jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Panpan Zhou | Lixin Chen
Source: Journal of Magnesium and Alloys (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Machine learning and non-thermal external fields: A dual-track paradigm for engineering high-performance of Mg-based hydrogen storage materials • A dual-track paradigm for Mg-based hydrogen storage materials was proposed. • Machine learning can accelerate the design and screening of novel materials. • Non-thermal external fields enable efficient synthesis and superior properties. Confronted with the inherent thermodynamic and kinetic challenges of Mg-based hydrogen storage materials (HSMs), research strategies have evolved from traditional alloying, nano-structuring, and catalytic modification toward a new dual-track paradigm that integrates machine learning (ML)-based rational design with non-thermal external field (NTEF)-assisted precision regulation. ML significantly accelerates the design and screening of novel HSMs through efficient performance prediction, key descriptor extraction, and microscopic mechanism elucidation, while NTEF provides a powerful experimental means for non-equilibrium synthesis, microstructure optimization, and de-/hydrogenation behavior control by leveraging its unique high energy efficiency and tunability. These two approaches complement each other and jointly advance Mg-based HSMs toward higher performance, lower cost, and superior cycling stability, thereby establishing a solid scientific and technological foundation for safe, efficient hydrogen storage and transportation as well as large-scale hydrogen-thermal coupled application. Hydrogen storage ; Field (mathematics) ; Rational design ; Key (lock) ; Energy storage ; Material Design ; Mechanism (biology) Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; Electrocatalysts for Energy Conversion
Authors: Haowei Hua | Chen Liang | Ding Pan | King, Irwin | Liu, Shengchao | Koji Tsuda | Wanyu Lin
Source: arXiv (Cornell University) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $α_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials. Equivariant map ; Dielectric ; Scalability ; Artificial neural network ; Nonlinear system ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Ziduo Yang | Yi-Ming Zhao | Xian Wang | Wei Zhuo | Xiaoqing Liu | Lei Shen
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Equivariant Atomic and Lattice Modeling Using Geometric Deep Learning for Crystal Structure Optimization Structure optimization, which yields the relaxed structure (minimum‑energy state), is essential for reliable materials property calculations, yet traditional ab initio approaches such as density‑functional theory (DFT) are computationally intensive. Machine learning (ML) has emerged to alleviate this bottleneck but suffers from two major limitations: (i) existing models operate mainly on atoms, leaving lattice vectors implicit despite their critical role in structural optimization; and (ii) they often rely on multi-stage, non-end-to-end workflows that are prone to error accumulation. Here, we present E³Relax, an end-to-end equivariant graph neural network that maps an unrelaxed crystal directly to its relaxed structure. E³Relax promotes both atoms and lattice vectors to graph nodes endowed with dual scalar–vector features, enabling unified and symmetry‑preserving modeling of atomic displacements and lattice deformations. A layer‑wise supervision strategy forces every network depth to make a physically meaningful refinement, mimicking the incremental convergence of DFT while preserving a fully end‑to‑end pipeline. We evaluate E³Relax on four benchmark datasets and demonstrate that it achieves remarkable accuracy and efficiency. Through DFT validations, we show that the structures predicted by E³Relax are energetically favorable, making them suitable as high-quality initial configurations to accelerate DFT calculations. Equivariant map ; Lattice (music) ; Ab initio ; Bottleneck ; Crystal structure ; Artificial neural network ; Deep learning ; Graph theory ; Workflow Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Graph Neural Networks
Authors: Songjia Kong | Ziheng Yu | Naoki Matsui | Michiyo Kamiya | Yudai Iwamizu | Yuki Tanaka | N. Kubota | Kuniharu Nomoto | Satoshi Hori | Masaaki Hirayama | Kota Suzuki | Ryoji Kanno
Source: Small (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes (Small 2/2026) Lithium-Ion Electrolytes The cover illustrates the composition-based machine learning framework Elements-To-Ionics (E2I) for predicting and optimizing ionic conductivities of argyrodite-type solid-state electrolytes. Guided by E2I predictions, in article number 2509918, Kota Suzuki, Ryoji Kanno, and co-workers successfully explored Si–Sn, Ge–Si, and Ge–Sn co-doped Li6PS5I systems, achieving ionic conductivities comparable to superionic conductors. This work highlights the potential of composition-based informatics for accelerating next-generation solid-state battery development. Electrolyte ; Ionic bonding ; Ionic conductivity ; Fast ion conductor ; Work (physics) ; Battery (electricity) ; Cover (algebra) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Hannah N. Turney | Micaela Matta
Source: ChemRxiv (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Accurate and robust prediction of polymer glass transition temperature using neural network charges and unsupervised machine learning Accurate computational modeling of the glass transition is a longstanding challenge in polymer chemistry. Atomistic molecular dynamics simulations offer a powerful approach to observe the changes in viscoelasticity, density and chain mobility associated with the glass transition, yet they often overestimate the glass transition temperature (Tg) and have limited sensitivity to number average molecular weight and dispersity. These deficiencies are largely attributed to high levels of noise in simulation data masking the Tg, inaccurate polymer representations, and limited force field transferability. Glass transition ; Noise (video) ; Artificial neural network ; Polymer ; Masking (illustration) ; Unsupervised learning ; Molecular dynamics ; Field (mathematics) Material Dynamics and Properties ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Jacob Steiner | Yasser Ismail | William Glumbfskie | Jack Price
Source: Corrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Predicting the degree of sensitisation of AA5456 alloys through convolution neural networks using microstructural images The degree of sensitisation is a reliable measure of the susceptibility to intergranular corrosion for Al-Mg and Al-Mg-Mn marine-grade alloys (Al 5XXX series). Measuring the degree of sensitisation is an exhaustive and expensive operation, as it requires destructive sectioning and outsourcing the measurement to a laboratory to determine the necessary corrosion risk level. This paper proposes an alternative method to predict degree of sensitisation using a convolutional neural network trained on microstructural images of AA5456 alloy specimens etched with phosphoric acid to reveal β-phase precipitates along grain boundaries. Two convolutional neural network architectures are proposed to predict the degree of sensitisation of AA5456 specimens from 490 microstructural images at 200× and 500× magnification. An overall accuracy of 83% for a continuous prediction convolutional neural network model and 87% for a classification prediction convolutional neural network model. The high classification accuracy reflects the success of the augmentation process, which increased the dataset to 9000 images with an equal distribution of degree of sensitisation targets. Convolutional neural network ; Degree (music) ; Artificial neural network ; Pattern recognition (psychology) ; Convolution (computer science) ; Alloy Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Corrosion Behavior and Inhibition
Authors: Matthew C. Carson | Alice Wu | Kalyana B. Duggal | Madeline E. Rotella | Marisa C Kozlowski
Source: Angewandte Chemie International Edition (article, 2026-03-05)
Field: Chemistry — Organic Chemistry

Photocatalytic Cross‐Coupling of Phenols and Heteroaryl Halides With Machine Learning‐Guided Reaction Prediction Developing sustainable methods for C(sp<sup>2</sup>)─C(sp<sup>2</sup>) bond formation that avoid transition-metals and prefunctionalized substrates remains a central goal in synthetic chemistry. Phenols and N-heteroarenes (azines) are abundantly available, yet their cross-coupling is hindered by mismatched redox properties and chemoselectivity issues. Herein, we report a photochemical strategy that couples phenols with heteroaryl halides under redox-neutral conditions using an organic dye photocatalyst and base. Concurrent oxidation of the phenol component and reduction of the azine component generates complementary radicals that cross-couple efficiently, delivering moderate to high yields (up to 91%) with high functional group tolerance. Mechanistic experiments and density functional theory (DFT) studies elucidate the radical reaction pathways, while substrate clustering, high-throughput experimentation (HTE), and machine learning (ML) enable prediction of C-C versus S<sub>N</sub>Ar reactivity across broad chemical space. Chemoselectivity ; Phenols ; Phenol ; Photocatalysis ; Halide ; Reactivity (psychology) ; Radical ; Functional group ; Redox Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Yuhong Li | J.M. Xu | Bo Guan | Yushuai Han | Wenzheng Liu | D. Wu | J. Wang | Guohong Ma | Zhi Hu | Jin Zou | Qiang Hu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Design of Cu-Ni-Si alloy with ultra-high strength and high conductivity based on machine learning model Ultimate tensile strength ; Electronegativity ; Artificial neural network ; Alloy ; Electrical resistivity and conductivity ; Gradient boosting ; Support vector machine ; Entropy (arrow of time) ; Kriging Machine Learning in Materials Science ; Microstructure and mechanical properties ; Metallurgical and Alloy Processes
Authors: Hongxing Lin | Jie Jiang | T. Zhang | Xiaodi Cui | Chenyang Li | Jinjin Li | Ling Zhao | Zhenhao Xi
Source: Macromolecules (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Adaptive Fusion of Graph Neural Networks and Fingerprints via Gating for Multitask Prediction of Polymer Thermal Properties Polymer thermal properties, including glass transition (Tg), melting (Tm), decomposition (Td), and crystallization (Tc) temperatures, alongside the softening point (SP), are pivotal for material design yet challenging to determine efficiently via laborious experiments or computationally intensive simulations. To leverage the complementary strengths of diverse molecular representations, this work introduces a deep learning framework featuring a gated fusion mechanism that integrates molecular graph representations and hybrid fingerprint descriptors. Evaluation on five thermal properties demonstrates that the proposed model shows improved performance over traditional benchmarks in single-task learning. Ablation studies and gating mechanism analysis reveal that the model adaptively prioritizes graph or fingerprint features, enabling effective multimodal fusion. Furthermore, a multitask learning strategy exploits latent correlations to reduce prediction errors for properties with limited data, offering an efficient and unified framework. This dual approach provides a competitive tool for accelerating data-driven material discovery. Leverage (statistics) ; Fusion ; Graph ; Exploit ; Artificial neural network ; Scalability ; Gating ; Domain adaptation Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zhi Hao Jiang | Marco Peres | Carlo Bradac | Gil Gonçalves
Source: Advanced Intelligent Systems (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis Diamond and diamond color centers have become prime hardware candidates for solid‐state‐based technologies in quantum information and computing, optics, photonics, and (bio)sensing. The synthesis of diamond materials with specific characteristics and the precise control of the hosted color centers is thus essential to meet the demands of advanced applications. Yet, challenges remain in improving the concentration, uniform distribution, and quality of these centers. Here, we perform a review and meta‐analysis of some of the main diamond synthesis methods and their parameters for the synthesis of N‐, Si‐, Ge‐, and Sn‐vacancy color centers. We extract quantitative data from over 60 experimental articles and organize it in a large database (170 datasets and 1692 entries). We then use the database to train two machine learning algorithms to make robust predictions about the fabrication of diamond materials with specific properties from careful combinations of synthesis parameters. We use traditional statistical indicators to benchmark the performance of the algorithms and show that they are powerful and resource‐efficient tools for researchers and material scientists working with diamond color centers and their applications. Diamond ; Benchmark (surveying) ; Fabrication ; Quality (philosophy) ; Diamond turning ; Control (management) Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: L.C. Liu | N.Y. Wu | Z.H. Pan | Z.Y. Xu | Sheng Zhou
Source: Journal of Membrane Science (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Machine-learning design V alloys with high hydrogen embrittlement resistance Hydrogen embrittlement ; Hydrogen ; Work (physics) ; Alloy ; RADIUS ; Density functional theory ; Embrittlement Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals ; Electrocatalysts for Energy Conversion
Authors: Yichuan Wang | Shuhan Liu | Hepeng Zhang | Shuai Chen | Chaoqun Xia | Tai Yang
Source: Energy (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

Breaking small sample data barriers: Machine learning prediction of hydrogen storage capacity in Mg-based alloys via data augmentation Generalization ; Sample (material) ; Hydrogen storage ; Small data ; Computer data storage ; Sample size determination ; Generalization error ; Data modeling Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; Machine Learning in Materials Science
Authors: Cassidy Hihara | Ryan Sugamoto | L.H. Hihara | P.S. Branicio
Source: CORROSION (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Data-Driven Machine Learning Analysis of Nonlinear Environmental Drivers of Marine-to-Inland Atmospheric Corrosion in Aluminum Alloys Machine learning (ML) offers a powerful approach to analyze the complex, nonlinear interactions in industrial and marine environments where atmospheric conditions involving moisture and chemical pollutants accelerate degradation on different alloys. An accurate understanding of what affects corrosion rates is essential for enhancing the durability and maintenance of aluminum-based infrastructure. Thus, this study applies ML to analyze the corrosion rates of aluminum alloys (AA) 1060, AA 2024, AA 6061, and AA 7075 based on experimental data collected from diverse environments worldwide, with many sites in Hawaii. A CatBoost gradient boosting model was developed incorporating key environmental variables, including average time of wetness and deposition rates of ions such as F−, Cl−, Br−, , , , Li+, Na+, , K+, Mg2+, and Ca2+. Model performance was optimized using the Optuna framework for hyperparameter tuning. The model effectively captured nonlinear relationships in the corrosion behavior of aluminum alloys, revealing the influence of various atmospheric parameters and material properties on corrosion rate variability. Thus, these findings underscore the potential of ML to advance data-driven corrosion modeling, offering insights relevant to material selection, performance evaluation, and experimental design strategies aimed at developing a predictive model and providing insight into lifecycle management in uncontrolled atmospheric environments. Corrosion ; Aluminium ; Hyperparameter ; Durability ; Pollutant ; Deposition (geology) Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Javeriya Sayed
Source: Global Trends in Science and Technology (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

AI Enhanced Organic Chemistry: Emerging Tools and Computational Strategies Artificial intelligence (AI) is transforming organic chemistry by making it possible to make predictive and data-driven contributions to the design of molecules, synthesis of molecules, and optimization of reactions. Machine learning, deep learning and cheminformatics AI applications are used to predict reactions, plan retrosynthetic, and analyze computations, and save time and resources on experimentation. New instruments and platforms combine AI and automation, high-throughput experimentation and molecular modeling, which is speeding up the discovery in pharmaceuticals, materials and green chemistry. Even though some of the challenges to AI have been the data quality, interpretability, and experimental translation, the opportunities that AI presents are enormous and unexplored. This review also mentions existing applications, case studies, computational strategies, and future views and demonstrates the transformative influence of AI in the field of modern organic chemistry. Cheminformatics ; Transformative learning ; Field (mathematics) ; Applications of artificial intelligence ; Plan (archaeology) ; Expert system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Saman Naseri Boroujeni | George Jackson | Amparo Galindo | Claire S. Adjiman
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

COSMO-NET: Fast and Accurate Machine Learning Surrogates for COSMO-based Molecular Descriptors Accurate predictive thermodynamic models are essential tools for the computational design of new molecules. Models based on COnductor-like Screening Models (COSMO), such as COSMO-SAC and COSMO-RS, are well-suited for this purpose but require computationally expensive quantum mechanical (QM) calculations to generate key properties-the surface charge density distribution (p(σ)), the surface area (A) and the cavity volume (V)-for each molecule. Here we develop graph-based neural network surrogate models, based on a directed message passing neural network (DMPNN) and a graph convolutional network (GCN), to predict these molecular properties rapidly and accurately. We train the models on a dataset of over 16,000 compounds generated through an automated QM calculation pipeline. The DMPNN model outperforms GCN for probability of surface charge density prediction, while the GCN shows higher accuracy for surface area and cavity volume. We build on these strengths to propose a hybrid model, COSMO-NET. When applied to predict octanol-water partition coefficients, COSMO-NET achieves a mean absolute error (MAE) of 0.31 compared to 0.34 and 0.36 for DMPNN and GCN, respectively. These results demonstrate that machine learning surrogates can replace costly QM calculations while maintaining accuracy, supporting the discovery of new molecules and the evaluation of their performance. Artificial neural network ; Convolutional neural network ; Graph ; Key (lock) ; Surface (topology) ; Partition (number theory) ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Mingzhang Pan | Tian Zhang | Jiawei Dong | Yubao Xie | Ke Zhong | Wei Guan | H. Wei | J. Fu | Yaqiong Su
Source: Advanced Science (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high-throughput density functional theory (DFT) and interpretable machine learning to accelerate the rational design of catalysts. Computational screening of face-centered-cubic (FCC) bimetallic catalyst surfaces identifies the bond cleavage energies of the first and the second C─H bonds and methyl adsorption energy as a key descriptor governing successive C─H activation and the shift in the rate-determining step (RDS). Through the synergistic interaction of these descriptors, machine learning models can be constructed more effectively, leading to the discovery of a bimetallic catalyst for consecutive C─H bond cleavages that outperforms conventional natural gas engine aftertreatment systems. Based on the computationally derived DFT dataset, four machine learning models were trained using a particle swarm optimisation (PSO) algorithm, from which the optimal model capable of accurately predicting C─H bond energies was selected. This model also further revealed the dominant electronic structural features of the predictive model through SHapley additive interpretability (SHAP) analysis. This work establishes an interpretable, data-driven methodology for designing high-efficiency multicomponent catalysts. Bimetallic strip ; Interpretability ; Rational design ; Density functional theory ; Catalysis ; Quantitative structure–activity relationship ; Work (physics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming