jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junmeng Li | X. LU | Yue Chen | Yu‐Wu Zhong | Jiannian Yao | Jingnan Wang | Xi Wang
Source: Chemical Engineering Science (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Machine learning-guided high-throughput screening of dual-atom catalysts for electrochemical C-C coupling Throughput ; Dual (grammatical number) ; Catalysis ; Electrochemistry ; Coupling (piping) ; Atom (system on chip) ; High-throughput screening ; Combinatorial chemistry ; Chemistry ; Computer science ; Materials science ; Parallel computing ; Physical chemistry ; Organic chemistry ; Metallurgy ; Telecommunications ; Electrode ; Biochemistry Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Ammonia Synthesis and Nitrogen Reduction
Authors: Lei Jiang | Wentao Zhoutai | Xinbiao Zhang | Zheng Shi | Zhilin Han | Yujie Cui | Jianxin Xie
Source: Journal of Magnesium and Alloys (article, 2025-11-08)
Field: Materials Science — Biomaterials

Interpretable machine learning design for concurrent and significant enhancement of the mechanical properties and corrosion resistance of low-density Mg-Li alloys • An interpretable machine learning strategy was developed to guide the design of low-density Mg-Li alloys. • Clarify the key intrinsic factors affecting the ultimate tensile strength, elongation, and corrosion rate of Mg-Li alloys. • The newly designed alloys achieve 34%∼44% higher strength and 35%∼79% lower corrosion rates than LAZ103, while maintaining high ductility and low density. Designing low-density, high-strength Mg-Li alloys is a major challenge in achieving extreme lightweighting of high-end equipment. This study proposes an interpretable machine learning strategy to simultaneously enhance the mechanical properties and corrosion resistance of Mg-Li alloy. Key alloy factors (KAFs) influencing ultimate tensile strength (UTS), elongation (EL), and corrosion rate (CR) were identified through alloy factor construction and screening. Using KAFs and processing parameters as inputs, gradient boosting regression models for UTS, EL, and CR were established, achieving the coefficients of determination of test-set above 0.85. Then, SHapley Additive exPlanations (SHAP) analysis quantified the impact of KAFs, and an element evaluation method was established to identify Al, Si, Ca, and Zn as candidates for alloy design. Finally, three new alloys were designed via multi-objective optimization. In the hot-extruded state, they exhibited UTS of 253∼273 MPa, EL of 18.4%∼27.9%, CR of 0.55∼1.61 mg/(cm 2 ·day), and ρ of 1.49∼1.54 g/cm 3 . Compared to LAZ103, the new alloys show 34%∼44% higher UTS, 35%∼79% lower CR, and comparable ρ. Microstructural analysis revealed increased α-Mg, decreased β-Li, reduced coarse secondary phases, and fine Ca-/Si-rich precipitates which are conducive to grain refinement and dislocation density increasing, synergistically enhancing comprehensive property. Ultimate tensile strength ; Corrosion ; Ductility (Earth science) ; Alloy ; Elongation ; Gradient boosting ; Strain rate Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Shaowen Li | Xinyu Mao | Xiao-Qin Cao | Yujun Feng | Yongqing Zhang | Hongyao Yin
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-02-11)
Field: Chemistry — Spectroscopy

Visualizing molecular structure—Adsorption efficiency relationship through an interpretable machine learning strategy Adsorption ; Artificial intelligence ; Computer science ; Machine learning ; Chemistry ; Materials science ; Nanotechnology ; Physical chemistry Analytical Chemistry and Chromatography ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Berna Alemdag | Görkem Saygılı | Matthias Franzreb | Gözde Kabay
Source: Advanced Electronic Materials (article, 2025-02-22)
Field: Materials Science — Materials Chemistry

Toward High‐Performance Electrochemical Energy Storage Systems: A Case Study on Predicting Electrochemical Properties and Inverse Material Design of MXene‐Based Electrode Materials with Automated Machine Learning (AutoML) Abstract This study highlights the potential of Automated Machine Learning (AutoML) to improve and accelerate the optimization and synthesis processes and facilitate the discovery of materials. Using a Density Functional Theory (DFT)‐simulated dataset of monolayer MXene‐based electrodes, AutoML assesses 20 regression models to predict key electrochemical and structural properties, including intercalation voltage, theoretical capacity, and lattice parameters. The CatBoost regressor achieves R 2 values of 0.81 for intercalation voltage, 0.995 for theoretical capacity as well as 0.807 and 0.997 for intercalated and non‐intercalated in‐plane lattice constants, respectively. Feature importance analyses reveal essential structure‐property relationships, improving model interpretability. AutoML's classification module also bolsters inverse material design, effectively identifying promising compositions, such as Mg 2+ ‐intercalated and oxygen‐terminated ScC 2 MXenes, for high‐capacity and high‐voltage energy storage applications. This approach diminishes reliance on computational expertise by automating model selection, hyperparameter tuning, and performance evaluation. While MXene‐based electrodes serve as a demonstrative system, the methodology and workflow can extend to other material systems, including perovskites and conductive polymers. Future efforts should prioritize integrating AutoML with real‐time experimental feedback and hybrid simulation frameworks to create adaptive systems. These systems can iteratively refine predictions and optimize trade‐offs among critical metrics like capacity, stability, and charge/discharge rates, driving advancements in energy storage and other material applications. Electrochemical energy storage ; Materials science ; Electrochemistry ; Energy storage ; Electrode ; Inverse ; Nanotechnology ; Electrochemical energy conversion ; Energy (signal processing) ; Engineering physics ; Mechanical engineering ; Supercapacitor ; Engineering ; Thermodynamics ; Physics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques
Authors: Chi-Yen Huang | Bo-Lin Huang | Su-Yu Liao | Yu-Yun Huang | Yuling Ma | Pei-Te Lin | Jiann-Heng Chen | Che-Ju Hsu | Chun‐Ying Huang
Source: Optik (article, 2025-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-precision liquid crystal cell gap estimation via machine learning Computer science ; Liquid crystal ; Estimation ; Optics ; Crystal (programming language) ; Artificial intelligence ; Materials science ; Optoelectronics ; Physics Liquid Crystal Research Advancements ; Visual perception and processing mechanisms ; Surface Roughness and Optical Measurements
Authors: Yorick L. A. Schmerwitz | L. Thirion | Gianluca Levi | Elvar Ö. Jónsson | Pavlo Bilous | Hannes Jónsson | P. Hansmann
Source: Journal of Chemical Theory and Computation (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure By combining Hartree-Fock with a neural-network-supported quantum-cluster solver proposed recently in the context of solid-state lattice models, we formulate a scheme for selective neural-network configuration interaction (NNCI) calculations and implement it with various options for the type of basis set and boundary conditions. The method's performance is evaluated in studies of several small molecules as a step toward calculations of larger systems. In particular, the correlation energy in the N<sub>2</sub> molecule is compared with published full CI calculations that included nearly 10<sup>10</sup> Slater determinants, and the results are reproduced with only 4 × 10<sup>5</sup> determinants using NNCI. A clear advantage is seen from increasing the set of orbitals included rather than approaching full CI for a smaller set. The method's high efficiency and implementation in a condensed matter simulation software expands the applicability of CI calculations to a wider range of problems, even extended systems through an embedding approach. Computer science ; Artificial neural network ; Human–computer interaction ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Cihat Güleryüz | Sajjad Hussain Sumrra | Abrar U. Hassan | Ayesha Mohyuddin | Ashraf Y. Elnaggar | Sadaf Noreen
Source: Materials Chemistry and Physics (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials Selenium ; Stability (learning theory) ; Surface (topology) ; Materials science ; Nanotechnology ; Biological system ; Chemistry ; Machine learning ; Computer science ; Mathematics ; Metallurgy ; Geometry Graphene research and applications ; Advanced Battery Materials and Technologies ; Conducting polymers and applications
Authors: Hussein Ali Kadhim Kyhoiesh | Karrar Hazim Salem | Azal S. Waheeb | Riyam A. Hasan | Hasan Salman | Ahmed A. Al-Kubaisi | Ashraf Y. Elnaggar | Islam H. El Azab | M.H.H. Mahmoud
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Exploring the degree of long-range order/disorder in indaceno-based photovoltaic small molecules using data-driven machine learning analysis Long-range order and disorder in small molecules significantly impact their physical and chemical properties, affecting their performance in photovoltaic devices. For the current study, a data-driven machine learning (ML) approach has been applied to explore the relationship between molecular structure and crystallinity in 480 indaceno-based small molecules. Three ML models, including support vector machines and random forest models, were trained to predict crystal propensity. A heatmap analysis revealed that 72.71% of the small molecules exhibit crystalline behavior, while the remaining 27.29% are non-crystalline. ML models achieved near-perfect accuracy (AUC : SVM-RBF = 0.999, RF = 0.998; MSE : RF = 0.00, SVM-RBF = 0.01). The predicted crystal propensity values showed high accuracy, with a mean squared error ranging from 0.0-0.64. Feature importance analysis using SHAP values identified Chi0v, kappa1, Chi1n, and NumRotatableBonds as the most contributing factors to crystal propensity. The synthetic accessibility score of the small molecules ranged from 0.02 to 0.12, providing insights for designing and optimizing indaceno-based small molecules with tailored crystallinity and photovoltaic properties. This study demonstrates the potential of ML approaches in guiding the development of high-performance small molecules for solar energy applications. Photovoltaic system ; Range (aeronautics) ; Molecule ; Order (exchange) ; Degree (music) ; Materials science ; Computer science ; Chemical physics ; Nanotechnology ; Physics ; Engineering ; Electrical engineering ; Composite material ; Quantum mechanics ; Business Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Jiajing Li | Jolene P. Reid
Source: Chemical Science (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Connecting the complexity of stereoselective synthesis to the evolution of predictive tools Synthetic methods have seemingly progressed to an extent where there is an apparent and increasing need for predictive models to navigate the vast chemical space. Methods for anticipating and optimizing reaction outcomes have evolved from simple qualitative pictures generated from chemical intuition to complex models constructed from quantitative methods like quantum chemistry and machine learning. These toolsets are rooted in physical organic chemistry where fundamental principles of chemical reactivity and molecular interactions guide their development and application. Here, we detail how the evolution of these methods is a successful outcome and a powerful response to the diverse synthetic challenges confronted and the innovative selectivity concepts introduced. In this review, we perform a periodization of organic chemistry focusing on strategies that have been applied to guide the synthesis of chiral organic molecules. Stereoselectivity ; Computer science ; Combinatorial chemistry ; Computational biology ; Chemistry ; Biology ; Organic chemistry Asymmetric Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Yanyan Lu | Yu Qiao | Haoming Bao | Kang Chen | Yi Wei | Qian Zhao | Géraldine León | Hongwen Zhang | Xing Yi Ling | Weiping Cai
Source: Analytical Chemistry (article, 2025-04-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Driven Surface Plasmon-Enhanced Dual Spectroscopies Improve Recognition and Real-Time Monitoring of Hazardous Chemicals To address the challenges of precise identification and real-time monitoring of hazardous chemicals, this work proposes and develops surface-plasmon-enhanced dual spectroscopies (SPEDS). This technique combines highly recognizable surface-enhanced Raman spectroscopy (SERS) with real-time plasmon-mediated differential ultraviolet-visible spectroscopy (P-DUS). The feasibility of this technique is demonstrated by successfully acquiring SPEDS signals of thiourea with a plasmonic gold colloidal system. By combining SPEDS with machine learning algorithms, we achieve accurate identification and precise quantification of chemicals, with accuracies of 98.2 and 98.6%, respectively, significantly outperforming single P-DUS (63.2 and 95.1%) and SERS (80.3 and 86.5%). Additionally, we demonstrate the universality and expandability of SPEDS through other plasmonic nanostructures of various shapes and surface modifications. Using a CuS-coated Au nanoarray, we demonstrate multiple 8-h monitoring sessions of Hg<sup>2+</sup> with good anti-interference and robust quantification, thereby highlighting the practical potential of SPEDS in real-world applications. These results position SPEDS as a cutting-edge and multifunctional chemical sensing platform, unlocking transformative possibilities for advancing environmental monitoring, industrial safety, and public health. Chemistry ; Hazardous waste ; Dual (grammatical number) ; Surface plasmon ; Nanotechnology ; Environmental chemistry ; Plasmon ; Optoelectronics ; Waste management Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Huiran Zhang | Yudian Lin | Shengzhou Li | Mengmeng Dai | Liang Yu | Lei Huang | Jiangcan Pang | Pin Wu | Junjie Peng | Zheng Tang | Peng Ding | Wei Xiao | Na Song | Dongbo Dai
Source: Macromolecules (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Substructure-Enhanced MPNN for Polymer Discovery and Knowledge: A Study in Predicting Glass Transition Temperature Understanding the relationship between polymer structures and the glass transition temperature (Tg) is crucial for the design of high-performance polymers. Machine learning models have shown great potential in accelerating the discovery and development of such materials by uncovering structure–property relationships. However, traditional machine learning models often overlook key structural features, such as functional groups, limiting their ability to effectively capture and represent complex structures of polymers. To address this, we developed the Substructure-Enhanced Message Passing Neural Network (SE-MPNN), which incorporates substructure information across multiple scales, from single atoms to functional groups, to evaluate their contributions to Tg. Results demonstrate that, by decomposing polymer structures into substructures, SE-MPNN provides more intuitive interpretations from the substructure level. These insights are consistent with established chemical understanding and offer valuable guidance for the rational design and optimization of polymer materials. Glass transition ; Substructure ; Polymer ; Materials science ; Chemistry ; Chemical physics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Computational Drug Discovery Methods
Authors: Paolo De Angelis | Giulio Barletta | Giovanni Trezza | Pietro Asinari | Eliodoro Chiavazzo
Source: Energy and AI (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Energy-GNoME: A living database of selected materials for energy applications Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 38,500 materials with potential as energy materials forming the core of the Energy-GNoME database. Our unique combination of Machine Learning (ML) and Deep Learning (DL) tools mitigates cross-domain data bias using feature spaces, thus identifying potential candidates for thermoelectric materials, novel battery cathodes, and novel perovskites. First, classifiers with both structural and compositional features detect domains of applicability, where we expect enhanced reliability of regressors. Here, regressors are trained to predict key materials properties, like thermoelectric figure of merit (zT), band gap (Eg), and cathode voltage (ΔVc). This method significantly narrows the pool of potential candidates, serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation, energy storage and conversion. Energy (signal processing) ; Field (mathematics) ; Energy consumption ; Work (physics) ; Efficient energy use Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advancements in Solid Oxide Fuel Cells
Authors: Lijie Ding | Chi-Huan Tung | Zhiqiang Cao | Zekun Ye | Xiaodan Gu | Yan Xia | Wei‐Ren Chen | Changwoo Do
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted profiling of a kinked ladder polymer structure using scattering Ladder polymers consisting of fused rings in the backbone have very limited conformational freedom, which results in very different properties from traditional linear polymers. Profiling (computer programming) ; Polymer ; Materials science ; Scattering ; Polymer science ; Computer science ; Chemical engineering ; Composite material ; Engineering ; Physics ; Optics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Mass Spectrometry Techniques and Applications
Authors: Julius Seumer | Jan H. Jensen
Source: PeerJ Physical Chemistry (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

Beyond predefined ligand libraries: a genetic algorithm approach for <i>de novo</i> discovery of catalysts for the Suzuki coupling reactions This study introduces a novel approach for the de novo design of transition metal catalysts, leveraging the power of genetic algorithms and density functional theory calculations. By focusing on the Suzuki reaction, known for its significance in forming carbon-carbon bonds, we demonstrate the effectiveness of fragment-based and graph-based genetic algorithms in identifying novel ligands for palladium-based catalytic systems. Our research highlights the capability of these algorithms to generate ligands with desired thermodynamic properties, moving beyond the restriction of enumerated chemical libraries. Limitations in the applicability of machine learning models are overcome by calculating thermodynamic properties from first principle. The inclusion of synthetic accessibility scores further refines the search, steering it towards more practically feasible ligands. Through the examination of both palladium and alternative transition metal catalysts like copper and silver, our findings reveal the algorithms’ ability to uncover unique catalyst structures within the target energy range, offering insights into the electronic and steric effects necessary for effective catalysis. This work not only proves the potential of genetic algorithms in the cost-effective and scalable discovery of new catalysts but also sets the stage for future exploration beyond predefined chemical spaces, enhancing the toolkit available for catalyst design. Scalability ; Catalysis ; Steric effects ; Computer science ; Combinatorial chemistry ; Algorithm ; Chemistry ; Materials science ; Organic chemistry ; Database Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Electrocatalysts for Energy Conversion
Authors: Pingyang Zhang | Shaodong Zhang | Yihan Qin | Tingting Du | Lei Wei | Xiangyu Li
Source: npj Computational Materials (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites Graphene foam (GF), synthesized via Chemical Vapor Deposition (CVD), has been proven to be the ideal bulk porous material. The addition of poly(dimethylsiloxane) (PDMS) within the porous structure enables enhancement of mechanical strength and alteration of heat transfer behavior. This study focuses on the thermodynamic behavior of GF/PDMS composites during deformation, and employs stochastic modeling and neuroevolution potential (NEP) for complex material modeling with precise prediction of microscopic mechanisms governing thermal property variations. The results demonstrate that the composite with a 5% doping rate of PDMS achieves the optimal mechanical performance and shows a 7.13-fold modulation in thermal resistance during the deformation from 40% stretching to 50% compression. Findings indicate PDMS fortifies structural stability while enabling dynamic thermal conductivity modulation in GF. This research provides critical insights into the micro-mechanisms of GF/PDMS composites and offers a theoretical foundation for applications in dynamic thermal management and self-powered sensor networks. Composite material ; Graphene ; Decodes ; Materials science ; Molecular dynamics ; Thermal ; Syntactic foam ; Graphene foam ; Computer science ; Nanotechnology ; Chemistry ; Graphene nanoribbons ; Algorithm ; Physics ; Thermodynamics Thermal properties of materials ; Graphene research and applications ; Transition Metal Oxide Nanomaterials