LAMMPS), finite element methods (COMSOL), and Graph Neural Networks to form a cohesive simulation pipeline. The framework demonstrates superior accuracy (99.8%) with high scalability and moderate computational speed, significantly outperforming traditional methods like PINN-surrogate P2D models or hybrid ML-SPM approaches. Key findings reveal improved predictions of energy density, charge rate, and stability, especially in quantum-enhanced materials. Validation against the OBELiX dataset confirms the model's robustness. Tools used include Quantum ESPRESSO, LAMMPS, COMSOL Multiphysics, and PyTorch-Geometric, supporting a modular and extendable simulation environment for advanced energy storage systems. Scalability ; Modular design ; Embedding ; Multiscale modeling ; Energy storage ; Battery (electricity) ; Key (lock) ; Coupling (piping) ; Quantum Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Electrocatalysts for Energy Conversion
Source: Journal on Materials and its Characterization (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Predicting Electrical Conductivity of Metal Halide and Nitrate Solutions Using Ada Boost Regression A Machine Learning Approach This study investigates the electrical conductivity of metal halide and nitrate solutions as a function of concentration and temperature to improve electrolyte performance in electrochemical applications. Traditional electrolytes pose safety concerns due to flammability and toxicity, thus requiring alternative solutions. The research addresses the knowledge gap in understanding structure-transport relationships in ionic solutions under various conditions. Using a dataset of 100 observations, Ada Boost regression models were developed to predict conductivity behaviour. The results demonstrate strong positive correlations between concentration and conductivity for both halide (r=0.90) and nitrate (r=0.91) systems, with concentration dominating over temperature. The Ada Boost models achieved exceptional predictive accuracy (R²&gt;0.98 for both halide and nitrate conductivity in the experimental data), with minimal error measurements confirming strong generalization. This machine learning approach successfully captures nonlinear ion transport mechanisms, providing a reliable framework for designing optimal electrolyte formulations in advanced battery and super capacitor technologies. Halide ; Electrolyte ; Conductivity ; Ionic conductivity ; Electrical resistivity and conductivity ; Linear regression ; Regression ; Regression analysis ; Support vector machine Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Valentín Vassilev-Galindo | Javier LLorca
Source: Chemical Science (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR The extraordinary progress of strategies coupling <i>ab initio</i> calculations and machine learning (ML) has opened the door for both fast and accurate chemical/physical property predictions and for the virtual design of materials. However, these techniques are very often used as a "black box" with the sole objective of obtaining high accuracy with scarce or no special attention on how ML models obtain their predictions. This can be improved by leveraging explainability of ML models, which, at the same time, would increase the chance of ML to offer new insights into the chemistry and physics of materials. Hence, the next generation of ML models in these realms must guarantee explainability by embedding explainable artificial intelligence (XAI) tools into their pipelines. Specifically, ML-assisted materials discovery and design can take great advantage of the use of XAI. Enabling explanations would increase the impact of these approaches by providing not only a set of candidates, but also insights into what makes a given material better than others. With this in mind, using the example of heterogeneous catalysts for hydrogen production and energy generation, here we propose a novel strategy for materials design based on counterfactual explanations. We were able to find materials featuring properties close to the design targets that were later validated with density functional theory calculations. Explanations were devised by comparing original samples, counterfactuals, and discovered candidates. Such explanations allowed us to unveil subtle relationships between the most relevant features, other, in principle, less important features, and the target property. Since this approach can be applied to different applications, this work provides an alternative to already available designing strategies, such as high-throughput screening or generative models, but that, for the first time, incorporates explainability as its main driving mechanism. Counterfactual thinking ; Embedding ; Set (abstract data type) ; Property (philosophy) ; Coupling (piping) ; Energy (signal processing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials