jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiwei Zhou | Yandong Wang | Maohua Li | Yue Qin | Rongjie Yang | Kang Xu | Yingying Guo | Linhong Li | Zhenbang Zhang | Jianxiang Zhang | Boda Zhu | Cheng‐Te Lin | Yixiang Xu | Kazuhito Nishimura | Nan Jiang | Jinhong Yu
Source: Advanced Composites and Hybrid Materials (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Ultra-high thermal conductivity multifunctional composites with uniaxially oriented boron nitride sheets for future wireless charging technology The rapid advancement of wireless charging systems (WCSs), fifth-generation (5G) technology, electric vehicles (EVs), and artificial intelligence (AI) systems result in a critical need for more efficient thermal management materials. h-BN, characterized by its ultra-high theoretical thermal conductivity and excellent electrically insulating properties, serves as a promising filler for blending with polymers to develop high-performance thermally conductive composites. However, it is still a challenge to attain a high through-plane thermal conductivity of over 40 Wm−1 K−1 at filler content at 80 vol%. This persistent limitation is mainly attributed to the fact that most current h-BN used in the manufacture of thermal conductive composites has relatively smaller lateral size (below 40 µm). Additionally, the effective orientation strategy (e.g., ice-template strategy) usually results in difficulties in mass production. Here, a two-step process involving blade coating and lamination is used to prepare BN/TPU composites with an through-plane thermal conductivity of 43 Wm−1 K−1 at h-BN content of 67 vol%. With upper-level electric insulating properties and wave-transparent characteristic, the multifunctional BN/TPU composite shows excellent thermal management ability in the high-power wireless charging area and also has the potential to be used in the 5G communication technology field. Boron nitride ; Materials science ; Composite material ; Thermal conductivity ; Thermal ; Boron ; Conductivity Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Ferroelectric and Piezoelectric Materials
Authors: Hayato Maeda | Stephen Wu | Rika Marui | Erina Yoshida | Kan Hatakeyama‐Sato | Yuta Nabae | Shiori Nakagawa | Meguya Ryu | Ryohei Ishige | Yoh Noguchi | Yoshihiro Hayashi | Masashi Ishii | Isao Kuwajima | Felix Jiang | Xuan Thang Vu | Sven Ingebrandt | Masatoshi Tokita | Junko Morikawa | Ryo Yoshida | Teruaki Hayakawa
Source: npj Computational Materials (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Discovery of liquid crystalline polymers with high thermal conductivity using machine learning Abstract Next-generation power electronics require efficient heat dissipation management, and molecular design guidelines are needed to develop polymers with high thermal conductivity. Polymer materials have considerably lower thermal conductivity than metals and ceramics due to phonon scattering in the amorphous region. The spontaneous orientation of the molecular chains of liquid crystalline polymers could potentially give rise to high thermal conductivity, but the molecular design of such polymers remains largely empirical. In this study, we developed a machine learning model that predicts with more than 96% accuracy whether liquid crystalline states will form based on the chemical structure of the polymer. By exploring the inverse mapping of this model, we identified a comprehensive set of chemical structures for liquid crystalline polyimides. The polymers were then experimentally synthesized, and the results confirmed that they form liquid crystalline phases, with all polymers exhibiting calculated thermal conductivities within the range of 0.722–1.26 W m −1 K −1 . Thermal conductivity ; Materials science ; Polymer ; Thermal ; Conductivity ; Composite material ; Chemistry ; Physics ; Meteorology Machine Learning in Materials Science
Authors: Prajwal Pisal | Ondřej Krejčí | Patrick Rinke
Source: npj Computational Materials (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion Abstract Transforming CO 2 into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts, using machine-learned force fields. We propose a new catalytic descriptor, termed adsorption energy distribution, that aggregates the binding energies for different catalyst facets, binding sites, and adsorbates. The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates. By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys, we offer a powerful tool for catalyst discovery. We propose new promising candidates such as ZnRh and ZnPt 3 , which to our knowledge, have not yet been tested, and discuss their possible advantage in terms of stability. Methanol ; Catalysis ; Computer science ; Process engineering ; Chemistry ; Artificial intelligence ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Mei Yang | Changxin Wang | Minhui Song | Lu Xie | Ping Qian | Yanjing Su
Source: International Journal of Hydrogen Energy (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted screening of non-metal doped MXenes catalysts for hydrogen evolution reaction MXenes ; Catalysis ; Doping ; Hydrogen ; Metal ; Materials science ; Chemistry ; Computer science ; Nanotechnology ; Optoelectronics ; Organic chemistry ; Metallurgy MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Meng Huang | Ruiyang Shi | Heng Liu | W.X. Ding | Jiahang Fan | Binghui Zhou | Bo Da | Zhengyang Gao | Hao Li | Weijie Yang
Source: The Journal of Physical Chemistry C (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Computational Single-Atom Catalyst Database Empowers the Machine Learning Assisted Design of High-Performance Catalysts The data-driven strategy has emerged as an important approach for the rapid screening of high-performance single-atom catalysts (SACs). However, the lack of a comprehensive SACs database seriously hinders the widespread application of this strategy. Herein, we construct a public SACs database comprising 1197 samples via doping nonmetallic atoms (B, N. O, P, and S) in the coordination environment and regulating 3d metal centers (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn). Based on density functional theory calculations, the electronic structural properties (i.e., Bader charge and d-band center) and binding energies are obtained. According to the binding energy calculations, 657 stable catalyst configurations are identified. Subsequently, the corresponding adsorption energies for O2, O, and NO are calculated. Moreover, machine learning (ML) models, specifically extreme gradient boosting regression (XGBR), random forest regression, and support vector regression, are trained to predict the electronic structure and the adsorption energies of O. Among these models, XGBR demonstrates the highest predictive accuracy, with a mean squared error less than 0.35. We successfully integrate ML models based on this SACs database and catalytic volcano model. Through this framework, the catalytic activities of 1261 4d SACs in the oxidation of NO and Hg0 are quickly predicted. Rh1B4 and Rh1C2S2 are identified as potential catalysts for the oxidation of NO and Hg0, with the respective energy barriers of 1.01 and 2.59 eV for Rh1B4, and 1.03 and 2.61 eV for Rh1C2S2. These values are significantly lower than those of previously reported SACs. We anticipate that this public SACs database and ML-based activity prediction framework can provide new pathways for the rapid screening of highly active SACs for various catalytic reactions. Catalysis ; Atom (system on chip) ; Computer science ; Materials science ; Chemistry ; Operating system ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Abdul Raafik Arattu Thodika | Xiaoliang Pan | Yihan Shao | Kwangho Nam
Source: Journal of Chemical Theory and Computation (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application has been hindered by the time-consuming process of generating the necessary training, validation, and test data for MLP models through QM/MM simulations. Furthermore, the entire process needs to be repeated for each specific enzyme system and reaction. To overcome this bottleneck, it is required that trained MLPs exhibit transferability across different enzyme environments and reacting species, thereby eliminating the need for retraining with each new enzyme variant. In this study, we explore this potential by evaluating the transferability of a pretrained ΔMLP model across different enzyme mutations within the MM environment using the QM/MM-based ML architecture developed by Pan, X. <i>J. Chem. Theory Comput.</i> 2021, 17(9), 5745-5758. The study includes scenarios such as single point substitutions, a homologous enzyme from different species, and even a transition to an aqueous environment, where the last two systems have MM environment that is substantially different from that used in MLP training. The results show that the ΔMLP model effectively captures and predicts the effects of enzyme mutations on electrostatic interactions, producing reliable free energy profiles of enzyme-catalyzed reactions without the need for retraining. The study also identified notable limitations in transferability, particularly when transitioning from enzyme to water-rich MM environments. Overall, this study demonstrates the robustness of the Pan et al.'s QM/MM-based ML architecture for application to diverse enzyme systems, as well as the need for further research and the development of more sophisticated MLP models and training methods. Transferability ; Dihydrofolate reductase ; Quantum ; Computer science ; Catalysis ; Chemistry ; Computational biology ; Machine learning ; Enzyme ; Physics ; Biochemistry ; Biology ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Igor Poltavsky | Anton Charkin-Gorbulin | Mirela Puleva | Grégory Fonseca | Ilyes Batatia | Nicholas J. Browning | Stefan Chmiela | Mengnan Cui | J. Thorben Frank | Stefan Heinen | Bing Huang | Silvan Käser | Adil Kabylda | Danish Khan | Carolin Müller | Alastair J. A. Price | Kai Riedmiller | Kai Töpfer | Tsz Wai Ko | Markus Meuwly | Matthias Rupp | Gábor Cśanyi | O. Anatole von Lilienfeld | Johannes T. Margraf | Klaus-Robert Müller | Alexandre Tkatchenko
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023 Atomistic simulations are routinely employed in academia and industry to study the behavior of molecules, materials, and their interfaces. Central to these simulations are force fields (FFs), whose development is challenged by intricate interatomic interactions at different spatio-temporal scales and the vast expanse of chemical space. Machine learning (ML) FFs, trained on quantum-mechanical energies and forces, have shown the capacity to achieve sub-kcal (mol<sup>-1</sup> Å<sup>-1</sup>) accuracy while maintaining computational efficiency. The TEA Challenge 2023 rigorously evaluated commonly used MLFFs across diverse applications, highlighting their strengths and weaknesses. Participants trained their models using provided datasets, and the results were systematically analyzed to assess the ability of MLFFs to reproduce potential energy surfaces, handle incomplete reference data, manage multi-component systems, and model complex periodic structures. This publication describes the datasets, outlines the proposed challenges, and presents a detailed analysis of the accuracy, stability, and efficiency of the MACE, SO3krates, sGDML, SOAP/GAP, and FCHL19* architectures in molecular dynamics simulations. The models represent the MLFF developers who participated in the TEA Challenge 2023. All results presented correspond to the state of the ML architectures as of October 2023. A comprehensive analysis of the molecular dynamics results obtained with different MLFFs will be presented in the second part of this manuscript. Crash ; Computer science ; Force field (fiction) ; Nanotechnology ; Engineering ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nuclear Materials and Properties
Authors: Guosong Zhu | Xiaoming Du | Dandan Sun
Source: Materials Today Communications (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Machine learning accelerated design of magnesium alloys with high strength and high ductility Materials science ; Ductility (Earth science) ; Magnesium ; Metallurgy ; Creep Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Aluminum Alloy Microstructure Properties
Authors: Zhi‐Ping Xie | Yanheng Li | Yijie Xia | Jun Zhang | Sihao Yuan | Fan Cheng | Yi Yang | Yi Qin Gao
Source: Journal of Chemical Theory and Computation (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Multiscale Force Field Model Based on a Graph Neural Network for Complex Chemical Systems Inspired by the QM/MM methodology, the ML/MM approach introduces a new opportunity for multiscale simulation, improving the balance between accuracy and computational efficiency. Benefited from the rapid advancements in molecular embedding methods, density functional theory level quantum mechanical (QM) calculations within the QM/MM framework can be accelerated by several orders of magnitude through the application of machine learning (ML) potential energy surfaces. As a problem inherited from the QM/MM methodology, challenges exist in designing the interactions between machine learning and molecular mechanics (MM) regions. In this study, electrostatic interactions between machine learning and MM atoms are treated by using a graphical neural network based on stationary perturbation theory. In this protocol, we process coordinates and MM charges to yield electrostatic energy and forces, resulting in a high-performance electrostatic embedding ML/MM architecture. The accuracy of the ML/MM energy was validated in aqueous solutions of alanine dipeptide and allyl vinyl ether (AVE). We investigated the transferability of parameters trained from AVE in a single solvent to various other solvents, including water, methanol, dimethyl sulfoxide, toluene, ionic liquids, and water-toluene interface environments. We then established a solvent-free protocol for data set preparation. Comparison of the free energy landscapes of the Claisen rearrangement of AVE in different solvation environments showed the catalytic effect of aqueous solutions, consistent with experiments. Computer science ; Force field (fiction) ; Artificial neural network ; Field (mathematics) ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rathachai Chawuthai | Teeratat Promchan | Jularak Rojsanga | Somrerk Chandra-ambhorn | Thanasak Nilsonthi | Patthranit Wongpromrat | Eakarach Bumrungthaichaichan | Amata Anantpinijwatna
Source: Scientific Reports (article, 2025-03-05)
Field: Materials Science — Materials Chemistry

Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning Material degradation is one of the main problems in various high-temperature processes, directly resulting in the failure of the material. Crack and protective oxide film spallation caused either by mechanical stress development in the oxidation process or thermal stress due to a mismatch of the thermal expansions of the formed oxide and alloy are common forms of failure in high-temperature processes. Typically, the Pilling-Bedworth ratio (PBR) is employed to predict crack and spallation of the oxide by determining the volume changes of oxide and alloy because of its simplicity. However, this approach provides poor crack and spallation predictions. Hence, machine learning was adopted in the present work to predict oxide formation and spallation in the temperature range of 600-1,200 °C. The inputs for the present developed model were alloy compositions, oxide formed during oxidation, and oxidation conditions and periods. Furthermore, the predicted results of the present developed machine learning model were compared to those obtained by the PBR method. The present results revealed that the accuracy of the oxide spallation prediction of the present model was better than that of the PBR method. The random forest with 15 estimators was the best machine learning model. Finally, it can be concluded that the machine learning model is essential for accurate material failure prediction. Spallation ; Oxide ; Materials science ; Alloy ; Work (physics) ; Stress (linguistics) ; Thermal oxidation ; Atmospheric temperature range ; Composite material ; Metallurgy ; Computer science ; Mechanical engineering ; Thermodynamics ; Physics ; Engineering ; Nuclear physics Nuclear Materials and Properties ; High-Temperature Coating Behaviors ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xu Wang | Yang Li | Jiahao Qian | Rui Liao | Xia Li | Yun Zou
Source: Electrochimica Acta (article, 2025-05-10)
Field: Materials Science — Biomaterials

Machine learning-assisted prediction and interpretation of electrochemical corrosion behavior in Mg–Li alloys Corrosion ; Interpretation (philosophy) ; Electrochemistry ; Materials science ; Metallurgy ; Chemistry ; Computer science ; Electrode ; Physical chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Muzhi Ma | Zhou Li | Yuyuan Zhao | Shen Gong | Qian Lei | Yanlin Jia | Wenting Qiu | Zhu Xiao | Yanbin Jiang | Xiandong Xu | Biaobiao Yang | Chenying Shi
Source: Journal of Material Science and Technology (article, 2025-01-04)
Field: Materials Science — Materials Chemistry

Developing softening-resistant Cu-Cr alloys and understanding their mechanisms via mechanism-informed interpretable machine learning Mechanism (biology) ; Softening ; Materials science ; Psychology ; Artificial intelligence ; Computer science ; Composite material ; Epistemology ; Philosophy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced materials and composites
Authors: Yu Lan | Guangsong Zheng | Run‐Wei Song | Jing-Nan Hao | Jialu Liu | Cheng‐Long Shen | Jinyang Zhu | Sheng Cao | Jialong Zhao | Qing Lou | Chongxin Shan
Source: Nature Communications (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Low-temperature molten-salt enabled synthesis of highly-efficient solid-state emitting carbon dots optimized using machine learning Fluorescent carbon dots (CDs) have garnered significant attention for their unique optoelectronic properties and applications, but their practical employment is hampered by the excessive synthesis temperature, tedious post-processing and limited solid-state luminescence efficiency. Herein, we develop a facile molten salt method to achieve the one-step synthesis of full-color CDs with efficient solid-state emission. Comprehensively, kilogram-scale solid-state CDs with a quantum yield of 90% can be readily synthesized via a salt-assisted approach under mild conditions (100-142 °C) within 10 min. The spectral characterization and density functional theory calculation confirm that zinc ion coordination can occur in liquated environment, which facilitates the polymerization of precursors at lower temperatures, suppresses the formation of non-radiative recombination channels on their surface, and further enhances luminescence in solid. The machine learning is further used to optimize CDs' luminous efficiency up to 99.86%, evoking excellent performance CDs-based light-emitting diodes with a maximum luminous efficiency of 272.65 lm W<sup>-1</sup> to drive backlit display with a long-persistent lifetime (T<sub>95</sub> at 100 cd m<sup>-2</sup> = 45108.7 h). This work provides a pathway for the design and fabrication of advanced carbon-based solid-state luminescent materials, significantly contributing to the advancement of next-generation lighting and display technologies. Carbon fibers ; Materials science ; Molten salt ; Solid-state ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Metallurgy ; Composite material ; Engineering ; Physical chemistry Carbon and Quantum Dots Applications ; Catalytic Processes in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Xinyi Wang | Hanna Breunig | Peng Peng
Source: Applied Energy (article, 2025-01-17)
Field: Chemistry — Inorganic Chemistry

Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning Hydrogen storage ; Metal-organic framework ; Range (aeronautics) ; Process engineering ; Environmental science ; Computer science ; Materials science ; Hydrogen ; Engineering ; Chemistry ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Hydrogen Storage and Materials
Authors: Shuying Lin | Linbing Jiang | Baoliang Zhang | Lingheng Luo | Xi-Jun Wu | Yu Ma | Wenguan Liu
Source: International Journal of Hydrogen Energy (article, 2025-01-17)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion in yttrium hydrides: First-principles calculations and machine learning molecular dynamics Yttrium ; Molecular dynamics ; Hydrogen ; Diffusion ; Materials science ; Chemical physics ; Thermodynamics ; Physical chemistry ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Metallurgy Nuclear Materials and Properties ; Hydrogen Storage and Materials ; Machine Learning in Materials Science
Authors: Iftikhar Hussain | Abdullah Al Mahmud | Riffat Amna | Abdul Hameed Pato | Uzair Sajjad | Zeeshan Ajmal | Kaili Zhang
Source: Materials Today (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Interface and surface engineering: The nexus of MXenes, MOFs, and AI in hybrid material design for energy storage/conversion MXenes ; Nexus (standard) ; Interface (matter) ; Energy storage ; Computer science ; Materials science ; Process engineering ; Nanotechnology ; Engineering ; Embedded system ; Composite material ; Physics ; Thermodynamics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques