jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammadreza Mohammadpour | Amirreza Mohammadpour
Source: Separation and Purification Technology (article, 2025-08-06)
Field: Chemistry — Inorganic Chemistry

Cross-Material Forecasting-Optimization of CO2 adsorption using Deep Transfer Learning and Particle Swarm Optimization: From porous carbons to Metal-Organic frameworks Adsorption ; Particle swarm optimization ; Metal-organic framework ; Porosity ; Transfer of learning ; Particle (ecology) ; Materials science ; Process engineering ; Computer science ; Chemical engineering ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Engineering ; Chemistry ; Machine learning ; Organic chemistry ; Geology ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Ye Ma | Songnan Lin | Bihan Wen
Source:  (article, 2025-03-12)
Field: Chemistry — Analytical Chemistry

Hyperspectral Image Reconstruction with Unseen Material Detection Reconstruction of hyperspectral images (HSIs) from their RGB measurements is an ill-posed inverse problem. The key to successful reconstruction relies on establishing an effective HSI prior, for which deep learning techniques have achieved impressive performance. However, the scarcity of large-scale HSI datasets poses a significant challenge, limiting the practical application of deep HSI reconstruction methods and often leading to incorrect results when dealing with unseen substances or materials. To tackle this challenge, we propose a deep RGB-to-HSI reconstruction model based on the sparse prior of hyperspectral signals. The network can effectively correlate HSI and RGB features via shared sparse codes, representing the weights of spectral-unique materials. Besides, testing images with unseen materials can be detected by measuring their sparse modeling errors. Experimental results demonstrate that the proposed method achieves promising results on RGB-to-HSI reconstruction. Further, the sparse modeling error evidently demonstrates its efficacy as an indicator for unseen materials. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Computer vision ; Iterative reconstruction ; Image (mathematics) ; Pattern recognition (psychology) ; Remote sensing ; Geology Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Remote-Sensing Image Classification
Authors: Xujian Wang | Xiongwu Wu | Bernard R. Brooks | Junmei Wang
Source: ChemRxiv (preprint, 2025-03-07)
Field: Materials Science — Materials Chemistry

Accurate Free Energy Calculation viaMultiscale Simulations Driven by HybridMachine Learning and Molecular MechanicsPotentials Our study focused on the implementation and testing of machine learning interatomic potentials (MLIPs) into the AMBER software suite. This implementation enables us to perform a novel type of molecular dynamics simulation utilizing the hybrid machine learning/molecular mechanics (ML/MM) potentials. To underpin the capabilities of ML/MM simulations, we first validated our implementation at a fundamental physical level by confirming energy and momentum conservation laws. The successful validation indicates that our implementation is able to produce adequate and physically interpretable samplings. Building upon this, for the first time to the best of our knowledge, we proposed an ML/MM-compatible thermodynamic integration (TI) protocol to tackle real-world challenges, such as solvation free energy calculation. Our results demonstrate that this computational protocol can predict hydration free energies with an accuracy of less than 1.00 kcal/mol compared to experimental data, paving the way for the use of ML/MM in multiscale simulations to addressing future drug design problems. Moreover, by applying ML/MM in molecular dynamics simulations of protein-ligand complexes, we demonstrated that the adequate samplings enable us to accurately reproduce experimental binding free energies. Thus, our implementation can offer new insights into biomolecular systems using the ML/MM "microscope". Last, we demonstrated that our implementation can achieve nanosecond timescale simulations daily after significant effort being put to improve the code performance. In a conclusion, we have successfully implemented ML/MM potential to AMBER software package after overcoming limitations in current multi-scale simulations including low computational efficiency. We have advanced TI theory allowing us to accurately predict free energies with ML/MM potentials. Energy (signal processing) ; Molecular mechanics ; Computer science ; Statistical physics ; Molecular dynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Camelia Berghian-Groșan | Simona Varvara | Attila Bende | Roxana Bostan
Source: Journal of Molecular Liquids (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

A complex electrochemical, theoretical and surface analysis investigation of copper corrosion inhibition in 3.5 wt% NaCl solution. Case study: SEM images and deep learning for incipient corrosion detection Corrosion ; Oxadiazole ; Copper ; Electrochemistry ; Raman spectroscopy ; Corrosion inhibitor ; Erosion corrosion of copper water tubes Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Junru Zhang | Ying Chen | Junfei Liu | Wanwen Li | Liyong Niu | Qiguang Zhu
Source: Talanta (article, 2025-11-29)
Field: Chemistry — Analytical Chemistry

High-selectivity phenol detection in cumene process wastewater via bromination and dynamic optical path Detection limit ; Phenol ; Halogenation ; Sensitivity (control systems) ; Wastewater ; Optical path ; Process (computing) ; Chassis Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Oil Spill Detection and Mitigation
Authors: Mathieu Calvat | Chris Bean | Dhruv Anjaria | Haoren Wang | Kenneth Vecchio | J.C. Stinville
Source: Scripta Materialia (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Plasticity encoding and mapping during elementary loading for accelerated mechanical properties prediction Encoding metal plasticity captured from high-resolution digital image correlation (HR-DIC) is leveraged to predict a wide range of monotonic and cyclic macroscopic properties of metallic materials. To capture the spatial heterogeneity of plasticity that develops in metals, latent space features describing plasticity of small regions are spatially mapped across large fields. These latent space feature maps capture the complexity and heterogeneity of metal plasticity as a low-dimensional representation. These feature maps are then used to train a convolutional neural network-based model to predict monotonic and cyclic macroscopic properties. The approach is demonstrated on a large set of face-centered cubic metals, enabling rapid and accurate property prediction. Plasticity ; Feature (linguistics) ; Monotonic function ; Range (aeronautics) ; Encoding (memory) ; Property (philosophy) ; Space (punctuation) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Ruan Zhang | Shuai Liu | Xianyu Hu | Yong Zhang | Yunlei Zhong | Qing Guo | Xiyuan Feng
Source: The Journal of Physical Chemistry Letters (article, 2025-12-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Space-Confined-Grown 10 nm 2D β-Ga <sub>2</sub> O <sub>3</sub> Nanoflakes for Electrode-Scalable Ultraviolet Photodetector and Neuromorphic Computing In the post-Moore era, next-generation optoelectronics and neuromorphic computing systems require ultrawide bandgap semiconductors with tunable properties. β-Ga<sub>2</sub>O<sub>3</sub> stands as a promising candidate, possessing a bandgap of 4.5-4.9 eV and a high critical electric field of ∼ 8 MV/cm. However, its practical application is hindered by difficulties in synthesizing high-quality 2D forms and optimizing metal-semiconductor contacts. In this study, highly crystalline ultrathin 2D β-Ga<sub>2</sub>O<sub>3</sub> nanoflakes with improved interface quality and enhanced carrier transport dynamics were synthesized using a space-confined CVD approach. The nanoflakes exhibit excellent structural and crystalline quality, as evidenced by the characteristic peaks in Raman spectroscopy and XRD, along with the clear lattice structure observed via TEM. Metal-semiconductor-metal devices with Ti-Ti, Pd-Pd, and Ti-Pd electrode configurations were fabricated and evaluated through current-voltage and time-resolved current-time measurements. The results reveal that the Schottky barrier height at the metal-Ga<sub>2</sub>O<sub>3</sub> interface plays a decisive role in device performance. Notably, the Ti-Ti device exhibited a distinctive two-stage photocurrent decay after light illumination ceased, attributed to defect-mediated trapping and release of photogenerated carriers. By incorporating this unique photoresponse behavior into a deep neural network, promising image recognition accuracy was achieved on the Modified National Institute of Standards and Technology data set. This work not only offers a reliable pathway for synthesizing high-quality 2D β-Ga<sub>2</sub>O<sub>3</sub> but also demonstrates its potential for application in high-performance optoelectronics and neuromorphic computing. Neuromorphic engineering ; Photocurrent ; Photodetector ; Schottky barrier ; Raman spectroscopy ; Band gap ; Schottky diode ; Ultraviolet Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; GaN-based semiconductor devices and materials
Authors: Adam Barsotti | Andrew C. Lee | Margaret Morrison | Oscar Paz-Suaznabar | Jiyun Kang | Luis Delfin | Samuel Lee | Molleigh B. Preefer | Johanna Nelson Weker | X. Wendy Gu
Source: International Journal of Hydrogen Energy (article, 2025-11-18)
Field: Materials Science — Metals and Alloys

Strain rate dependent secondary cracking in hydrogen embrittled nickel Necking ; Hydrogen embrittlement ; Strain rate ; Hydrogen ; Brittleness ; Ductility (Earth science) ; Embrittlement ; Cracking ; Grain boundary Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Metal and Thin Film Mechanics
Authors: G. Moskal
Source: Journal of Thermal Analysis and Calorimetry (article, 2025-04-28)
Field: Materials Science — Ceramics and Composites

Selection of low and ultra-low thermal conductivity materials for insulating layers of TBC systems in the context of computational materials science opportunities—a review Context (archaeology) ; Thermal conductivity ; Materials science ; Selection (genetic algorithm) ; Nanotechnology ; Engineering physics ; Thermal ; Material selection ; Composite material ; Engineering ; Computer science ; Physics ; Thermodynamics ; Artificial intelligence Advanced ceramic materials synthesis ; Advanced materials and composites ; High-Temperature Coating Behaviors
Authors: A. Aryanpour | Ali Sadeghi
Source: Physical review. B./Physical review. B (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Machine learning the local electronic density of states Electronic density of states (DOS) plays a crucial role in determining and understanding materials properties. We investigate the machine learnability of additive atomic contributions to electronic DOS, focusing on atom-projected DOS rather than structural DOS. This approach for structure-property mapping is both scalable and transferable, and achieves high prediction accuracy for pure and compound silicon and carbon structures of various sizes and configurations. Furthermore, we demonstrate the generalizability of this model to complex Sn-S-Se compound structures. Utilizing locally trained DOS is shown to significantly enhance the accuracy of predicting material properties, including band energy, Fermi energy, heat capacity, and magnetic susceptibility. Our findings indicate that directly learning atomic DOS, rather than structural DOS, improves the efficiency, accuracy, and interpretability of machine learning in structure-property mapping. This streamlined approach reduces computational complexity, paving the way for examination of electronic structures in materials without the need for computationally expensive ab initio calculations. Local density of states ; Density of states ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: K. A. L. Lima | José A.S. Laranjeira | Nicolas F. Martins | Júlio R. Sambrano | Alexandre C. Dias | Luiz Antônio Ribeiro | Douglas S. Galvão
Source: Nanoscale (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

First-principles and machine learning investigation of the structural and optoelectronic properties of dodecaphenylyne: a novel carbon allotrope We report the computational discovery and characterization of Dodecaphenylyne (DP), a novel carbon allotrope with a unique geometric structure. The structural, dynamic, mechanical, electronic, and optical properties of DP were evaluated using density functional theory (DFT) and a machine learning interatomic potential trained specifically for this material. The formation energy of -7.98 eV per atom indicates high dynamic stability, further supported by the absence of imaginary phonon modes and the preservation of structural integrity up to 1000 K in <i>ab initio</i> molecular dynamics (AIMD) simulations. Both DFT and AIMD simulations were performed within the generalized gradient approximation using the PBE functional. Mechanical analysis reveals high in-plane stiffness with directional dependence: Young's modulus values of 469.09 GPa and 600.41 GPa along the <i>x</i> and <i>y</i> directions, respectively. Electronic band structure and projected density of states analyses confirm the DP semiconducting character. Calculations of carrier mobility using the deformation potential theory reveal pronounced anisotropy, with maximum values reaching up to 30.6 × 10<sup>4</sup> cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> (electrons, e) and 8.4 × 10<sup>4</sup> cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> (holes, h), much higher than the observed for other 2D materials. DP also exhibits anisotropic optical absorption in the visible and ultraviolet spectrum, highlighting its potential for optoelectronic applications. Density functional theory ; Anisotropy ; Phonon ; Deformation (meteorology) ; Atom (system on chip) ; Electronic band structure ; Molecular dynamics ; Electronic structure ; Absorption (acoustics) Boron and Carbon Nanomaterials Research ; Graphene research and applications ; 2D Materials and Applications
Authors: Trishna Barman | Sonya Coleman | Dermot Kerr | Shane Harrigan | Justin Quinn
Source:  (article, 2025-03-17)
Field: Chemistry — Analytical Chemistry

Optimizing Industrial E-Waste Recycling with Attention-Driven Deep Learning for PCB Segmentation Using Hyperspectral Imaging Recently, the generation of electronic waste (E-waste) has increased significantly due to rapid changes in consumer demand and advancements in technology. Recycling E-waste is essential for boosting the economy and advancing the sustainability of the electronics industry. Printed circuit boards (PCBs) contribute significantly to E-waste, as they are widely used in various electronic devices. However, a challenge in recycling E-waste is the rapidly and diversely changing material composition. To enhance the efficacy of E-waste recycling, an automated, non-invasive method is essential for process control and decision-making. By exploiting hyperspectral imaging (HSI), which offers spectroscopic analysis to accurately identify materials, this paper presents attention-based deep learning segmentation models to accurately identify components in PCBs. This approach allows for the automatic extraction of information from E-waste, leading to more efficient and optimized recycling practices. Hyperspectral imaging ; Segmentation ; Deep learning ; Artificial intelligence ; Image segmentation ; Environmental science ; Computer science ; Computer vision ; Remote sensing ; Process engineering ; Engineering ; Geology Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Recycling and Waste Management Techniques
Authors: Tianyu Fang | Lingling Xie | Yaran Niu | Hong Du | Yuankun Zhu | Ziyu Wang | Xuebin Zheng
Source: Ceramics International (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Thermal shock cycle finite element simulation and optimization design of Yb2Si2O7-based high temperature abradable sealing coatings based on machine learning Materials science ; Finite element method ; Thermal shock ; Thermal ; Shock (circulatory) ; Mechanical engineering ; Composite material ; Structural engineering ; Thermodynamics ; Engineering Nuclear Materials and Properties ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Liangwei Liu | Shiqiang Feng | Lili Han
Source: Chemical Research in Chinese Universities (article, 2025-03-17)
Field: Materials Science — Surfaces, Coatings and Films

Chemical-sensitive Electron Tomography for Nanomaterials Nanomaterials ; Electron tomography ; Tomography ; Electron ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Optics ; Nuclear physics ; Transmission electron microscopy Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Copper-based nanomaterials and applications
Authors: Sungwoo Kang | Runlong Cai | Daliao Yang | Dong Jin Ham | Markku Kulmala | John H. Seinfeld | Jingkun Jiang | Hyun Chul Lee
Source: Small Methods (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Efficient Configuration Sampling for Hybrid Functional DFT Calculations to Train Machine‐Learning Potentials: Application to Atmospheric Chemistry Machine-learning potentials (MLPs), trained to predict energies from quantum chemical calculations, are widely employed to conduct large-scale MD simulations. However, MLPs are mostly trained on computationally inexpensive local/semilocal functionals, as generating training datasets using higher-accuracy theories, such as hybrid functionals, is challenging due to their high computational cost. Here, an active transfer learning scheme is developed to efficiently sample configurations for hybrid functional calculations. The proposed method is evaluated on atmospheric secondary aerosol formation reactions: clustering of sulfuric acid (SA), and dimethylamine (DMA), and oxidation of toluene. The accuracy of the trained MLP is shown to be comparable to that of the quantum chemical calculations, with errors within a few meV per atom. The molecular dynamics simulations are executed stably on a nanosecond scale, resulting in the formation of nanometer-size clusters. Thus, this study paves the way for establishing a general protocol to enable high-level atomistic simulations for a wide range of chemical systems. Dimethylamine ; Density functional theory ; Molecular dynamics ; Quantum chemical ; Cluster analysis ; Statistical physics ; Range (aeronautics) ; Stability (learning theory) ; Computer science ; Hybrid functional ; Computational chemistry ; Chemistry ; Physics ; Artificial intelligence ; Materials science ; Machine learning ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Yihang Wang | Gregory A. Voth
Source: The Journal of Chemical Physics (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

Adversarial training for dynamics matching in coarse-grained models Molecular dynamics simulations are essential for studying complex molecular systems, but their high computational cost limits scalability. Coarse-grained (CG) models reduce this cost by simplifying the system, yet traditional approaches often fail to maintain dynamic consistency, compromising their reliability in kinetics-driven processes. Here, we introduce an adversarial training framework that aligns CG trajectory ensembles with all-atom (AA) reference dynamics, ensuring both thermodynamic and kinetic fidelity. Our method adapts the adversarial learning paradigm, combining a physics-based generator with a neural network discriminator that differentiates between AA and CG trajectories. By adversarially optimizing CG parameters, our approach eliminates the need for predefined kinetic features. Applied to liquid water, it accurately reproduces radial and angular distribution functions as well as dynamical mean squared displacement, even extrapolating long-timescale dynamics from short training trajectories. This framework offers a new approach for bottom-up CG modeling, offering a systematic and principled way to preserve dynamic consistency in complex coarse-grained molecular systems. Adversarial system ; Discriminator ; Generator (circuit theory) ; Consistency (knowledge bases) ; Matching (statistics) ; Trajectory ; Reliability (semiconductor) ; Artificial neural network Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: M. Xu | Li-Wen Zhou | Jin Huan Xia | Shenjie Han | Xingying Zhang | Kailong Gu | Zhiqiang Dong | Junfeng Hou
Source: Sustainable materials and technologies (article, 2025-12-04)
Field: Materials Science — Polymers and Plastics

Intelligent prediction of fire retardancy and smoke suppression in waste wood composites with a hybrid deep learning model Smoke ; Raw material ; Composite number ; Adhesive ; Young's modulus ; Engineered wood ; Char ; Lignin Flame retardant materials and properties ; Natural Fiber Reinforced Composites ; Fire dynamics and safety research
Authors: Lukas M. Sigmund | Tina Seifert | Riya Halder | Giulia Bergonzini | Magnus J. Johansson | Per‐Ola Norrby | Kjell Jorner | Mikhail Kabeshov
Source: Angewandte Chemie (article, 2025-09-14)
Field: Chemistry — Organic Chemistry

Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach Abstract The direct thianthrenation of aromatic C─H bonds is a valuable late‐stage functionalization strategy that can assist, for example, the development of new drugs. We herein present a predictive computational model for this reaction, denoted PATTCH, which is based on semiempirical quantum mechanics and machine learning. It classifies each C aromatic –H unit either as reactive or not with an accuracy of above 90%. It can address both the site‐selectivity and reaction feasibility question associated with the thianthrenation protocol. First, this was achieved by selecting carefully engineered features, which take into account the electronic and steric influence on the site‐selectivity. Second, parallel experimentation was used to supplement the available literature data with 54 new negative reactions (unsuccessful thianthrenation), which we show was instrumental for developing the PATTCH tool. Ultimately, we successfully applied the model to a challenging test set encompassing the differentiation between carbocycle versus heterocycle functionalization, the identification of substrates that were reported to result in a mixture of isomeric products, and to molecules that could not be thianthrenated. The computational predictions were experimentally validated. The PATTCH tool can be obtained free of charge from https://github.com/MolecularAI/thianthrenation_prediction . Steric effects ; Selectivity ; Molecule ; Surface modification ; Charge (physics) ; Identification (biology) ; Reaction conditions ; Set (abstract data type) ; Quantum chemical Sulfur-Based Synthesis Techniques ; Chemical Synthesis and Reactions ; Synthesis of heterocyclic compounds