jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junmei Du | Yifan Yan | Xiu‐Mei Li | Jiao Chen | Chunsheng Guo | Yuanzheng Chen | Hongyan Wang
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A mechanism-guided descriptor for the hydrogen evolution reaction in 2D ordered double transition-metal carbide MXenes Selecting effective catalysts for the hydrogen evolution reaction (HER) among MXenes remains a complex challenge. While machine learning (ML) paired with density functional theory (DFT) can streamline this search, issues with training data quality, model accuracy, and descriptor selection limit its effectiveness. These hurdles often arise from an incomplete understanding of the catalytic mechanisms. Here, we introduce a mechanism-guided descriptor (<i>δ</i>) for the HER, designed to enhance catalyst screening among ordered transition metal carbide MXenes. This descriptor integrates structural and energetic characteristics, derived from an in-depth analysis of orbital interactions and the relationship between Gibbs free energy of hydrogen adsorption (Δ<i>G</i> <sub>H</sub>) and structural features. The proposed model (Δ<i>G</i> <sub>H</sub> = -0.49<i>δ</i> - 2.18) not only clarifies structure-activity links but also supports efficient, resource-effective identification of promising catalysts. Our approach offers a new framework for developing descriptors and advancing catalyst screening. MXenes ; Mechanism (biology) ; Carbide ; Transition metal ; Hydrogen ; Materials science ; Chemical physics ; Chemistry ; Nanotechnology ; Physics ; Metallurgy ; Organic chemistry ; Catalysis MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Siyi Gao | Jiacheng Qi | Kai Jia | Liuting Zhang | Fuying Wu | Xuezhang Xiao
Source: Composites Part B Engineering (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

A review of high-entropy alloys for next-generation solid-state advanced hydrogen storage materials Hydrogen storage ; Hydrogen ; High entropy alloys ; Scalability ; Density functional theory ; Hydrogen economy ; Material Design Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Zhong-Hao Ye | Jia-Hua Liu | Chuanguo Chai | Yushi Wen | Shouxin Cui | Guiqing Zhang | Kejiang Li | Feng Guo | Xiaochun Wang
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

ReaxFF-nn: a reactive machine-learning potential in GULP/LAMMPS and its applications in the thermal conductivity calculations of carbon nanostructures The term "ReaxFF-nn" refers to the reactive force field (ReaxFF) with neural networks. In the current work, we have incorporated it into the general utility lattice program (GULP) and the large-scale atomic/molecular massively parallel simulator (LAMMPS), which are programmed with modern FORTRAN and C++, respectively. The parameters of ReaxFF-nn can be trained using our I-ReaxFF package. By combining GULP, LAMMPS and ReaxFF-nn, various tasks, such as determination of thermal properties and crystal properties, and energy minimization, can be performed with precision at the quasi-density functional theory (DFT) level. Compared to other machine-learning potentials (MLPs), our approach does not involve the development of an entirely new machine-learning potential; instead, a small neural network was implemented to compute the bond order and bond energy. To validate the model in GULP and LAMMPS, the forces of graphene and carbon nanotube (CNT) structures were compared among the auto-differentiation, GULP, and LAMMPS packages with our codes. The differences between these calculations are within 10<sup>-5</sup>. After the potential was trained against DFT calculations with losses of forces up to 10<sup>-2</sup> eV Å<sup>-1</sup> per atom, an example study of the thermal conductivity (<i>κ</i>) of graphene and carbon nanotubes (CNTs) using the Boltzmann transport equation (BTE) and non-equilibrium molecular dynamics (NEMD) methods was conducted. The value of <i>κ</i> for graphene obtained using ReaxFF-nn closely matches the results obtained from DFT calculations. The size dependence and the relations with the CNT diameter are discussed through NEMD calculations. Thermal conductivity ; ReaxFF ; Nanostructure ; Materials science ; Nanotechnology ; Thermodynamics ; Chemistry ; Molecular dynamics ; Physics ; Computational chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Yuxiao Jia | Hui Fang | Lixin Chen | Bo Han | Lin Tang | Jianchuan Wang | Yongpeng Xia | Yongjin Zou | Lixian Sun | Haiwen Li | Marek Polański | Xiulin Fan | Yong Du | Xuezhang Xiao
Source: Journal of Magnesium and Alloys (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Interpretable machine learning enables high performance of magnesium borohydride hydrogen storage system • A multi-head attention neural network model is first developed for hydrogen storage. • The model can accurately predict various dehydrogenation performance of Mg(BH 4 ) 2 . • Interpretability analysis provides guidance for experimental parameter design. • The model proves high scalability adapted to system co-doping hydrides and catalysts. • The model discovers high-performance catalysts and optimal Mg(BH 4 ) 2 systems. Aiming to provide optimal solutions to the sluggish kinetics of Mg(BH 4 ) 2 , this study proposes, for the first time, a novel machine learning model to predict dehydrogenation behaviors of modified Mg(BH 4 ) 2 . Notably, numerous data points are collected from temperature-programmed, isothermal, and cyclic dehydrogenation behaviors, a neural network model is proposed by using multi-head attention mechanisms, which exhibits the highest predictive performance compared to traditional machine learning models. The study also ranks different variables influencing dehydrogenation processes, employing interpretable analysis to identify critical variable thresholds, offering guidance for the experimental parameter design. The model can also be adapted to scenarios involving co-doping of hydrides and catalysts in Mg(BH 4 ) 2 system and proved high accuracy and scalability in predicting dehydrogenation curves under diverse conditions. Employing the model, performance predictions for a series of undeveloped Mg(BH 4 ) 2 co-doping systems can be made, and superior dehydrogenation catalytic effects of fluorinated graphite (FGi) are uncovered. Real-world experimental validation of the optimal Mg(BH 4 ) 2 -LiBH 4 -FGi system confirms consistency with model predictions, and performance enhancement attributes to experimental parameter optimization. Further characterizations provide mechanistic insights into the synergistic interactions of FGi and LiBH 4 . This work paves the way for advancing utilization of machine learning in the high-capacity hydrogen storage field. Hydrogen storage ; Borohydride ; Magnesium ; Materials science ; Hydrogen ; Process engineering ; Chemical engineering ; Metallurgy ; Engineering ; Catalysis ; Chemistry ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Magnesium Alloys: Properties and Applications
Authors: Sen Lu | Zhiguo Wang | Zhikai Gao | Tiren Peng | Pei Song | Zepeng Jia | Yuhang Zhou | Hong Cui | Weizhi Tian | Rong Feng | Lingxia Jin | Hongkuan Yuan
Source: ACS Applied Materials & Interfaces (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Modulation of Hydrogen Evolution Reaction Performance of MXenes by Doped Transition Metals: Comprehensive Exploration of High-Throughput Computing and Machine Learning Due to the unique properties of MXenes, the doping of transition metals can modulate their catalytic properties and make them potential materials for hydrogen evolution reaction (HER). Nevertheless, the extensive combinatorial space poses a challenge for rapid screening of catalysts. To address this issue, we conducted high-throughput calculations on a series of transition metal atom-doped Ti<sub>3</sub>CNO<sub>2</sub> and Zr<sub>2</sub>HfCNO<sub>2</sub>. Furthermore, the local structure and the corresponding electronic structure changes are analyzed, focusing on their influence on the HER properties. Furthermore, site identification features were introduced to train a multisite prediction model with a final model accuracy of <i>R</i><sup>2</sup> = 0.97 and predicted the trend of hydrogen adsorption Gibbs free energy (ΔG<sub>H*</sub>) across a range of MXenes structures, which were doped with TM atoms. The results show that Nb, Sc, Rh, W, Ti, and V doping resulted in |ΔG<sub>H*</sub>| < 0.2 eV for more than 38 M'<sub>2</sub>M″CNO<sub>2</sub>, respectively, and they are effective dopant atoms for enhancing the catalytic ability of M'<sub>2</sub>M″CNO<sub>2</sub>. This study not only demonstrates the potential of doped TM atoms in enhancing the performance of MXenes HER but also highlights the importance of multisite prediction models in the rapid identification and development of efficient HER catalysts. MXenes ; Throughput ; Materials science ; Doping ; Modulation (music) ; Nanotechnology ; Hydrogen ; Transition metal ; Computer science ; Catalysis ; Optoelectronics ; Telecommunications ; Wireless ; Biology ; Physics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Advanced Memory and Neural Computing
Authors: Zijun Pan | Xiaolei Shi | Zhiyuan Zhu | Xuan Tao | Xiang Zhao | Lujiang Xu | Shuai Gao | Liang Wang | Chen Wei | Zhen Fang
Source: Renewable and Sustainable Energy Reviews (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Joule heating for carbon material Synthesis: Mechanisms, material evolution, and sustainable prospects Carbon fibers ; Joule heating ; Graphene ; Heteroatom ; Sustainability ; Carbon nanotube ; Life-cycle assessment Graphene research and applications ; Carbon Nanotubes in Composites ; Catalytic Processes in Materials Science
Authors: Jihan K. Zaki | Jakub Tomasik | Jade A. McCune | Sabine Bahn | Píetro Lió | Oren A. Scherman
Source: ACS Sensors (article, 2025-09-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Explainable Deep Learning Framework for SERS Bioquantification Surface-enhanced Raman spectroscopy (SERS) is rapidly gaining attention as a fast and inexpensive method of biomarker quantification, which can be combined with deep learning to elucidate complex biomarker-disease relationships. Current standard practices in SERS analysis are behind the state-of-the-art machine learning approaches; however, the present challenges of SERS analysis could be effectively addressed with a robust computational framework. Furthermore, there is a need for improved model explainability for SERS analysis, which at present is insufficient in assessing the contexts in which confounding factors affect prediction outcomes. This study presents a framework for SERS bioquantification rooted in a three-step process, including spectral processing, quantification, and explainability. A serotonin quantification task in urine was assessed as a model task, with 682 SERS spectra measured in a micromolar range using cucurbit[8]uril chemical spacers. A denoising autoencoder was utilized for spectral enhancement, while convolutional neural networks (CNNs) and vision transformers were utilized for biomarker quantification. In addition, a context representative interpretable model explanation (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability. Serotonin quantification was most efficient in denoised spectra analyzed using a CNN with a three-parameter logistic output layer (mean absolute error = 0.15 μM, mean percentage error = 4.67%). Subsequently, the CRIME method revealed the CNN model to present six unique prediction contexts, of which three were associated with serotonin. The proposed framework could unlock a novel, untargeted hypothesis-generating method of biomarker discovery, considering the rapid and inexpensive nature of SERS measurements and the potential to identify biomarkers from CRIME contexts. Context (archaeology) ; Artificial intelligence ; Autoencoder ; Computer science ; Deep learning ; Convolutional neural network ; Biomarker ; Pattern recognition (psychology) ; Machine learning ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Idris Temitope Bello | Surendra B. Karki | Shuanglin Zheng | Jiufeng Ruan | Anshu Kumari | Yuqi Geng | Sooraj Patel | Zheng Li | Pejman Kazempoor | Wenjuan Bian | Yihan Ling | Iman Ghamarian | Yingtao Liu | Bin Wang | Zongping Shao | Meng Ni | Hanping Ding
Source: Energy Reviews (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Multiscale engineering of BaZr1-xYxO3-δ -based protonic ceramics: A critical review of defect chemistry, interface design, and computational insights Protonic ceramic energy devices represent a promising frontier for sustainable energy conversion and storage, operating efficiently at intermediate temperatures (350–650 ​°C) and facilitating integration with renewable energy sources. Among protonic ceramic materials, yttrium-doped barium zirconate (BaZr1-xYxO3-δ, BZY) stands out for its competitive proton conductivity, chemical resilience, and compatibility with diverse fuels and environments. This review critically examines the fundamentals and multiscale design strategies for BZY-based ceramic cells. We discuss atomic-level composition-structure relationships, innovative synthesis routes, and advanced processing methods to overcome manufacturing and scalability challenges. We then highlight microstructure engineering and interface design approaches that minimize resistance and elevate device performance, supported by state-of-the-art characterization and predictive modeling techniques, including density functional theory and machine learning. Recent advances, such as hybrid architectures and AI-driven defect optimization, demonstrate significant improvements in conductivity, stability, and Faradaic efficiency, confirming BZY's pivotal role in green hydrogen production and power-to-chemicals applications. By integrating insights across materials chemistry, electrochemistry, and engineering, this review provides a comprehensive roadmap for researchers aiming to translate laboratory breakthroughs into robust, scalable protonic ceramic technologies for decarbonized energy systems. Ceramic ; Scalability ; Compatibility (geochemistry) ; Interface (matter) ; Renewable energy ; Multiscale modeling ; Energy storage Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Chuan-Nan Li | Han-Pu Liang | Siyuan Xu | Haochen Wang | B. Zhao | Jingxiu Yang | Xie Zhang | Zijing Lin | Su‐Huai Wei
Source: Journal of the American Chemical Society (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

LoreX: A Low-Energy Region Explorer Boosts Efficient Crystal Structure Prediction Machine learning has boosted the remarkable development of crystal structure prediction (CSP), greatly accelerating modern materials design. However, slow location of the low-energy regions on the potential energy surface (PES) is still a key bottleneck for the overall search efficiency. Here, we develop a low-energy region explorer (LoreX) to rapidly locate low-energy regions on the PES. This achievement stems from graph-deep-learning-based PES slicing, which classifies structures into different prototypes to divide and conquer the PES. The accuracy and efficiency of LoreX are validated on 27 typical compounds, showing that it correctly locates low-energy regions with only 100 selected samples. The powerful capability of LoreX is demonstrated in solving two challenging problems: discovering new boron allotropes and identifying the puzzling crystal structures of the ordered vacancy compound CuIn<sub>5</sub>Se<sub>8</sub>. This study establishes a new method for rapid PES exploration and offers a highly efficient and generally applicable approach to accelerating CSP. Chemistry ; Crystal structure prediction ; Crystal structure ; Energy (signal processing) ; Crystal (programming language) ; Crystallography ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Mouyang Cheng | Chenyan Wang | Chunling Qin | Yuxiang Zhang | Qingyuan Zhang | Li Han | Ji Chen
Source: Chinese Physics Letters (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Predicting Macroscopic Properties of Amorphous Monolayer Carbon via Pair Correlation Function Abstract Establishing the structure-property relationship in amorphous materials has been a long-term grand challenge due to the lack of a unified description of the degree of disorder. In this work, we develop SPRamNet, a neural network based machine-learning pipeline that effectively predicts structure-property relationship of amorphous material via global descriptors. Applying SPRamNet on the recently discovered amorphous monolayer carbon, we successfully predict the thermal and electronic properties. More importantly, we reveal that a short range of pair correlation function can readily encode sufficiently rich information of the structure of amorphous material. Utilizing powerful machine learning architectures, the encoded information can be decoded to reconstruct macroscopic properties involving many-body and long-range interactions. Establishing this hidden relationship offers a unified description of the degree of disorder and eliminates the heavy burden of measuring atomic structure, opening a new avenue in studying amorphous materials. Amorphous solid ; Materials science ; Amorphous carbon ; Computer science ; Range (aeronautics) ; Function (biology) ; Monolayer ; Pipeline (software) ; Property (philosophy) ; Statistical physics ; Biological system ; Nanotechnology ; Physics ; Chemistry ; Crystallography Machine Learning in Materials Science ; Theoretical and Computational Physics ; Diamond and Carbon-based Materials Research
Authors: Adrian Krzyzanowski | Stephen D. Pickett | Péter Pogány
Source: Journal of Chemical Information and Modeling (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pretraining data, and adversarial training on a BERT-based model for yield prediction of Buchwald-Hartwig and Suzuki-Miyaura coupling reactions using publicly available HTE data sets. We demonstrate that molecular representation choice (SMILES, DeepSMILES, SELFIES, Morgan fingerprint-based notation, IUPAC names) has minimal impact on model performance, while typically BPE and SentencePiece tokenization outperform other methods. WordPiece is strongly discouraged for SELFIES and fingerprint-based notation. Furthermore, pretraining with relatively small data sets (<100 K reactions) achieves comparable performance to larger data sets containing millions of examples. The use of artificially generated domain-specific pretraining data is proposed. The artificially generated sets prove to be a good surrogate for the reaction schemes extracted from reaction data sets such as Pistachio or Reaxys. The best performance was observed for hybrid pretraining sets combining the real and the domain-specific, artificial data. Finally, we show that a novel adversarial training approach, perturbing input embeddings dynamically, improves model robustness and generalizability for yield and reaction success prediction. These findings provide valuable insights for developing robust and practical machine learning models for yield prediction in synthetic chemistry. GSK's BERT training code base is made available to the community with this work. Lexical analysis ; Yield (engineering) ; Computer science ; Representation (politics) ; Artificial intelligence ; Natural language processing ; Machine learning ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Brooklyne Ariana Thompson | Shengguang Wang | Konstantinos A. Goulas | M. Ross Kunz | Rebecca Fushimi
Source: Journal of Catalysis (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Lowering the barrier to access information-rich transient kinetic data for machine learning methods Chemistry ; Transient (computer programming) ; Kinetic energy ; Catalysis ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Arslan Mazitov | Sofiia Chorna | Guillaume Fraux | Marnik Bercx | Giovanni Pizzi | Sandip De | Michele Ceriotti
Source: Scientific Data (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning The development of machine-learning models for atomic-scale simulations has greatly benefited from the large databases of materials and molecular properties, computed using electronic-structure calculations. Recently, these databases enabled the training of "universal" models that aim to make accurate predictions for arbitrary atomic geometries and compositions. However, many of these databases were originally designed for materials discovery, focusing primarily on equilibrium structures. Here, we introduce a dataset designed to train machine-learning models to make reasonable predictions for arbitrary structures. Starting with relatively small sets of stable structures, we built the dataset aiming to achieve "massive atomic diversity" (MAD) by aggressively modifying these structures and utilizing highly consistent electronic-structure settings for property calculations. Despite containing fewer than 100,000 entries, the MAD dataset has already enabled the training of universal interatomic potentials that rival those trained on datasets containing two to three orders of magnitude more data. We detail the design philosophy of the dataset and introduce low-dimensional structural latent space descriptors that can be used as a general-purpose materials cartography tool. Property (philosophy) ; Space (punctuation) ; Training set ; Parameter space ; Interatomic potential Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Inorganic Chemistry and Materials