jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cody Bezik | Muhammad Aftab Akram | Blake Bowser | Kaitlin N. Detwiler | Ashley Hilmas | Ganesh Balasubramanian | Vikas Varshney
Source: Ceramics International (article, 2025-05-24)
Field: Materials Science — Ceramics and Composites

Predicting ultimate tensile strength of SiC/SiC mini-composites via machine learning Materials science ; Ultimate tensile strength ; Composite material ; Silicon carbide Advanced ceramic materials synthesis ; Advanced Surface Polishing Techniques ; Advanced machining processes and optimization
Authors: Pan Wang | Qingshan Liu | Donghai Xu | Guanyu Jiang | Wenshan Peng | Feng Liu | Jian Hou
Source: International Journal of Heat and Mass Transfer (article, 2025-11-29)
Field: Materials Science — Biomaterials

Research progress on the deposition mechanisms and mitigation of particulate and crystallization fouling in heat exchangers Fouling ; Crystallization ; Heat exchanger ; Nucleation ; Supersaturation ; Deposition (geology) ; Particulates ; Heat transfer Calcium Carbonate Crystallization and Inhibition ; Membrane Separation Technologies ; Freezing and Crystallization Processes
Authors: Upadesh Subedi | Nele Moelans | Tomasz Tański | Anil Kunwar
Source: Engineering With Computers (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Foretelling microstructural interface with multi-generational convolutional-LSTM framework Interface (matter) ; Computer science ; Artificial intelligence ; Theoretical computer science ; Parallel computing Machine Learning in Materials Science ; Injection Molding Process and Properties ; Model Reduction and Neural Networks
Authors: Gerardo Valadez Huerta | Kaoru Hisama | Katsutoshi Sato | Katsutoshi Nagaoka | Michihisa Koyama
Source: iScience (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Surface steric effect in heterogeneous catalysis as the origin of the high activity induced by strong metal-support interactions Supported nanoparticles offer unique opportunities for enhancing catalytic activity via strong metal-support interaction (SMSI). Even with state-of-the-art experimental techniques, the atomistic origin of this enhancement remains unclear, while current computational limitations make it difficult to provide a theoretical explanation. This study focused on clarifying the atomistic mechanism of SMSI by investigating N<sub>2</sub> dissociation from Ru/La<sub>0.5</sub>Ce<sub>0.5</sub>O<sub>1.75-x</sub> catalysts. Fast calculations using a neural network potential enabled the analysis of 328 complex nanoparticle models with varying degrees of site heterogeneity, encompassing over 25,768 adsorption sites. Our findings were validated against infrared spectra and helped identify catalyst configurations with enhanced catalytic activity, driven by SMSI. Specifically, the dissociation path of N<sub>2</sub> molecules sandwiched between decoration cations on a nanoparticle near the support exhibited a low activation barrier. Our theoretical approach represents a major advancement in bridging the gap between simulation and empirical data and in our understanding of complex supported nanoparticle catalysts. Steric effects ; Catalysis ; Heterogeneous catalysis ; Chemistry ; Metal ; Nanotechnology ; Chemical engineering ; Materials science ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Fei Zhan | Haodong Yao | Zhi Geng | Lirong Zheng | Can Yu | Xue Han | Xiaoqing Song | Shuguang Chen | Haifeng Zhao
Source: The Journal of Physical Chemistry A (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

A Graph Neural Network-Based Approach to XANES Data Analysis The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction. It turns out to be faster than the traditional XANES fitting method when the simulation approach and XANES optimization algorithm are combined to fit the 3D structure of the given system. The geometric features of the system are included in the weighted message passing block of XAS3Dabs and their importance is investigated. XAS3Dabs model demonstrates superior accuracy in XANES prediction compared to most machine learning models. By extracting graphs constituted by edges related to the absorbing atom, our model reduces redundant information, thereby not only enhancing the model's performance but also improving its robustness across different hyperparameters. XAS3Dabs model can be generalized to simulate the spectra for the systems with the absorber having the designed absorption edge so as to meet the expectations of online data processing. The method is expected to be the key part of the online 3D structure analysis framework for the XAS-related beamlines of high-energy photon source (HEPS) now under construction. XANES ; Computer science ; Power graph analysis ; Artificial neural network ; Graph ; Artificial intelligence ; Theoretical computer science ; Physics ; Spectroscopy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Fault Detection and Control Systems
Authors: Luis H. Delgado-Granados | LeeAnn M. Sager-Smith | Kristina Trifonova | David A. Mazziotti
Source: The Journal of Physical Chemistry Letters (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Machine Learning of Two-Electron Reduced Density Matrices for Many-Body Problems We present a novel machine learning algorithm for the many-electron problem, predicting the convex combination of two-electron reduced density matrices (2-RDMs)─obtained from upper- and lower-bound energy calculations─that closely approximates the exact energy. In contrast to other recently developed approaches based on the wave function or one-electron density, our 2-RDM machine-learning approach predicts energies and properties without steep scaling or functional approximation. As conjectured by Preskill and co-workers, a small amount of data in a physics-based machine learning algorithm─in this case, information about the RDMs and their violation of selected higher-order <i>N</i>-representability conditions─yields highly accurate electronic energies that capture both dynamic and static correlation. We demonstrate the method by predicting the potential energy curves for BH and N<sub>2</sub> within a few millihartrees of results from exact diagonalization. This machine learning algorithm provides a general framework for improving electronic structure calculations, with the potential for wide-reaching applications to both moderately and strongly correlated molecular systems. Electron ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Zhuo Chen | Yuejin Yuan | Yanzhou Wang | Penghua Ying | Shouhang Li | Cheng Shao | Wenyang Ding | Gang Zhang | Meng An
Source: Advanced Electronic Materials (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Softening of Vibrational Modes and Anharmonicity Induced Thermal Conductivity Reduction in a‐Si:H at High Temperatures Abstract Hydrogenated amorphous silicon (a‐Si:H) has garnered considerable attention in the semiconductor industry, particularly for its use in solar cells and passivation layers for high‐performance silicon solar cells, owing to its exceptional photoelectric properties and scalable manufacturing processes. A comprehensive understanding of thermal transport mechanism in a‐Si:H is essential for optimizing thermal management and ensuring the reliable operation of these devices. In this study, we developed a neuroevolution machine learning potential based on first‐principles calculations of energy, forces, and virial, which enables accurate modeling of interatomic interaction in both a‐Si:H and a‐Si systems. Using the homogeneous nonequilibrium molecular dynamics (HNEMD) method, this study systematically investigated the thermal conductivity of a‐Si:H and a‐Si across a temperature range of 300–1000 K and hydrogen concentrations ranging from 6 to 12 at%. The simulation results showed that thermal conductivity of a‐Si:H with 12 at% hydrogen is significantly reduced by 41.2% compared to that of a‐Si at 300 K. The spectral thermal conductivity, vibrational density of states and lifetimes of vibrational modes are analyzed, which revealed both the softening of vibrational modes and anharmonicity effects contribute to the reduction of thermal conductivity as temperature and hydrogen concentration increase. Furthermore, the influence of hydrogen concentration and temperature on diffuson and propagon contribution to thermal conductivity of a‐Si:H is quantatively revealed. This study provides valuable insight for developing thermal management strategies in silicon‐based semiconducting devices and advancing the understanding of thermal transport in amorphous systems. Anharmonicity ; Materials science ; Softening ; Thermal conductivity ; Reduction (mathematics) ; Composite material ; Thermal ; Conductivity ; Molecular vibration ; Condensed matter physics ; Thermodynamics ; Physical chemistry ; Molecule ; Organic chemistry Thermal properties of materials ; Silicon and Solar Cell Technologies ; Semiconductor materials and interfaces
Authors: Chen Shen | Siamak Attarian | Yixuan Zhang | Hongbin Zhang | Mark Asta | Izabela Szlufarska | Dane Morgan
Source: Research Square (preprint, 2025-01-15)
Field: Materials Science — Materials Chemistry

SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System Molten salt ; Transferability ; Work (physics) ; Workflow ; Computer science ; Artificial neural network ; Field (mathematics) ; Thermodynamics ; Artificial intelligence ; Physics ; Machine learning ; Mathematics Machine Learning in Materials Science
Authors: Liang-Hao Huang | Wen Ni | Yaru Jia | Minqing Zhu | Tiantian Yang | Mingchao Yu | Jiaxiang Zhang
Source: Pharmaceutics (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Process Development for the Continuous Manufacturing of Carbamazepine-Nicotinamide Co-Crystals Utilizing Hot-Melt Extrusion Technology <b>Objectives:</b> Hot-melt extrusion (HME) offers a solvent-free, scalable approach for manufacturing pharmaceutical co-crystals (CCs), aligning with the industry's shift to continuous manufacturing (CM). However, challenges like undefined yield optimization, insufficient risk management, and limited process analytical technology (PAT) integration hinder its industrial application. This study aimed to develop a proof-of-concept HME platform for CCs, assess process risks, and evaluate PAT-enabled monitoring to facilitate robust production. <b>Methods:</b> Using carbamazepine (CBZ) and nicotinamide (NIC) as model compounds, an HME platform compatible with PAT tools was established. A systematic risk assessment identified five key risk domains: materials, machinery, measurement, methods, and other factors. A Box-Behnken design of experiments (DoE) evaluated the impact of screw speed, temperature, and mixing sections on CC quality. Near-infrared (NIR) spectroscopy monitored CBZ-NIC co-crystal formation in real time during HME process. <b>Results:</b> DoE revealed temperature and number of mixing sections significantly influenced particle size (D<sub>50</sub>: 2.0-4.0 μm), while screw speed affected efficiency. NIR spectroscopy detected a unique CC absorption peak at 5008.3 cm⁻¹, enabling real-time structural monitoring with high accuracy (R² = 0.9999). Risk assessment highlighted material attributes, process parameters, and equipment design as critical factors affecting CC formation. All experimental batches yielded ≥ 94% pure CCs with no residual starting materials, demonstrating process reproducibility and robustness. <b>Conclusions:</b> Overall, this work successfully established a continuous hot-melt extrusion (HME) process for manufacturing CBZ-NIC co-crystals, offering critical insights into material, equipment, and process parameters while implementing robust in-line NIR monitoring for real-time quality control. Additionally, this work provides interpretable insights and serves as a basis for future machine learning (ML)-driven studies. Extrusion ; Carbamazepine ; Nicotinamide ; Process development ; Process (computing) ; Materials science ; Process engineering ; Chemistry ; Metallurgy ; Computer science ; Engineering ; Medicine ; Organic chemistry ; Epilepsy Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Fan Zhang | Li Fu | Weiwei Gao | Peihong Zhang | Jijun Zhao
Source: npj Computational Materials (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information Vacancy-ordered double perovskites (VODPs) are promising alternatives to three-dimensional lead halide perovskites for optoelectronic applications. Mixing these materials creates a vast compositional space for tunable properties but complicates efficient screening of target candidates. Here, we illustrate the diverse electronic and optical characteristics as well as the nonlinear mixing effects within mixed VODPs. Furthermore, inspired by the observation that all physical properties of mixed systems with limited local environment options can be uniquely determined by the information regarding atomic-site occupation, we developed a method combining data augmentation and a transformer-inspired graph neural network to effectively encodes atomic-site information in mixed systems. This approach accurately predicts band gaps and formation energies for mixed VODPs, achieving Root Mean Square Errors of 21 meV and 3.9 meV/atom, respectively. Trained with samples with up-to three mixed elements and small supercells (<72 atoms), the model not only can be generalized to medium- and high-entropy systems and larger supercells (>200 atoms), but also well reproduces the bandgap bowing effect in Sn-based mixed VODPs. Vacancy defect ; Space (punctuation) ; Materials science ; Crystallography ; Computer science ; Chemistry Machine Learning in Materials Science ; Transition Metal Oxide Nanomaterials ; Perovskite Materials and Applications
Authors: Tong Wu | Dongli Yan | Sheng Lin | Ran Zhang | Yuhan Wang | Min Li | Shengzhu Yu | Xiaoyan Ma | Zhenya Chen | Yi‐Xin Huo
Source: Journal of Advanced Research (article, 2025-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

Design of strictly orthogonal biosensors for maximizing renewable biofuel overproduction By demonstrating the dominant role of the BSH counts in TF-SM interactions and establishing a machine learning-guided framework for TF evolution, this work advances rational design principles for engineering TFs with precise molecular recognition, offering broad applications in synthetic biology and metabolic engineering. Overproduction ; Synthetic biology ; Rational design ; Work (physics) ; Renewable energy ; Production (economics) thermodynamics and calorimetric analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Microbial Metabolic Engineering and Bioproduction
Authors: Sundarrajan Priya. | Rishabha Malviya | Saurabh Srivastava | Ching Siang Tan | Abdullah A. Aseeri
Source: Colloids and Interface Science Communications (article, 2025-11-21)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired nanostructured surfaces for antimicrobial and antifouling applications Microbial biofouling threatens healthcare, water treatment and food systems by promoting resilient biofilms, reducing device lifetime and increasing infection risk. Bioinspired nano- and micro-structured surfaces (NMSS) offer non-leaching, physics-driven antifouling and mechano-bactericidal strategies that aim to reduce reliance on toxic biocides. This review evaluates advances in bioinspired surface engineering, emphasizing the comparative performance, material trade-offs, and long-term stability of antifouling and antimicrobial strategies. NMSS reduces the real contact area, increases local shear, and, for dense high-aspect-ratio features, imposes membrane deformation that can cause lysis; bactericidal and antifouling efficacy depends on pillar height, tip radius, spacing, substrate stiffness, and wettability. Multiscale models link nanoscale membrane stresses to mesoscale transport and critical shear thresholds, allowing geometry selection tailored to flow regimes and microbial types. Hybrid strategies that combine topography with benign chemistries (zwitterions, photocatalysts) and stimuli-responsive actuation enhance robustness under protein conditioning and mixed-species biofilms while reducing ecological load. In conclusion, the combination of multiscale physics, machine-learning optimisation, and sensor-integrated maintenance, along with bioinspired NMSS, can deliver scalable, lower-impact antifouling solutions. • Nanopillars kill microbes through geometry-dependent stress. • Bactericidal action can be considered to have several synergistic pathways. • The topography-chemistry hybrid enhances the antifouling. • Computational models guide the design strategies for nanoscale systems. • Adaptive antifouling is possible using smart responsive surfaces. Biofouling ; Nanopillar ; Biofilm ; Nanoscopic scale ; Robustness (evolution) ; Pillar Polymer Surface Interaction Studies ; Bacterial biofilms and quorum sensing ; Marine Biology and Environmental Chemistry
Authors: Max Geier | Khachatur G. Nazaryan | Timothy Zaklama | Liang Fu
Source: arXiv (Cornell University) (preprint, 2025-02-07)
Field: Materials Science — Surfaces, Coatings and Films

Is attention all you need to solve the correlated electron problem? The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter self-attention neural network can be used to solve the interacting electron problem in solids. By a systematic neural-network variational Monte Carlo study on a moiré quantum material, we demonstrate that the self-attention ansatz provides an accurate and efficient solution without human bias. Moreover, our numerical study finds that the required number of variational parameters scales roughly as $N^2$ with the number of electrons, which opens a path towards efficient large-scale simulations. Electron ; Computer science ; Psychology ; Physics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques
Authors: Yifan Li | Xiuying Zhang | Mingkang Liu | Lei Shen
Source: Journal of Materials Informatics (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

A critical review of machine learning interatomic potentials and Hamiltonian Machine learning interatomic potentials (ML-IAPs) and machine learning Hamiltonian (ML-Ham) have revolutionized atomistic and electronic structure simulations by offering near ab initio accuracy across extended time and length scales. In this Review, we summarize recent progress in these two fields, with emphasis on algorithmic and architectural innovations, geometric equivariance, data efficiency strategies, model-data co-design, and interpretable AI techniques. In addition, we discuss key challenges, including data fidelity, model generalizability, computational scalability, and explainability. Finally, we outline promising future directions, such as active learning, multi-fidelity frameworks, scalable message-passing architectures, and methods for enhancing interpretability, which is particularly crucial for the field of AI for Science (AI4S). The integration of these advances is expected to accelerate materials discovery and provide deeper mechanistic insights into complex material and physical systems. Scalability ; Interpretability ; Computer science ; Generalizability theory ; Fidelity ; Machine learning ; Artificial intelligence ; High fidelity ; Ab initio ; Theoretical computer science ; Computational science ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Rongrong Jin | Hui Yang | Chaonan Cui | James P. Lewis | Pengju Ren | Xiaodong Wen | Zhou‐jun Wang
Source: Journal of Catalysis (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Revealing H2 dissociation for selective CO2 hydrogenation on group IIIA metal oxides via an adequate sampling algorithm Chemistry ; Catalysis ; Dissociation (chemistry) ; Metal ; Group (periodic table) ; Sampling (signal processing) ; Inorganic chemistry ; Combinatorial chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Bowen Kan | Yumeng Zhou | Daiyou Xie | Pengyu Zhou | Yunquan Zhang | Honghui Shang
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

NNQS-SCI: Tackling Trillion-Dimensional Hilbert Space with Adaptive Neural Network Quantum States Neural Network Quantum States (NNQS) offer a powerful variational Monte Carlo (VMC) approach for quantum many-body problems, balancing polynomial scaling with high expressive power. However, scaling NNQS to large chemical systems faces challenges in preserving accuracy with exact energy and managing vast configurations efficiently. In this work, we introduce NNQS-SCI, a high-performance Selected Configuration Interaction (SCI) based NNQS method designed to overcome these limitations. NNQS-SCI employs highly parallelized Slater-Condon rules for fast local energy evaluations, avoiding accuracy loss, while its adaptive SCI engine dynamically manages billions of configurations without space explosion or arbitrary cutoffs that plague other NNQS-CI approaches. Optimized for extreme scalability via multi-level parallelism and memory compression, NNQS-SCI successfully simulates systems up to 152 spin orbitals, tackling Hilbert space dimensions exceeding 1014 and demonstrating significant advances in scale and efficiency. NNQS-SCI thus provides a robust and scalable path towards high-accuracy quantum chemistry on high-performance computing platforms. Scalability ; Hilbert space ; Scaling ; Quantum ; Quantum computer ; Artificial neural network ; Polynomial ; Energy (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Gabriel Gemadzie | Peter McPike | Aliaksei Boika
Source: Current Opinion in Electrochemistry (article, 2025-06-16)
Field: Chemistry — Electrochemistry

Harnessing instrumentation and artificial intelligence for new insights and applications in single-entity electrochemistry Instrumentation (computer programming) ; Nanotechnology ; Computer science ; Systems engineering ; Materials science ; Biochemical engineering ; Engineering Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Analytical Chemistry and Sensors
Authors: Yuhang Liu | Kai Huang | Kun Gao | Yan Wang | Zhanguang Chen | Shangyang Yu | Li Zhang | Liang Yin | Cheng Lei | Licheng Guo
Source: International Journal of Fatigue (article, 2025-06-13)
Field: Materials Science — Polymers and Plastics

A method to predict stiffness evolution of 3D woven composites based on acoustic emission and deep learning Acoustic emission ; Composite material ; Materials science ; Stiffness ; Woven fabric Textile materials and evaluations ; Mechanical Behavior of Composites ; Material Properties and Processing