jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhendong Li | Longwei Han | Tao Ouyang | Juexian Cao | Yongxin Yao | Xiaolin Wei
Source: Physical Review Materials (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Thermal and mechanical properties of deep-ultraviolet light sources candidate materials <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>BeGeN</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> by machine-learning molecular dynamics simulations It is well known that thermal and mechanical properties are the key physical parameters of deep-ultraviolet (DUV) light sources materials, and play critical roles in practical applications. In this work, the thermal conductivity, thermal expansion coefficient, and mechanical performance of newly predicted DUV materials are systematically investigated by means of neuroevolution potential (NEP) combined molecular dynamics (MD) simulation. Both equilibrium molecular dynamics (EMD) and homogeneous nonequilibrium molecular dynamics (HNEMD) calculations demonstrate that the ${\mathrm{BeGeN}}_{2}$ hosts the best thermal transport performance with $39.4\phantom{\rule{0.16em}{0ex}}{\mathrm{Wm}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$ at room temperature, among the five DUV light-emitting candidates (i.e., ${\mathrm{BeGeN}}_{2}, {\mathrm{Mg}}_{3}{\mathrm{NF}}_{3},\phantom{\rule{0.16em}{0ex}}{\mathrm{KCaBr}}_{3}$, KHS, and RbHS). Significant boosted thermal conductivity (about 104 ${\mathrm{Wm}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$) could be further observed in compressive ${\mathrm{BeGeN}}_{2}$ with $3%$ strain, implying the ability of ${\mathrm{BeGeN}}_{2}$ to address the severe heat dissipation issue in DUV optoelectronic devices. By performing phonon spectral energy density analysis, we reveal that such enhancement of thermal conductivity mainly originates from the increased phonon lifetime in the low-frequency region. Meanwhile, our calculations also show that the coefficient of thermal expansion of ${\mathrm{BeGeN}}_{2}$ is merely $25.87\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}/\mathrm{K}$, the corresponding Pugh modulus ratio (G/B), and the Vickers hardness are, respectively, 0.719 and 22.239 GPa. The findings presented in this work authenticate that the ${\mathrm{BeGeN}}_{2}$ possesses a high thermal conductivity and stable mechanical properties, foreshowing the great potential in DUV light-emitting field applications. Materials science ; Ultraviolet ; Thermal ; Thermodynamics ; Physics ; Optoelectronics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Semiconductor Detectors and Materials
Authors: Mingming Wei | Mei Yang | Leng Han | Yang Cao
Source: Sensors and Actuators B Chemical (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

A novel intelligent sensing strategy: Integration of metal-doped carbon dots nanozymes and machine learning for rapid screening of biothiols in disease Doping ; Nanotechnology ; Carbon fibers ; Computer science ; Chemistry ; Materials science ; Optoelectronics ; Composite number Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Carbon and Quantum Dots Applications
Authors: Nicholas J. Williams | Lara Kabalan | Ljiljana Stojanović | Viktor Zólyomi | Edward O. Pyzer‐Knapp
Source: Scientific Data (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface. We present Hessian QM9, the first database of equilibrium configurations and numerical Hessian matrices, consisting of 41,645 molecules from the QM9 dataset at the ωB97x/6-31G* level. Molecular Hessians were calculated in vacuum, as well as water, tetrahydrofuran, and toluene using an implicit solvation model. To demonstrate the utility of this dataset, we show that incorporating second derivatives of the potential energy surface into the loss function of a MLIP significantly improves the prediction of vibrational frequencies in all solvent environments, thus making this dataset extremely useful for studying organic molecules in realistic solvent environments for experimental characterization. Hessian matrix ; Quantum chemistry ; Chemistry ; Computational chemistry ; Quantum ; Information retrieval ; Organic chemistry ; Computer science ; Reaction mechanism ; Physics ; Mathematics ; Applied mathematics ; Catalysis ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Daniel M. Kaplan | Angang Zheng | Joanna Bławat | Rongying Jin | R. J. Cava | V. S. Oudovenko | Gabriel Kotliar | A. Sengupta | Weiwei Xie
Source: The European Physical Journal Plus (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Deep learning-based superconductivity prediction and experimental tests Abstract The discovery of novel superconducting materials is a long-standing challenge in materials science, with a wealth of potential for applications in energy, transportation and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized a compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction. Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge of the chemical properties of the compound, while our neural net inputs depend solely on the chemical composition. With the help of hints from our network, we discover a new ternary compound Mo 20 Re 6 Si 4 , which becomes superconducting below 5.4 K. We further discuss the existing limitations and challenges associated with using AI to predict and, along with potential future research directions. Superconductivity ; Artificial intelligence ; Condensed matter physics ; Computer science ; Physics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Electronic and Structural Properties of Oxides
Authors: Hongxing Yue | Shuyu Wang | Xiaochun Sha | Qiaoyun Wang
Source: Chemical Engineering Journal (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Discovery of polymers with high bulk modulus and low thermal conductivity using a hybrid generative pipeline Pipeline (software) ; Thermal conductivity ; Polymer ; Materials science ; Thermal ; Modulus ; Conductivity ; Generative grammar ; Composite material ; Mechanical engineering ; Computer science ; Engineering ; Chemistry ; Physics ; Thermodynamics ; Artificial intelligence Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Ying Fang | Suraj Venkateshwaran Mullurkara | Keith M. Taddei | Paul R. Ohodnicki | Guofeng Wang
Source: npj Computational Materials (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Machine learning enabled accurate prediction of structural and magnetic properties of cobalt ferrite A machine learning enabled computational approach has been developed to accurately predict the equilibrium degree of inversion in spinel lattice and some magnetic properties of cobalt ferrite (CoFe₂O₄) crystal. The computational approach is composed of construction of a database from density functional theory calculations, training of machine learning models, and atomistic simulations. Support vector regression was employed to derive the relation between system energy and atomic structures of CoFe₂O₄. Using this trained machine learning model, atomistic Monte Carlo simulations predicted the equilibrium degree of inversion of CoFe₂O₄ to be 0.755 at 1237 K. The strength of twenty-three types of superexchange interactions were determined using the linear regression model and further applied in magnetic Monte Carlo simulations to predict the Curie temperature of CoFe2O4 to be 914 K. The predictions from the presented computational approach are well validated by the results from neutron diffraction measurement on CoFe₂O₄. Cobalt ferrite ; Ferrite (magnet) ; Cobalt ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy ; Composite material Magnetic Properties and Synthesis of Ferrites ; Non-Destructive Testing Techniques ; X-ray Diffraction in Crystallography
Authors: Xiaoyu Yang | Peng Wang | Xuze Tang | Zinan Wang | Jiarui Ye | Wei Duan | Ying Yue | Tiejun Ci | Yunpeng Liu | Yang Ju
Source: Nano Energy (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Dual-modal hydrogels with synergistically enhanced mechanical-thermoelectric performance for intelligent wearable sensing and automotive temperature feedback systems Materials science ; Automotive industry ; Thermoelectric effect ; Wearable computer ; Self-healing hydrogels ; Wearable technology ; Modal ; Dual (grammatical number) ; Nanotechnology ; Automotive engineering ; Composite material ; Computer science ; Embedded system ; Engineering ; Aerospace engineering Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Jiahui Liu | Jesper Byggmästar | Zheyong Fan | Bing Bai | Ping Qian | Yanjing Su
Source: Journal of Nuclear Materials (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy Alloy ; High entropy alloys ; Radiation tolerance ; Entropy (arrow of time) ; Computer science ; Nuclear engineering ; Materials science ; Artificial intelligence ; Physics ; Metallurgy ; Thermodynamics ; Engineering ; Medicine ; Radiation therapy ; Internal medicine Nuclear Materials and Properties ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Jacob Toney | R. Michel | Aaron Garrison | Ilia Kevlishvili | Heather J. Kulik
Source: Proceedings of the National Academy of Sciences (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Graph neural networks for predicting metal–ligand coordination of transition metal complexes High-throughput virtual screening campaigns are invaluable for surveying the combinatorial space of possible transition metal complexes (TMCs), but they rely on accurate metal-ligand connectivity for meaningful results. Here, we curate a dataset of 70,069 unique ligands of known coordination from experimental structures of TMCs deposited in the Cambridge Structural Database. Using this dataset, we train separate graph neural network models to predict the total number and individual identities of ligand coordinating atoms with high accuracy and precision. Interpreting each model in terms of the learned molecular representations uncovers trends aligned with our understanding of coordination chemistry as well as chemical insights. Next, we integrate the trained models with the high-throughput screening software molSimplify and illustrate their utility by generating 1,175 TMCs and validating their geometries with density functional theory calculations. We anticipate these models will accelerate computational screening of TMCs with de novo combinations of metals and ligands in physically realistic coordination. Chemical space ; Artificial neural network ; Graph ; Transition metal ; Software ; Graph theory ; Virtual screening ; Software tool Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Xiaoli Wang | Qiao-Zhi Li | Yuliang Zhao | Xingfa Gao
Source: ACS Applied Materials & Interfaces (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Recent Advances in Oxidase-like Nanozymes: Mechanisms, Prediction Models, and Applications Nanozymes, defined as nanomaterials exhibiting intrinsic enzyme-like catalytic properties, represent a rapidly expanding interdisciplinary frontier. Since the initial discovery of peroxidase-like nanozymes, a wide variety of nanomaterials have demonstrated diverse enzyme-like activities. In recent years, oxidase (OXD)-like nanozymes have demonstrated considerable potential, particularly in applications such as cancer therapy, biosensing, environmental monitoring, antibacterial activity, food safety, industrial catalysis, energy storage, and so on. This Perspective systematically summarizes recent progress in OXD-like nanozymes, including metals, metal oxides, carbon materials, metal-organic frameworks, and single/double-atom catalysts. Following the introduction of the characteristics, advantages, and applications of OXD-like nanozymes, this review discusses the catalytic mechanisms underlying OXD-like activity, specifically the four-electron (4e<sup>-</sup>) and two-electron (2e<sup>-</sup>) pathways for several important substrates, including 3,3',5,5'-tetramethylbenzidine (TMB), glucose (Glu), ascorbic acid (AA), nicotinamide adenine dinucleotide (NADH), uric acid (UA), and glutathione (GSH). It further examines the application of first-principles calculations and machine learning (ML) in predicting nanozyme activity and guiding rational design. Current challenges in mechanistic elucidation and computational model optimization are also addressed, along with future research perspectives. By integrating experimental findings and theoretical studies, this review aims to provide guidelines for the future development and application of OXD-like nanozymes. Ascorbic acid ; Nanomaterials ; Rational design ; Nicotinamide adenine dinucleotide ; Variety (cybernetics) Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Yujing Zhang | Huabing Cai | Beien Zhu | Zhongkang Han | Hui Li | M. V. Ganduglia-Pirovano | Yi Gao
Source: The Journal of Physical Chemistry Letters (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Revealing the Non-Arrhenius Migration of Oxygen Vacancies at the CeO<sub>2</sub>(111) Surface Ceria (CeO<sub>2</sub>) is renowned for its exceptional oxygen storage capacity, which makes it highly valuable in various applications. Central to its functionality is the migration of oxygen vacancies (V<sub>O</sub>'s). While previous studies have extensively examined the distribution of V<sub>O</sub>'s and Ce<sup>3+</sup> polarons, their kinetic behaviors and interactions, especially in the presence of multiple vacancies, are not yet fully understood. In this study, we employ density functional theory (DFT), ab initio molecular dynamics (AIMD) simulations, and machine-learning methods to investigate these phenomena. Our findings reveal a nonmonotonic temperature dependence of the migration rate of V<sub>O</sub>'s at the CeO<sub>2</sub>(111) surface. Our theoretical model further demonstrates that the migration of V<sub>O</sub>'s and the hopping of polarons are intricately coupled. Notably, frequent polaron hopping at high temperatures hinders V<sub>O</sub> migration, indicating a non-Arrhenius mechanism. This finding is further validated through long-time molecular dynamics simulation enhanced with neural network potentials. Our results provide a microscopic understanding of the interplay between V<sub>O</sub>'s and Ce<sup>3+</sup> polarons, offering crucial insights into the complex dynamics governing oxygen vacancy migration in ceria. This knowledge paves the way for improved material design and functionality. Arrhenius equation ; Oxygen ; Materials science ; Surface (topology) ; Thermodynamics ; Mineralogy ; Crystallography ; Chemistry ; Activation energy ; Physical chemistry ; Physics ; Geometry ; Mathematics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advancements in Solid Oxide Fuel Cells
Authors: Yuechun Li | Zhaowen Cui | Chunyan Ji | Cancan Jia | Wentao Zhang | Jianlong Wang | Yizhong Shen
Source: Advanced Science (review, 2025-08-29)
Field: Materials Science — Materials Chemistry

Multi‐Dimensional Synergistic Engineering for Boosting Nanozyme Catalysis Pursuing nanozymes with predominant catalytic activities holds great potential in meeting the needs of multi-field applications, such as in the environment, yet remains challenging. Herein, the cutting-edge strategies for boosting nanozyme catalysis are systematically analyzed from four synergistic dimensions, and a theoretical framework is developed integrating morphological structure, electronic structure, external stimulation, and machine learning (ML)-aided design. From the morphological perspective, the structure-activity relationship between nanostructures and catalytic performance is elucidated, revealing the regulatory mechanism underlying active site exposure, substrate accessibility, and electron transfer kinetics. Subsequently, the electronic structure governed by catalytic activities through precisely optimizing adsorption energy and reaction pathways, such as d-band center, e<sub>g</sub> occupancy, defect engineering, spin state, etc., is discussed in depth. Then, the mechanism underlying the dynamic regulation of catalytic activity via external stimulations, such as ultrasound, light, electric field, etc., is systematically summarized. Notably, the revolutionary role of ML-driven high-throughput screening in analyzing complex structure-activity relationships and accelerating the discovery of high-performance nanozymes is emphasized. Ultimately, this paper highlights the key role of interdisciplinary integration, encompassing material science, catalytic engineering, and artificial intelligence, etc., in overcoming current bottlenecks, unlocking the potential of nanozymes to address global challenges, and providing a new perspective for advancing the development of nanozymology. Nanotechnology ; Mechanism (biology) ; Biochemical engineering ; Computer science ; Catalysis ; Materials science ; Chemistry ; Engineering ; Physics Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Arsalan D. Badaraev | Sven Rutkowski | Shadfar Davoodi | Sergei I. Tverdokhlebov
Source: Computers in Biology and Medicine (article, 2025-08-12)
Field: Materials Science — Biomaterials

Predicting diameter and tensile strength of electrospun fibers for biomedicine: A comparison of Box-Behnken design, traditional machine learning and deep learning Box–Behnken design ; Ultimate tensile strength ; Materials science ; Electrospinning ; Artificial neural network ; Composite material ; Linear regression ; Fiber ; Overfitting ; Response surface methodology ; Machine learning ; Artificial intelligence ; Computer science ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Xin Zhong | Felix Höfling | Timm John
Source: Geochemistry Geophysics Geosystems (article, 2025-02-01)
Field: Materials Science — Ceramics and Composites

Hydrogen Diffusion in Garnet: Insights From Atomistic Simulations Abstract Garnet has been widely used to decipher the pressure‐temperature‐time history of rocks, but its physical properties such as elasticity and diffusion are strongly affected by trace amounts of hydrogen. Experimental measurements of H diffusion in garnet are limited to room pressure. We use atomistic simulations to study H diffusion in perfect and defective garnet lattices, focusing on protonation defects at the Si and Mg sites, which are shown to be energetically favored. Transient trapping of H renders ab‐initio simulations of H diffusion computationally challenging, which is overcome with machine learning techniques by training a deep neural network that encodes the interatomic potential. Our results from such deep potential molecular dynamics (DeePMD) simulations show high mobility of hydrogen in defect‐free garnet lattices, whereas H diffusivity is significantly diminished in defective lattices. Tracer simulations focusing on H alone highlight the vital role of atomic vibrations of heavier atoms like Mg on the release of H atoms. Two regimes of H diffusion are identified: a diffuser‐dominated regime at high hydrogen content with low activation energies due to saturation of vacancies by hydrogen, and a vacancy‐dominated regime at low hydrogen content with high activation energies due to trapping of H atoms at vacancy sites. These regimes account for experimental observations, such as a H‐concentration dependent diffusivity and the discrepancy in activation energy between deprotonation and D‐H exchange experiments. This study underpins the crucial role of vacancies in H diffusion and demonstrates the utility of machine‐learned interatomic potentials in studying kinetic processes in the Earth's interior. Geology ; Diffusion ; Hydrogen ; Geophysics ; Earth science ; Seismology ; Thermodynamics ; Physics Glass properties and applications ; Mineralogy and Gemology Studies ; Crystal Structures and Properties
Authors: Yaochen Yu | Jiahui Fan | Yajie Lei | Haiyang Niu
Source: Journal of Material Science and Technology (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Structure exploration of gallium based on machine-learning potential Gallium ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Andaç Batur Çolak
Source: International Journal of Hydrogen Energy (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Optimizing hydrogen storage in Li-decorated borophene slit pores: A novel machine learning study on volumetric and gravimetric capacities Gravimetric analysis ; Hydrogen storage ; Borophene ; Materials science ; Chemistry ; Nanotechnology ; Composite material ; Alloy ; Monolayer Hydrogen Storage and Materials ; Advancements in Battery Materials ; Fuel Cells and Related Materials
Authors: Baoyin Yuan | Xiaohan Zhang | Tang Chun-mei | Ning Wang | Siyu Ye
Source: Journal of Materials Informatics (article, 2025-03-22)
Field: Materials Science — Materials Chemistry

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell? The development of efficient and stable hydrogen production technologies is crucial for global clean energy transition. Solid oxide electrolysis cells (SOECs) have emerged as a promising technology for green hydrogen production due to their high efficiency, low-cost catalysts, and excellent adaptability to renewable energy sources. However, significant challenges remain in materials design, interface engineering, and system integration. This perspective reviews recent advances in artificial intelligence (AI)-guided SOEC development, focusing on machine learning approaches for design of key materials. Furthermore, we highlight how AI technologies can address the key challenges in both single-cell performances and system-level integration with renewable energy sources. Looking forward, we outline strategic directions for advancing AI-driven SOEC development toward commercial implementation, which may offer valuable insights and experiences within the field of energy conversion and storage. Hydrogen production ; Electrolysis ; Production (economics) ; Hydrogen ; Oxide ; Chemistry ; Materials science ; Chemical engineering ; Metallurgy ; Engineering ; Organic chemistry ; Electrode ; Economics Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Molten salt chemistry and electrochemical processes