jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dandan Ren | Mei Hong | Ruiying Li | Yu Zhou | Zheng Wang | C.R. Li | Jieqiong Ding | Fei Wang | Xingchen Liu | Weixin Huang | Xiaodong Wen | Zhenhua Zhang
Source: Journal of the American Chemical Society (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Deciphering the Interfacial Catalysis of Metal–Oxide Nanocatalysts in CO <sub>2</sub> Hydrogenation through a Machine Learning Approach Understanding the structure-property relationship is of pivotal importance for the rational design of efficient solid catalysts yet persists as a significant challenge due to the inherent complexity of solid materials. Here, we present an efficient strategy to decipher the interfacial catalysis in metal-oxide nanocatalysts during CO<sub>2</sub> hydrogenation through synergistically integrated experimental and theoretical investigations with machine learning (ML) algorithms. Using model Pd/CeO<sub>2</sub> catalysts as a proof-of-concept system, the matched experimental and ML-predicted results demonstrate that given sufficient oxygen vacancy concentrations on the support matrix the catalytic performance is primarily governed by the nature of supported metals. Specifically, atomically dispersed Pd species exhibit exceptional intrinsic activity for CO production, which is attributed to its enhanced H spillover and hydrogenation capacities but weakened CO binding affinity. Further ML analysis indicates the sum surface d charge of supported metal as the principal factor governing catalytic performance among four types of typical intrinsic features influencing catalytic hydrogenation processes, which could be directly evaluated by a geometric descriptor of average coordination number of the metal on the support, associated with the metal particle size. This work provides a generalizable theoretical framework for understanding metal-oxide interfaces in CO<sub>2</sub> hydrogenation and opens up a novel approach for catalytically fundamental studies to unravel the complex nature of heterogeneous catalysis. Nanomaterial-based catalyst ; Catalysis ; Rational design ; Heterogeneous catalysis ; Metal ; Work (physics) ; Particle (ecology) Machine Learning in Materials Science ; Catalysts for Methane Reforming ; CO2 Reduction Techniques and Catalysts
Authors: Zhenfeng Tan | Yuanming Zhu | Bin Shao | Feng Qian | Jun Hu
Source: ACS Catalysis (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Thermodynamics-Guided Machine Learning Framework with a Multiobjective Optimizer for Catalyst Discovery Machine learning (ML) holds great promise for discovering catalysts; however, simultaneously possessing high interpretability, prediction accuracy, and catalyst discovery efficiency remains a substantial challenge. Here, an ML framework with thermodynamic guidelines from scratch is constructed to explore superior multimetallic catalysts for the representative reverse water–gas shift (RWGS) reaction. A normalization approach is employed to redefine the elemental physicochemical properties, leveraging the advantages of the elemental and property features of active metals. This not only enables accurate predictions but also derives the value range of each feature through deep insights into the catalytic mechanisms. More importantly, a genetic algorithm (GA)-based multiobjective optimizer is developed for reverse engineering the compositions of multimetallic catalysts. Specifically, by enforcing explicit thermodynamic constraints and self-correcting by experimentally validated results into the ML models, both rigorous discovery of catalysts within the training data set and extrapolation are successfully achieved. After two rounds of self-corrections, optimal ternary-metallic catalysts are experimentally validated with a superior CO yield close to the equilibrium limitation. Therefore, this ML framework is a promising paradigm for catalyst research, as highlighted by intrinsic theoretical guidance and experimental validation. Extrapolation ; Catalysis ; Normalization (sociology) ; Yield (engineering) ; Range (aeronautics) ; Set (abstract data type) ; Property (philosophy) ; Scratch Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Mamduh J. Aljaafreh | Sajjad H. Sumrra | Sadaf Noreen
Source: ACS Omega (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

A Machine Learning Quest to Design Molecular Graph Fingerprints of Organic Chromophores for Adjusting Photoluminescent Quantum Yields The design of organic chromophores with a high photoluminescent quantum yield (PLQY) is crucial for various optoelectronic applications. However, the vast chemical space of organic chromophores poses a significant challenge for experimental screening. Here, we report a molecular fingerprinting-based deep learning pipeline to discover organic chromophores with the desired PLQY. We convert 713 organic chromophores into 2048-bit fingerprints and screen them using machine learning (ML) techniques to predict their effect on PLQY. Support vector and gradient boosting regressor models achieve good predictive performance, with <i>R</i> <sup>2</sup> values ranging from 0.68 to 0.88. By breaking retrosynthetic analysis, we designed 5200 new organic chromophores with desirable PLQY. Furthermore, we visualize and screen 1840 chromophores using structure-activity landscape analysis. Our work demonstrates the power of molecular fingerprinting and ML in designing new chromophores with desired optical properties, providing a useful strategy for accelerating the discovery of high-performance organic materials. Chromophore ; Boosting (machine learning) ; Photoluminescence ; Chemical space ; Pipeline (software) ; Quantum yield ; Molecular engineering Machine Learning in Materials Science ; Luminescence and Fluorescent Materials ; Graph theory and applications
Authors: Guanghai Bai | Jun Liu | Yuan Yue | Zhanwang Zheng | B. Chen | Weijia Gong | Yanwei Zhang | Huanchun Wu | Runzhong Wang | Jinshan Li
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Study on the Composition Design and Corrosion Resistance of Zirconium Alloys Based on Machine Learning Corrosion ; Zirconium ; Zirconium alloy ; Valence (chemistry) ; Cladding (metalworking) ; Artificial neural network ; Alloy ; Feature selection Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Xin-Xuan Wang | Zhen-Shuai Lei | Wenjuan Li | Xiaobin Feng | Gang Chen | Peng-Cheng Zhai | Bo Duan | Guo-Dong Li | Qing-Jie Zhang
Source: Research Square (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

Ductile Mg-Te-Pb Thermoelectric Materials with Ultralow Lattice Thermal Conductivity Predicted by a Deep Learning Potential Model Thermoelectric effect ; Thermoelectric materials ; Seebeck coefficient ; Thermal conductivity ; Thermoelectric generator ; Crystal structure Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Leonardo Medrano Sandonas | David Balcells | Anton Bochkarev | Jacqueline M. Cole | Volker L. Deringer | Werner Dobrautz | Adrian Ehrenhofer | Thorben Frank | Pascal Friederich | Rico Friedrich | Janine George | Luca M. Ghiringhelli | Alejandra Hinostroza Caldas | Veronika Jurásková | Hannes Kneiding | Yury Lysogorskiy | Johannes T. Margraf | Hanna Türk | Anatole von Lilienfeld | Milica Todorović | Alexandre Tkatchenko | Mariana Rossi | Gianaurelio Cuniberti
Source: ArXiv.org (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Perspective: Towards sustainable exploration of chemical spaces with machine learning Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics. Bridging (networking) ; Benchmarking ; Big data ; Sustainability ; Resource (disambiguation) ; Resource efficiency Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Julian Kimmig | Yannik Köster | Timo Koswig | Punith Raviswamy | Subhash V. S. Ganti | Stefan Zechel | Christopher Kuenneth | Ulrich S. Schubert
Source: Macromolecular Rapid Communications (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles Machine learning applications in polymer science are often inefficient due to molecular representations that neglect the inherent hierarchical and statistical nature of macromolecules. This work introduces a structure-aware graph convolutional network (GCN) framework that addresses this limitation by treating polymer samples as statistical ensembles. The approach utilizes a hierarchical graph representation where nodes correspond to monomer units and explicitly integrates molecular mass distribution (MMD) data to account for sample dispersity. A key innovation is an ensemble-based training strategy using topologically realistic graphs generated on-demand via an optimized kinetic Monte Carlo simulation. The model's efficacy was validated on a broad range of tasks. On synthetic data, it achieved more than 98% accuracy in classifying complex polymer architectures. When applied to a large experimental dataset, the model predicts glass transition temperatures (T<sub>g</sub>) with high accuracy (R<sup>2</sup> = 0.89 ± 0.01). Crucially, a fine-tuning experiment demonstrated that the model could successfully learn the physically / chemically grounded relationship between T<sub>g</sub> and molar mass by integrating MMD information. This work establishes a robust and physically realistic paradigm for polymer informatics, enabling more accurate property predictions and paving the way for accelerated in silico material design. Graph ; Representation (politics) ; Monte Carlo method ; Statistical model ; Convolutional neural network ; Range (aeronautics) ; Complex network ; Graph theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Kiarash Farajzadehahary | Nicholas Ballard
Source: Advanced Intelligent Discovery (article, 2026-03-01)
Field: Chemistry — Organic Chemistry

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers The transition toward sustainable polymer materials would be helped by the replacement of petroleum‐based monomers with bio‐based alternatives. This work presents an integrated machine learning framework that aims at accelerating this transition by simultaneously predicting four critical properties of bio‐based monomers to be used in emulsion polymerization processes: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. Improved models for each property were developed using neural networks and gradient boosting approaches, demonstrating enhanced predictive accuracy compared to existing methods. These models were integrated into a unified selection tool that systematically evaluates candidate bio‐based monomer pairs as replacements for conventional formulations, while maintaining target copolymer properties. The framework was validated through two experimental case studies: replacement of n‐butyl acrylate/methyl methacrylate for coating applications, and replacement of n‐butyl acrylate/styrene for adhesive applications. Using the selected bio‐based candidates, both bio‐based systems achieved high conversions and demonstrated glass transition temperatures comparable to their conventional counterparts, confirming the framework's capability to identify viable bio‐based alternatives to conventional petrochemical based polymeric systems. Glass transition ; Polymer ; Monomer ; Methacrylate ; Emulsion polymerization ; Coating ; Work (physics) Advanced Polymer Synthesis and Characterization ; Polymer composites and self-healing ; Machine Learning in Materials Science
Authors: Yang Zhao
Source: IET conference proceedings. (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Research on microstructure image recognition and performance prediction of energy materials based on deep learning A unified framework for microstructure image recognition and performance prediction of energy materials based on multi-scale deep learning is proposed to break through the efficiency bottleneck of traditional "trial and error method" in material research and development. Aiming at the problems of multi focus single scale structure recognition and structural performance modeling fracture in existing methods, a dual branch convolutional neural network (CNN) is constructed to extract local texture and global morphological features of scanning electron microscopy (SEM) images, and a convolutional block attention module (CBAM) is introduced to achieve adaptive feature fusion; Further design a multi task learning architecture, jointly optimize microstructure classification and performance regression objectives, and enhance feature physical interpretability. The experiment was based on a self built solid-state electrolyte SEM image dataset (1250 multi-scale samples, labeled with 4 key structures and calibrated for ion conductivity). The results showed that the complete model achieved an accuracy of 93.8% in structure recognition tasks, with a macro F1 score of 0.926; In performance prediction tasks, the root mean square error (RMSE) was reduced to 0.34 mS/cm, and the R² was increased to 0.98, significantly better than single branch CNN and traditional manual feature methods. Grad-CAM visualization shows that the model focuses on the key areas such as grain interior and continuous grain boundary, which is highly consistent with the theory of materials science, and verifies the interpretability of the "structure-properties" relationship. This study provides an efficient and accurate end-to-end solution for data-driven energy material design. Convolutional neural network ; Interpretability ; Feature (linguistics) ; Deep learning ; Pattern recognition (psychology) ; Energy (signal processing) ; Mean squared error ; Focus (optics) Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Machine Learning in Bioinformatics
Authors: Yincan Sun | Zhigang Ding | Jincheng Kan | Yonghao Zhao | Shuang Li | Xiang Chen
Source: Physical Review Materials (article, 2026-03-12)
Field: Materials Science — Biomaterials

Crack initiation and propagation in magnesium-lithium alloys: Revealed by machine learning potential Accurately predicting the crack behavior of magnesium-lithium (Mg-Li) alloys through atomic-scale simulations is essential for understanding crack-defect interactions and designing high-plasticity alloys. However, existing molecular dynamics potentials for Mg-Li alloys are inadequate for reliably modeling crack-related phenomena, such as stacking fault energies and dislocation core structures. In this work, by combining 1722 density functional theory (DFT) calculated data points with machine learning method, we develop a broadly applicable machine learning potential (MLP) for Mg-Li alloys. This MLP accurately reproduces lattice constants, elastic constants, surface energies, and generalized stacking fault energy curves, showing reasonable agreement with DFT data. Furthermore, MLP simulations reveal that either the continuous formation of basal ${\mathrm{I}}_{2}$ stacking faults or the formation of basal ${\mathrm{I}}_{1}$ stacking faults trigged by the cross-slip of pyramidal dislocation effectively relieves stress concentration at the crack tip, resulting in crack blunting. These findings help clarify the experimental debate regarding the dual role of basal slip: basal slip bands can also delay crack propagation, while discontinuous basal slip can initiate fatigue cracks. Stacking ; Slip (aerodynamics) ; Stacking-fault energy ; Stacking fault ; Dislocation ; Lattice (music) ; Basal plane ; Density functional theory Magnesium Alloys: Properties and Applications ; Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties
Authors: Agnes Valenti | Ina Park | Antoine Georges | Andrew J. Millis | Olivier Parcollet
Source: arXiv (Cornell University) (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Neural-Network Quantum Embedding Solvers for Correlated Materials Quantum impurity solvers are the computational bottleneck of quantum embedding approaches to correlated materials, such as dynamical mean-field theory (DMFT). We show that neural networks trained on synthetic, material-agnostic data learn the impurity mapping from hybridization functions and local interactions to Green's functions with quantitative accuracy for both model systems and real materials, providing fast solvers for single- and multi-orbital models. Benchmarks against numerically controlled quantum Monte Carlo show that the method reproduces the Mott transition, multi-orbital phase diagrams of Hubbard-Kanamori models, and the electronic properties of SrVO$_3$ and SrMnO$_3$. The learned solvers achieve orders-of-magnitude speedup and can initialize controlled calculations, dramatically accelerating DMFT while preserving accuracy. Embedding ; Quantum ; Bottleneck ; Speedup ; Quantum Monte Carlo ; Monte Carlo method ; Quantum computer Machine Learning in Materials Science ; Quantum many-body systems ; Electronic and Structural Properties of Oxides
Authors: Guixin Chen | Yonggang Yan | Yi  Fan Zhao | Hui Li | Dong Wang | Xunxiang Hu
Source: Journal of Alloys and Compounds (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

A machine learning framework for high-strength Mo-Re alloy design Ultimate tensile strength ; Alloy ; Gaussian process ; Atomic radius ; Fabrication ; RADIUS ; Process (computing) ; Space (punctuation) ; Mechanical strength ; Tensile testing Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties ; Advanced materials and composites
Authors: Zhengkun Zhan | Maolian Gong | Zirui Zeng | Hangyu Liu | Xu‐zhao Li | Zhuxin Dong | Xiaoju Men | Haobin Chen
Source: Energy & environment materials (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Machine Learning–Enhanced Fluorescence Cryptography with <scp>NADH</scp> ‐Responsive Polymer Dots The development of high security anticounterfeiting technologies is crucial to address the escalating demand for information security. Stimuli‐responsive fluorescent materials have garnered significant interest for advanced information encryption due to their dynamic signal outputs and compatibility with intelligent analysis tools. In this work, we fabricate and characterize semiconducting polymer dots (Pdots) that exhibit distinct, reproducible fluorescence signal changes in response to varying concentrations of NADH, a biologically relevant reducing agent. We establish a robust encryption‐decryption platform by integrating machine learning algorithms, wherein encrypted messages are encoded via NADH‐triggered fluorescence changes and decoded exclusively using pretrained models. This approach provides a secure, cost‐effective, and intelligent anticounterfeiting strategy, demonstrating the considerable potential of synergistically coupling chemical sensing with artificial intelligence for data protection. Encryption ; Fluorescence ; SIGNAL (programming language) ; Cryptography ; Quantum dot ; Coupling (piping) Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques
Authors: Shihao Feng | Wenyang Ding | Huichao Chen | Meng An | Gang Zhang
Source: ACS Applied Energy Materials (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Screening Liquid Cooling Materials via Enthalpy of Vaporization Liquid cooling (LC) is increasingly important for thermal management in high-power electronic systems, yet the identification of suitable working fluids remains limited by an insufficient understanding of molecular structure-property relationships and reliance on empirical screening. Herein, we develop an interpretable machine learning (ML) framework for the systematic evaluation of LC materials based on the enthalpy of vaporization, a key thermodynamic parameter governing phase-change heat transfer capacity. A curated data set of 2474 organic compounds was constructed using SMILES-derived molecular descriptors, and multiple regression models were trained and optimized. The gradient boosting decision tree (GBDT) model achieved a test-set R2 of 0.99 under random splitting and was subsequently applied to screen 136 candidate compounds. Additional assessments of synthetic accessibility, safety, and operating temperature compatibility enabled the prioritization of promising LC materials. Interpretability analyses identify four key molecular features, polarity, conformational flexibility, intermolecular interactions, and molecular size, as influential factors in vaporization energetics within the studied chemical space. Complementary symbolic regression and qualitative molecular dynamics simulations provide mechanistic insight into the role of molecular flexibility. This work establishes a reproducible and interpretable computational strategy for data-informed screening and rational design of LC materials, with potential extension to other thermophysical properties and energy-relevant liquid systems. Enthalpy of vaporization ; Interpretability ; Molecular descriptor ; Decision tree ; Enthalpy ; Gradient boosting ; Vaporization ; Experimental data ; Random forest ; Cheminformatics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure