jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuejian Qin | Taoyuze Lv | Zhicheng Zhong
Source: ArXiv.org (preprint, 2025-08-06)
Field: Materials Science — Materials Chemistry

EAC-Net Charge Density Dataset: 150k Sampled Grids from Materials Project CHGCAR Subset for Equivariant Learning Charge density is central to density functional theory (DFT), as it fully defines the ground-state properties of a material system. Obtaining it with high accuracy is a computational bottleneck. Existing machine learning models are constrained by trade-offs among accuracy, efficiency, and generalization. Here, we introduce the Equivariant Atomic Contribution Network (EAC-Net), which couples atoms and grids to integrate the strengths of grid-based and basis-function frameworks. EAC-Net achieves high accuracy (typically below 1% error), enhanced efficiency, and strong generalization across complex systems. Building on this framework, we develop EAC-mp, a universal charge density model covering the periodic table. The model demonstrates robust zero-shot performance across diverse systems, and generalizes beyond the training distribution, supporting downstream applications such as band structure calculations. By linking local chemical environments to charge densities, EAC-Net provides a scalable framework for accelerating electronic structure prediction and enabling high-throughput materials discovery. Generalization ; Charge (physics) ; Equivariant map ; Scalability ; Density functional theory ; Charge density ; Topology (electrical circuits) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Qinglin Wei | Jiaxiang Qiu | Ruirui Wang | Zhongti Sun | Yuee Xie | Yuanping Chen | Yangyang Wan
Source: The Journal of Physical Chemistry C (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Accelerating Band Gap Prediction and High-Throughput Screening of Covalent Organic Frameworks Based on Transfer Learning Covalent organic frameworks (COFs) are a vast class of materials with nearly infinite structural possibilities, making it challenging to quickly identify COFs with specific properties, particularly their electronic properties. In this study, we apply transfer learning to overcome these limitations by fine-tuning a pretrained model (PMTransformer) on various COF data sets, enabling the rapid prediction of COF band gaps. Our model accurately predicts COF band gaps with a mean absolute error of 0.23 eV and R2 of 0.89, outperforming the crystal graph convolutional neural network model. We validate the model’s predictions using density functional theory (DFT) calculations on a separate COF data set, confirming the consistency of predicted and DFT-calculated band gaps. By applying the model to a large COF database, we identify promising (sim) conductive COFs, demonstrating the model’s potential as an efficient screening tool for discovering COFs with optimized electronic properties for future applications in electronics and optics. Throughput ; Transfer of learning ; High-throughput screening ; Covalent bond ; Computer science ; Nanotechnology ; Computer architecture ; Materials science ; Chemistry ; Artificial intelligence ; Telecommunications ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Yuki Inada | M. Fujioka | Haruhiko Morito | Tohru Sugahara | Hisanori Yamane | Yukari Katsura
Source: Chemistry of Materials (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Elemental Reactivity Maps for Materials Discovery When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials discovery. The essential issue for such prediction is the uncertainty of whether the unreported combinations are nonreactive or not just investigated, though the reactive combinations can be easily collected as positive data sets from the materials databases. To construct the negative data sets, we developed a process to select reliable nonreactive combinations by evaluating the similarity between unreported and reactive combinations. The machine learning models were trained by both data sets, and the prediction results were visualized by two-dimensional heatmaps: elemental reactivity maps to identify elemental combinations with high reactivity but no reported stable compounds. The maps predicted high reactivity (i.e., synthesizability) for the Co–Al–Ge ternary system, and two novel ternary compounds were synthesized: Co4Ge3.19Al0.81 and Co2Al1.26Ge1.74. Reactivity (psychology) ; Materials science ; Elemental analysis ; Nanotechnology ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Georganna Benedetto | Robert M. Stolz | Zheng Meng | Joseph Y. M. Chan | Elissa O. Shehayeb | Colin T. Morrell | Gbenga Fabusola | Nikolaus Elsaesser | Cory M. Simon | Katherine A. Mirica
Source: Journal of the American Chemical Society (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Conductive Covalent Organic Frameworks as Chemiresistive Sensor Arrays for the Detection and Differentiation of Gasotransmitters This paper describes a chemiresistive sensor array using four structurally analogous, but chemically distinct, conductive covalent organic frameworks (COFs) (M-COF-DC-8, M = Fe, Co, Ni, and Cu) capable of detecting and differentiating four important gaseous analytes: nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H<sub>2</sub>S), and ammonia (NH<sub>3</sub>). The COFs were synthesized from the condensation of 2,3,9,10,16,17,23,24-octaamino-metallophthalocyanine precursors with pyrenetetraone linkers resulting in chemically robust and electrically conductive materials. Chemiresistive sensing experiments, together with machine learning to parse the response pattern of the sensor array, show that the M-COF-DC-8 (M = Fe, Co, Ni, Cu) materials can detect and differentiate this suite of oxidizing and reducing gases at parts-per-million concentrations, with theoretical limits of detection (LOD) in the parts-per-billion range in dry N<sub>2</sub>. Importantly, the COF array containing M-COF-DC-8 (M = Co, Ni, Cu) retains its ability to detect and differentiate these analytes in air and humidity under low power consumption. Spectroscopic investigations reveal that the synthetic control over the identity of the metallophthalocyanine core efficiently tunes material-analyte interactions and, therefore, emergent device performance. The use of highly tunable COFs as the active material in sensor arrays enables low-power, sensitive, and real-time gas detection with future applications in healthcare and personal protection. Electrical conductor ; Covalent bond ; Analyte ; Oxidizing agent ; Hydrogen sulfide ; Covalent organic framework ; Carbon monoxide ; Chemiresistor Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Xiaohu Yu | Liping Feng | Lingfeng Jia | Xin Zhang | Yunjia Huang | Pengfei Liu | Xitong Wang | Yulong Yang | Dongming Sun | Ting Yu
Source: ACS Materials Letters (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

High-Performance <i>p</i>-Type Field-Effect Transistor Based on WS<sub>2</sub> Monolayer Prepared by Liquid-Phase-Assisted Space-Confined CVD Although some progress has been made in the synthesis of p-type two-dimensional (2D) WS2, the hole mobility of WS2 field-effect transistors (FETs) is still very low, significantly impeding its application in logic complementary circuits. In this study, p-type 2D WS2 has been successfully prepared by liquid-phase assisted space-confined chemical vapor deposition (LASCVD) method and machine learning (ML) has been employed to optimize the process parameters of the growth of WS2 nanosheets. Results show that, under the optimal growth conditions, the maximum domain size of WS2 monolayer reaches 475 μm. Additionally, W vacancies in WS2 nanosheets have been observed through STEM-HAADF, Raman and PL analysis, contributing to the p-type characteristic of 2D WS2. Moreover, p-type high-performance FETs based on the WS2 monolayer have been fabricated, exhibiting a high hole mobility of 46.87 cm2 V–1 s–1 and an on/off ratio of 105. This study provides a promising direct synthesis method for p-type 2D TMDCs. Monolayer ; Space (punctuation) ; Transistor ; Field-effect transistor ; Materials science ; Phase (matter) ; Field (mathematics) ; Liquid phase ; Analytical Chemistry (journal) ; Optoelectronics ; Chemistry ; Nanotechnology ; Physics ; Computer science ; Chromatography ; Mathematics ; Thermodynamics ; Quantum mechanics ; Organic chemistry ; Pure mathematics 2D Materials and Applications ; Perovskite Materials and Applications ; Nanowire Synthesis and Applications
Authors: Vahe Gharakhanyan | Yi Yang | Luis Barroso-Luque | Muhammed Shuaibi | Daniel S. Levine | Kyle Michel | Viachaslau Bernat | Misko Dzamba | Xiang Fu | Meng Gao | Xingyu Liu | Keian Noori | Lafe J. Purvis | T.K. Gundu Rao | Brandon M. Wood | Ammar Rizvi | Matt Uyttendaele | Andrew J. Ouderkirk | Chiara Daraio | C. Lawrence Zitnick | Arman Boromand | Noa Marom | Zachary W. Ulissi | Anuroop Sriram
Source: ArXiv.org (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms Crystal Structure Prediction (CSP) of molecular crystals plays a central role in applications, such as pharmaceuticals and organic electronics. CSP is challenging and computationally expensive due to the need to explore a large search space with sufficient accuracy to capture energy differences of a few kJ/mol between polymorphs. Dispersion-inclusive density functional theory (DFT) provides the required accuracy but its computational cost is impractical for a large number of putative structures. We introduce FastCSP, an open-source, high-throughput CSP workflow based on machine learning interatomic potentials (MLIPs). FastCSP combines random structure generation using Genarris 3.0 with geometry relaxation and free energy calculations powered entirely by the Universal Model for Atoms (UMA) MLIP. We benchmark FastCSP on a curated set of 28 mostly rigid molecules, demonstrating that our workflow consistently generates known experimental structures and ranks them within 5 kJ/mol per molecule of the global minimum. Our results demonstrate that universal MLIPs can be used across diverse compounds without requiring system-specific tuning. Moreover, the speed and accuracy afforded by UMA eliminate the need for classical force fields in the early stages of CSP and for final re-ranking with DFT. The open-source release of the entire FastCSP workflow significantly lowers the barrier to accessing CSP. CSP results for a single system can be obtained within hours on tens of modern GPUs, making high-throughput crystal structure prediction feasible for a broad range of scientific applications. Workflow ; Benchmark (surveying) ; Crystal structure prediction ; Set (abstract data type) ; Range (aeronautics) ; Relaxation (psychology) ; Crystal (programming language) ; Molecular dynamics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Masayoshi Matsubara | Sasaki Ryo | J Takahara | Shinji Moritake | Yasuyuki Harada | Hiromasa Kaneko
Source: Analytical Science Advances (article, 2025-07-30)
Field: Chemistry — Analytical Chemistry

Development of a Model for Predicting the Thermophysical Properties of Carbon Materials and Proposal of Manufacturing Conditions Using the Model A steelmaking method using electric furnaces is attracting attention in the iron and steel industry, and a carbon material called needle coke is used as an aggregate for the electrode in electric steelmaking. The performance of needle coke as an aggregate for electrodes in steelmaking is greatly affected by the quality of the needle coke, which depends on the ingredients of the raw materials and the process conditions. Because the raw material ingredients are not always constant and depend on the place and time they are produced, the quality of the needle coke is not stable under the same process conditions. Therefore, it is necessary to optimise the process conditions. In this study, to optimise the process conditions using machine learning, a model was constructed to predict the thermophysical properties of needle coke from the raw material ingredients and process conditions based on previous data. Because the subject plant is operated in a dynamic process and there is a time delay in the previous data, the genetic-algorithm-based process variables and dynamics selection method, which selects the time delays and process variable regionally, was studied. Furthermore, inverse analysis was performed on a sample whose quality was considered to be outside the specifications based on the previous data, with the aim of controlling the quality within the product specifications by changing only the process conditions. Steelmaking ; Coke ; Process engineering ; Raw material ; Process (computing) ; Quality (philosophy) ; Aggregate (composite) ; Computer science ; Materials science ; Mechanical engineering ; Engineering ; Metallurgy ; Composite material Spectroscopy and Chemometric Analyses ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Sungmo Kang | Rokyeon Kim | Seungwu Han | Young‐Woo Son
Source: APL Machine Learning (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while preserving its formal simplicity and computational efficiency. We apply the new method to binary and ternary systems such as GeSe and Ge-Sb-Te (GST) compounds, well-known materials for non-volatile phase-change memory and related technologies. Using a training set of ab initio electronic energy bands and rotation-covariant descriptors for various GeSe and GST compounds, we generate transferable EPs for Ge, Se, Sb, and Te. We demonstrate that the new ML model accurately reproduces the energy bands and wavefunctions of structures outside the training set, closely matching first-principle calculations. This accuracy is achieved with significantly lower computational costs due to the elimination of self-consistency iterations and the reduced size of the plane-wave basis set. Notably, the method maintains accuracy even for diverse local atomic environments, such as amorphous phases or larger systems not explicitly included in the training set. Amorphous solid ; Materials science ; Nanotechnology ; Engineering physics ; Crystallography ; Chemistry ; Engineering Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films
Authors: Faouzi Lasmi | Houria Hamitouche | Hassiba Laribi-Habchi | Amel Boudechicha | Anis Lasmi | Rima Benbekai | Sid Ahmed Bennoui | Yacine Benguerba | Nadjib Chafai
Source: Journal of Applied Electrochemistry (article, 2025-04-02)
Field: Chemistry — Electrochemistry

Electrochemical synthesis of PVP K30-AgNPs modelling based on ANN-LMP: Box-Behnken optimization for antioxidant and antibacterial activities Box–Behnken design ; Electrochemistry ; Chemistry ; Antibacterial activity ; Antioxidant ; Nuclear chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Chromatography ; Electrode ; Bacteria ; Biochemistry ; Response surface methodology ; Biology Electrochemical Analysis and Applications ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Liu L | Lixian Lian | Xiaobing Li | Jiaqi Wu | Wang Hu | Ying Liu
Source: Materials Genome Engineering Advances (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Intelligent optimized design of novel high‐temperature titanium alloys Abstract 600°C is regarded as the “thermal barrier” temperature for traditional Ti‐based alloys. As the working temperature rises, alloys' creep performance and strength at high temperatures exhibit a dramatic decrease, which becomes a major obstacle to the development of high‐temperature titanium alloys. In order to break the thermal barrier temperature, a new design strategy that integrates machine learning with multiobjective optimization has been employed. A high‐precision predictive model has been established, achieving R 2 values exceeding 0.9, with mean absolute error (MAE) and root mean square error (RMSE) not exceeding 5 and 11, respectively. By referencing domain knowledge, constraints have been proposed, leading to the optimization. Additionally, the α 2 phase is utilized as a reinforcement phase, balancing plasticity while controlling its content range. Titanium alloys that demonstrate high yield strength (greater than 490 MPa) and extended creep life (exceeding 25 h), suitable for conditions up to 650°C, have been designed using multiobjective optimization with constraints. Compared to current typical high‐temperature titanium alloys, these newly developed alloys exhibit superior yield strength and creep life with similar density and cost. This method provides a valuable reference for designing advanced high‐temperature titanium alloys. Titanium ; Materials science ; Titanium alloy ; Metallurgy ; Computer science ; Alloy Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Aluminum Alloy Microstructure Properties
Authors: Wei Wei | Ziyang Wang | Chao Xu | Zhiyi Li | Hongliang Bai | Zhijun Liu
Source: Industrial & Engineering Chemistry Research (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Advances in the Structural Design of Covalent Organic Frameworks for Direct Air Capture of CO <sub>2</sub> The increasing concentration of atmospheric carbon dioxide (CO2) poses a severe threat to global climate security, prompting an urgent demand for efficient carbon removal technologies. Among various carbon capture strategies, direct air capture (DAC) stands out due to its ability to extract CO2 directly from ambient air, making it a critical component of negative emissions approaches. Covalent organic frameworks (COFs), as a class of crystalline and porous organic polymers with tunable structures and high thermal and chemical stability, have emerged as promising candidates for DAC applications. Their well-defined architectures enable precise control over pore size, topology, surface area, and functional group incorporation, all of which are key to achieving selective and efficient CO2 adsorption under low-concentration conditions. This review systematically summarizes recent advances in the structural design and functional regulation of COFs for DAC. We first introduce the evolution of COF structural dimensionality─from 2D to 3D frameworks─and their impact on porosity and mass transfer. Subsequently, we discuss various strategies for functionalizing COFs with active sites, such as amines, ionic groups, and metal centers, to enhance the CO2-binding affinity and moisture resistance. Computational approaches, including grand canonical Monte Carlo (GCMC) simulations, density functional theory (DFT), and machine learning (ML), are also reviewed as powerful tools to guide the rational design of COF-based DAC materials. Finally, we evaluate the performance of representative COFs in DAC applications and outline the current challenges and future directions toward scalable, energy-efficient, and cost-effective COF-DAC systems. This review aims to provide a comprehensive reference for advancing COF-based materials toward practical deployment in carbon capture technologies. Rational design ; Adsorption ; Carbon fibers ; Metal-organic framework ; Density functional theory ; Porosity ; Surface modification ; Key (lock) Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Yufan Yao | Shuai Lv | Wei Hu
Source: Solid State Communications (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Influence of exchange–correlation functional choices on machine learning potential accuracy in the coupled PWDFT-DeePMD framework Reliability (semiconductor) ; Quality (philosophy) ; Coupling (piping) ; Basis (linear algebra) ; Density functional theory ; Training (meteorology) ; Artificial neural network Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Nuclear Physics and Applications
Authors: Tiancheng Cui | Lige Zhang | Huijuan Yan | Yuandalei Cao | Ruizhu Li | Zhibin Yang | Jian-Qiang Wang | Guoping Xiao
Source: Energy and AI (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Transient thermomechanical response of an electrolyte supported planar solid oxide electrolysis cell under dynamic loading conditions Solid oxide electrolysis cells (SOECs) offer high efficiency, noble-metal-free construction, and broad power adaptability, making them promising for large-scale green hydrogen production integrated with renewable energy. However, the intermittency of renewable energy induces spatial and temporal gradients in temperature and electrochemical fields of the cell, leading to stress concentrations and potential mechanical failure. Understanding the transient thermomechanical response during dynamic loading of a SOEC is thus crucial for evaluating the electrochemical performance and predicting cell lifetime. While previous studies focused on developing steady-state models to investigate the thermal stress in the cell, we develop a transient multi-physics model to capture the coupled thermo–electro–chemo–mechanical behavior of an electrolyte-supported planar SOEC under dynamic operating conditions. Numerical simulations are performed with varying power control strategies, heating rates and water vapor molar fractions. Results reveal that the electrolyte layer consistently experiences the highest average maximum principal stress and failure risk. Stepped current density control induces a higher heating rate and thermal stress compared to stepped voltage control. Additionally, a slower water molar fraction decline reduces electrochemical reaction and heat absorption rates, leading to a slower temperature rise and reduced thermal stress. To enable rapid and accurate prediction of critical thermomechanical responses, a Random Forest Regression model is trained on simulation data using gas inlet heating rate as input. The model accurately predicts temperature and stress in the electrolyte layer and demonstrates strong generalization on unseen scenarios. The integration of high-fidelity physics-based modeling and machine learning provides a foundation for intelligent SOEC control and real-time optimization, enhancing system reliability and extending operational lifetime under renewable energy operation. Electrolyte ; Materials science ; Transient (computer programming) ; Electrolysis ; Oxide ; Electrolytic cell ; Planar ; Transient response ; Composite material ; Chemistry ; Electrode ; Computer science ; Metallurgy ; Electrical engineering ; Engineering Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Fuel Cells and Related Materials