jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruru Gao (8445714) | Hongcheng Shang (17844652) | Qinglin Zhou (21571874) | Bao-Feng Tan (21571877) | Xiu-Shen Wei (16698617) | Jinghui Zhang (12013) | Yingzhi Jiao (8445711) | Weijin Li (2997675)
Source: Figshare (article, 2025-06-19)
Field: Materials Science — Materials Chemistry

MachineLearning-Guided Conductivity Prediction in2D Organic Metal Chalcogenides for Accelerated Electromagnetic WaveAbsorber Design The rational design of electromagnetic wave (EMW) absorbers relies on precise conductivity control, yet conventional trial-and-error methods fail to efficiently explore the multidimensional synthesis parameter space of two-dimensional organic metal chalcogenides (2D OMCs). Here, we propose a machine learning framework that deciphers the nonlinear relationships between synthetic parameters and electrical conductivity in 2D OMCs, enabling quantitative predictions for targeted EMW absorber design. The trained model achieves 86% accuracy in three-level conductivity classification (I: <10<sup>–6</sup> S/m, II: 10<sup>–6</sup>–10<sup>–2</sup> S/m, III: >10<sup>–2</sup> S/m), significantly outperforming empirical approaches. Notably, it demonstrates robust extrapolation capability by correctly predicting 12 out of 15 novel OMCs beyond the training data set. Guided by conductivity predictions, the EMW absorption performance of specific 2D OMCs can be efficiently ranked, accelerating material design with experimentally validated accuracy. This machine learning-assisted strategy reveals the complex relationship between synthesis parameters and conductivity, expediting the design and synthesis of materials with optimized EMW absorption performance. Extrapolation ; Expediting ; Conductivity ; Absorption (acoustics) ; Nonlinear system ; Feature (linguistics) ; Parameter space ; Electrical resistivity and conductivity ; Performance prediction 2D Materials and Applications ; Synthesis and Properties of Aromatic Compounds ; Covalent Organic Framework Applications
Authors: Anna Katharina Picha | Marcus Wieder | Stefan Boresch
Source: ChemRxiv (preprint, 2025-06-13)
Field: Materials Science — Materials Chemistry

Condensed phase properties and transferable neural network potentials Transferable neural network potentials (NNP) are undergoing rapid development. Many practical applications of NNPs focus on single molecules; e.g., using NNPs as a fast replacement for quantum chemical methods for dihedral angle scans in force field development. Similarly, the reference data on which most transferable NNPs have been trained are single molecule properties. As NNPs are beginning to be used to simulate more complex systems, such as solute-solvent simulations, the question arises whether the current generation of transferable NNPs is accurate enough to reproduce condensed phase properties, which in most cases are outside the training domain of the models. Here we present a first analysis of how well two transferable NNPs (ANI-2x, MACE-OFF23(S/M)) perform in reproducing properties such as density, heat of vaporization, heat capacity, and isothermal compressibility of several pure liquids (water, methanol, acetone, benzene, n-hexane at room temperature, and N-methylacetamide at 100 C). In addition, we examine selected radial distribution functions and the self-diffusion constant. We find specific weaknesses for each of the models, and seemingly small flaws lead to poor performance when applied to condensed phase simulations. The varied outcomes observed with the machine learning potentials suggest that, currently, selecting an architecture or model for all-NNP simulations of real-world applications requires careful consideration and testing. Phase (matter) ; Artificial neural network ; Materials science ; Biological system ; Computer science ; Physics ; Artificial intelligence ; Biology ; Quantum mechanics Machine Learning in Materials Science
Authors: Hiroto Kuroyanagi | Tatsuro Yuge
Source: PubMed (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Deep learning of thermodynamic laws from microscopic dynamics. We numerically show that a deep neural network (DNN) can learn macroscopic thermodynamic laws purely from microscopic data. Using molecular dynamics simulations, we generate the data of snapshot images of gas particles undergoing adiabatic processes. We train a DNN to determine the temporal order of input image pairs. We observe that the trained network induces an order relation between states consistent with adiabatic accessibility, satisfying the axioms of thermodynamics. Furthermore, the internal representation learned by the DNN acts as an entropy. These results suggest that machine learning can discover emergent physical laws that are valid at scales far larger than those of the underlying constituents-opening a pathway to data-driven discovery of macroscopic physics. Adiabatic process ; Artificial neural network ; Axiom ; Deep learning ; Representation (politics) ; Physical law ; Relation (database) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Hossein Najafzadeh | Zahra Raissi | Shole Golmohammady | Parivash Safari Kaji | Mahdad Esmaeili
Source: Habilitation Regulations of the Faculty of Mechanical Engineering (University of Paderborn) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform This study aimed to develop and evaluate deep learning approaches for the classification of quantum emission signals from WS2 monolayer nanobubbles across multiple spectral bands, addressing challenges in quantum materials characterization and spectral distinguishability assessment. We utilized a dataset of quantum emission signals ranging from 604 to 629 nm, emitted from WS₂ monolayer nanobubbles on gold substrates, categorized into four spectral bands (604.06–608.24 nm, 611.07–616.34 nm, 617.42–623.35 nm, and 624.16–636.57 nm). Our methodology involved signal preprocessing through normalization and moving average smoothing, followed by transformation into 128 × 128 RGB images using Continuous Wavelet Transform (CWT) with Complex Morlet wavelet. Three convolutional neural network architectures (ResNet50, VGG16, and Xception) were implemented and evaluated using fivefold cross-validation across six possible band pair combinations. All models demonstrated exceptional classification performance, with VGG16 achieving the highest overall mean accuracy of 99.4%, followed by Xception (99.1%) and ResNet50 (98.2%). Perfect classification accuracy (100%) was consistently achieved for spectrally distant band pairs, particularly Band 1 versus Band 4 (20.5 nm separation), while the most challenging classification involved adjacent bands (Band 2 vs. Band 3, 6.27 nm separation) with VGG16 achieving 96.5% accuracy. Statistical analysis using Friedman tests confirmed significant performance differences among models (χ2 = 8.67, p = 0.013). Xception demonstrated remarkable computational efficiency, achieving optimal convergence in as few as 2 epochs for certain band combinations while maintaining ultralow training loss values (8.23 × 10⁻6). Deep learning models, particularly when combined with CWT preprocessing, provide a robust framework for quantum emission signal classification with significant implications for quantum photonics, quantum cryptography, and quantum sensing applications. Our approach bridges the gap between classical machine learning and quantum materials characterization, establishing quantifiable metrics for evaluating spectral distinguishability in quantum information systems. The demonstrated ability to achieve high classification accuracy with minimal training through transfer learning addresses data scarcity challenges inherent to quantum systems, offering a promising direction for future quantum technology development. Deep learning ; Pattern recognition (psychology) ; Quantum ; Convolutional neural network ; Normalization (sociology) ; Wavelet ; Preprocessor 2D Materials and Applications ; Mechanical and Optical Resonators ; Advanced Chemical Sensor Technologies
Authors: Masayuki Karasawa | Chee Siang Leow | Hideaki Yajima | S. Arai | Hiromitsu Nishizaki | Tohru Terada | Hajime Sato
Source: Journal of Chemical Information and Modeling (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

ColabReaction: Accelerating Transition State Searches with Machine Learning Potentials on Google Colaboratory We have developed a rapid and automated transition state (TS) search method for chemical reactions by combining the double-ended method, Direct MaxFlux (DMF), with machine learning (ML) potentials. Compared to conventional quantum mechanical (QM) scan-based approaches, this method achieves approximately 2 orders of magnitude speedup, typically locating TS structures within 10 min. To promote broad accessibility, this method is implemented on Google Colaboratory (Colab), leveraging its cloud-based GPU environment to eliminate the need for local computational resources. We named this implementation as ColabReaction. A modified panel-based graphical user interface is also provided, allowing users to perform TS searches through a web-based interface without writing code. This platform offers a cost-free, user-friendly solution for reaction pathway exploration and mechanistic analysis, particularly for experimental researchers and students without prior experience in computational chemistry. ColabReaction is open-source and freely available at https://ColabReaction.net and https://github.com/BILAB/ColabReaction. Interface (matter) ; Transition (genetics) ; State (computer science) ; Graphical user interface ; Quantum chemical ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Garam Bae | Jae Hee Cho | Do Hyung Lee | Wooseok Song | Ki‐Seok An
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Surface Defect–Mobility Correlation in Graphene Fets Unveiled by Raman Spectroscopy and Machine Learning Graphene ; Raman spectroscopy ; Materials science ; Surface (topology) ; Nanotechnology ; Spectroscopy ; Optoelectronics ; Condensed matter physics ; Physics ; Optics ; Mathematics ; Quantum mechanics Graphene research and applications ; Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis
Authors: Markus Strienz | Roman Kimmich | Alexander Conzelmann | Andreas Schnepf
Source: Molecules (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Cu14H12(PtBu3)6Cl2—The Expanse of Stryker’s Reagent Although a large number of copper hydride complexes and clusters have been reported, phosphine-stabilized copper hydrides remain comparatively rare. This is particularly noteworthy given the continuing interest in Stryker's reagent [HCu(PPh<sub>3</sub>)]<sub>6</sub> due to its use as a hydrogenation reagent. In this work, we report on the synthesis and full characterization of a novel copper hydride cluster, [Cu<sub>14</sub>H<sub>12</sub>(P<i><sup>t</sup></i>Bu<sub>3</sub>)<sub>6</sub>Cl<sub>2</sub>]. The structure of this copper hydride was determined via SC-XRD. In addition, the reactivity of the hydrides and their position were investigated via a convolutional neural network, quantum chemical calculations, and NMR, and they are compared to the well-known, smaller Stryker's reagent. Reagent ; Copper ; Hydride ; Reactivity (psychology) ; Quantum chemical ; ALUMINUM HYDRIDE Nanocluster Synthesis and Applications ; Organometallic Complex Synthesis and Catalysis ; Catalytic Cross-Coupling Reactions
Authors: Vivek Kumar | Abhishek Jangid | Manas Sharma | Manvi Verma | Jampala Pasyanthi | K. Shiv Kumar | Piyush Sharma | Emil Chiglintsev | Mikhail I. Panin | Sudeep N. Punnathanam | A. I. Chernov | Ananth Govind Rajan | Akshay Singh
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

Tailored Vapor Deposition Unlocks Large-Grain, Wafer-Scale Epitaxial Growth of 2D Magnetic CrCl3 Two-dimensional magnetic materials (2D-MM) are an exciting playground for fundamental research, and for spintronics and quantum sensing. However, their large-grain large-area synthesis using scalable vapour deposition methods is still an unsolved challenge. Here, we develop a tailored approach for centimetre-scale growth of semiconducting 2D-MM CrCl3 films on mica substrate, via physical vapour transport deposition. A controlled synthesis protocol, enabled via innovations concerning light management, very-high carrier-gas flow, precursor flux, and oxygen/moisture removal, is critical for wafer-scale growth. Optical, stoichiometric, structural, and magnetic characterization identify crystalline, phase-pure 2D-MM CrCl3. Substrate temperature tunes thickness of films from few-layers to tens of nanometres. Further, selective-area growth and large-area transfer are demonstrated. Substrate-dependent growth features are explained by density functional theory and state-of-the-art machine learning interatomic potential-based atomic-scale simulations. This scalable vapour deposition approach can be applied for growth of several 2D-MM, and low growth temperature (~500 C) will enable creation of hybrid heterostructures. Chemical vapor deposition ; Epitaxy ; Spintronics ; Deposition (geology) ; Substrate (aquarium) ; Physical vapor deposition ; Characterization (materials science) ; Mica 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: I. V. Grigoryan | Liubov A Antiufrieva | A S Grigoryan | Andrei A. Korigodskii | C. Junyang | Y. Shuxiong | Vladislava A. Pigareva | A.V. Tishchenko | Г.Б. Хомутов | Andrey V. Sybachin
Source: Colloid Journal (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Application of Machine Learning for Predicting Phase Behavior of Interpolyelectrolyte Complexes in Water–Salt Media Water–salt solutions of interpolyelectrolyte complexes (IPECs) represent a classical example of “smart” systems, the phase equilibrium in which is regulated by many factors associated with both the parameters of the polymer components and the physical and chemical properties of an environment. This paper presents a model created on the basis of machine learning for predicting the region of existence of water-soluble IPECs. An approach is proposed for independent account of the physicochemical properties of polyelectrolytes and the properties of the environment. The developed model is universal and can be used to predict the properties of multicomponent systems of various chemical natures. Salt (chemistry) ; Salt water ; Phase (matter) ; Salt lake ; Chemistry ; Chemical engineering ; Materials science ; Geology ; Engineering ; Geotechnical engineering ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrostatics and Colloid Interactions
Authors: Di Wu | Zhongli Ye | Hui Liang | Qian Gu | Guohua Cai | Yan Lin | Yi‐Ling Chen | Zechun Liu | Wei Xie | Di Wang | Qiaoling Li
Source: Research Square (preprint, 2025-05-08)
Field: Chemistry — Analytical Chemistry

From Leaf to Blend: CNN-Enhanced Multi-source Feature Fusion Enables Threshold-Driven Style Control in Digital Tobacco Formulation Feature (linguistics) ; Fusion ; Style (visual arts) ; Computer science ; Tobacco leaf ; Control (management) ; Artificial intelligence ; Pattern recognition (psychology) ; Engineering ; Art Spectroscopy and Chemometric Analyses
Authors: Masayuki Karasawa (21804167) | Chee Siang Leow (22513527) | Hideaki Yajima (22513530) | Shuta Arai (22513533) | Hiromitsu Nishizaki (22513536) | Tohru Terada (160117) | Hajime Sato (76981)
Source: Figshare (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

ColabReaction: Accelerating Transition State Searcheswith Machine Learning Potentials on Google Colaboratory We have developed a rapid and automated transition state (TS) search method for chemical reactions by combining the double-ended method, Direct MaxFlux (DMF), with machine learning (ML) potentials. Compared to conventional quantum mechanical (QM) scan-based approaches, this method achieves approximately 2 orders of magnitude speedup, typically locating TS structures within 10 min. To promote broad accessibility, this method is implemented on Google Colaboratory (Colab), leveraging its cloud-based GPU environment to eliminate the need for local computational resources. We named this implementation as ColabReaction. A modified panel-based graphical user interface is also provided, allowing users to perform TS searches through a web-based interface without writing code. This platform offers a cost-free, user-friendly solution for reaction pathway exploration and mechanistic analysis, particularly for experimental researchers and students without prior experience in computational chemistry. ColabReaction is open-source and freely available at https://ColabReaction.net and https://github.com/BILAB/ColabReaction. Interface (matter) ; State (computer science) ; Transition (genetics) ; Graphical user interface ; Training set ; User interface Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: G. R. Shadrina | V. I. Anisimova | I. S. Rodionov | A. A. Baldinov | N. V. Ulitin | YA.L. LYULINSKAYA | K. A. Tereshchenko | D. A. Shiyan
Source: Doklady Physical Chemistry (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

A Hybrid Approach to Predicting Glass Transition Temperatures of Organic Homopolymers: A Combination of the QSPR Model and the Increment Method In recent years, machine learning algorithms have become popular for predicting the physicochemical properties of polymers, including the glass transition temperature (Tg). Accurate Tg prediction is critical for developing polymers with desired properties. Traditional Tg prediction was based on semi-empirical methods, such as Askadskii’s method. The goal of this study was to develop a hybrid approach for predicting the Tg of organic homopolymers, combining Askadskii’s method and the QSPR model with machine learning (ML), which uses the advantages of theoretical analysis and the capabilities of ML to improve prediction accuracy. Random Forest, K-Nearest Neighbors, and a multilayer perceptron were used. The molecular structure of the polymers was represented by structural keys (MACCSKeys) and Morgan fingerprints. Optimization of the random forest algorithm hyperparameters enabled an R2 of up to 0.77 to be achieved on the test set. A comparative analysis showed that Morgan fingerprints that consider the spatial arrangement of fragments provide higher prediction accuracy, especially for isomeric homopolymers, where the spatial arrangement of substituents is important. The results demonstrate the potential of using ML for predicting polymer Tg based on glass transition theories and highlight the need for further research into hybrid models. Glass transition ; Quantitative structure–activity relationship ; Hyperparameter ; Polymer ; Random forest ; Multilayer perceptron Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions ; Epoxy Resin Curing Processes
Authors: Lauriane Jacot‐Descombes | Stefan P. Schmid | Kjell Jorner
Source: CHIMIA International Journal for Chemistry (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Educating Future Chemists in the Age of AI: A Digital Chemistry Course Artificial intelligence (AI) and machine learning (ML) are developing fast and are increasingly adopted in both chemical industry and academic research. With the projected role such tools will play in the future, for every chemist, these developments call for a fundamental and sound education for future generations of scientists in these areas. In this perspective, we describe the development of the course Digital Chemistry at ETH Zurich, which addresses these topics. In particular, we outline our approach to teaching ML and its applications in chemistry. We especially emphasize that the skills of understanding, applying and critically assessing ML models will be fundamental for future chemists. We hope that this article will serve as inspiration for educators in this field and help to enhance the teaching in this area of future significance. Course (navigation) ; Field (mathematics) ; Chemistry education ; Teaching method ; Curriculum Machine Learning in Materials Science ; Various Chemistry Research Topics ; Chemistry and Chemical Engineering
Authors: Kaichen Ouyang | Shiyun Zhang | Songling Liu | Jiachuan Tian | Yuanhao Li | Hua Tong | H. Y. Bai | Weihua Wang | Yuan‐Chao Hu
Source: AI for Science (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Graph learning metallic glass discovery from Wikipedia Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal combinations of multiple elements to resist crystallization. This constraint renders only several thousands of candidates explored in the vast material space since 1960. Recently, data-driven approaches armed by advanced machine learning techniques provided alternative routes for intelligent materials design. Due to data scarcity and immature material encoding, the conventional tabular data is usually mined by statistical learning algorithms, giving limited model predictability and generalizability. Here, we propose sophisticated data learning from material network representations. The node elements are encoded from the Wikipedia by a language model. Graph neural networks with versatile architectures are designed to serve as recommendation systems to explore hidden relationships among materials. By employing Wikipedia embeddings from different languages, we assess the capability of natural languages in materials design. Our study proposes a new paradigm to harvesting new amorphous materials and beyond with artificial intelligence. Graph ; Process (computing) ; Artificial neural network ; Natural language ; Deep learning ; Node (physics) Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Nanoporous metals and alloys