jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seungwoo Hwang | Jiho Lee | Seungwu Han | Youngho Kang | Sungwoo Kang
Source: ArXiv.org (preprint, 2025-09-30)
Field: Materials Science — Materials Chemistry

Discovery of oxide Li-conducting electrolytes in uncharted chemical space via topology-constrained crystal structure prediction Oxide Li-conducting solid-state electrolytes (SSEs) offer excellent chemical and thermal stability but typically exhibit lower ionic conductivity than sulfides and chlorides. This motivates the search for new oxide materials with enhanced conductivity. Crystal structure prediction is a powerful approach for identifying such candidates. However, the structural complexity of oxide SSEs, often involving unit cells with more than 100 atoms, presents significant challenges for conventional methods. In this study, we introduce TOPIC, a structure prediction algorithm that reduces configurational complexity by enforcing corner-sharing (CS) bond topology constraints. We demonstrate that TOPIC successfully reproduces the ground-state and metastable structures of known oxide SSEs, including LiTa$_2$PO$_8$ and Li$_7$La$_3$Zr$_2$O$_{12}$, which contain up to about 200 atoms per unit cell. By combining this approach with a pretrained machine-learning interatomic potential, we systematically screen quaternary oxide compositions and identify 92 promising candidates with CS frameworks. In particular, Li$_4$Hf$_2$Si$_3$O$_{12}$, which corresponds to the ground state at its composition, exhibits an ionic conductivity of 14 mS cm$^{-1}$, a hull energy of 21 meV atom$^{-1}$, and a band gap of 6.5 eV. Through our investigation, we identify the Li ratio as one of the key factors determining the stability of CS structures. Overall, our approach provides a practical and scalable pathway for discovering high-performance oxide solid electrolytes in previously unexplored chemical spaces. Oxide ; Metastability ; Ionic conductivity ; Chemical stability ; Crystal structure ; Ionic bonding ; Crystal structure prediction ; Electrolyte ; Conductivity Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Text and Document Classification Technologies
Authors: Abdullah Al Siam | Touhid Bhuiyan | Md Maruf Hassan
Source:  (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Nanotechnology: Key Methodologies and Their Transformative Impact The integration of artificial intelligence (AI) with nanotechnology is generating remarkable improvements by tackling fundamental difficulties in material design, defect detection, process optimization, and scientific information extraction. This research covers four important AI methodologies—Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and Natural Language Processing (NLP)—emphasizing their strengths, applications, and limitations in nanotechnology. ML excels in predictive modeling and classification, DL boosts imaging and flaw detection, RL optimizes autonomous nanorobotics, and NLP promotes effective information extraction from huge scientific data. Notwithstanding these capabilities, challenges such data reliance, computational complexity, and scalability remain, underscoring the necessity for domain-specific solutions and interdisciplinary collaboration. This paper identifies critical research gaps, offering practical insights for the advancement of AI-driven nanotechnology. The findings underline AI's transformative potential in changing material science, biomedical applications, and nanoscale engineering, paving the path for creative solutions at the molecular and atomic levels. Transformative learning ; Key (lock) ; Process (computing) ; Big data ; Scalability ; Information extraction ; Applications of artificial intelligence Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Nanotechnology research and applications
Authors: Hassan Gashmard | H. Shakeripour | Mojtaba Alaei
Source: ArXiv.org (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

AI-Driven Discovery of High-Temperature Superconductors via Materials Genome Initiative and High-Throughput Screening Inspired by nature, this study employs the Materials Genome Initiative to identify key components of HTSC superconductors. Integrating AI with high-throughput screening, we uncover crucial superconducting "genes". Through HTS techniques and advanced machine learning models, we demonstrate that Functional Convolutional Neural Networks (CNNs) ensure accurate extrapolation of potential compounds. Leveraging extensive datasets from the ICSD, the Materials Project and COD, our implemented HTS pipeline classifies superconductors, with CNN and long short-term memory (LSTM) models predicting Tc and their foundational elements. We address the scarcity of non-superconducting material data by compiling a dataset of 53,196 non-superconducting materials (DataG Non-Sc) and introduce a novel neural network architecture using Functional API for improved prediction, offering a powerful tool for future superconductor discovery. Our findings underscore the transformative potential of combining HTS with AI-driven models in advancing HTSC materials, highlighting Pu and H elements (with Tc nearly 100 K) as significant predictors of high-temperature superconductivity, suggesting their role as a crucial gene in these materials. Pipeline (software) ; Convolutional neural network ; Deep learning ; Transformative learning ; Extrapolation ; Key (lock) Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Physics of Superconductivity and Magnetism
Authors: Tanjin He | Aikaterini Vriza | Ian Foster | Rajeev S. Assary | Logan Ward | Maria K. Y. Chan
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Harnessing Multimodal Data from Scientific Literature for Spectroscopy Informatics Spectroscopy techniques, such as X-ray absorption spectroscopy (XAS), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), are powerful tools for structural characterization and compositional analysis of materials. During spectroscopic analysis, experimental spectra are often evaluated by comparing them to previously measured or computed reference spectra. A comprehensive spectroscopy database can reduce the probability of misinterpretation and facilitate the analysis of spectra for new materials. To construct a large-scale spectroscopy database across various chemical systems and structures, we propose a multimodal learning approach to unlock the vast amount of experimental spectroscopy data embedded in materials science literature. By developing an agentic workflow that orchestrates different machine learning models, including optical character recognition (OCR), object detection, instance segmentation, and textual information retrieval, we are able to automatically convert unstructured spectroscopy data from scientific figures and text into structured numerical data and metadata that can serve as references in new spectroscopy experiments. To demonstrate the capability of our workflow, we have extracted and digitized hundreds of XAS curves relevant to battery materials. This growing database will accelerate the discovery of advanced materials by allowing for rapid determination of their crystallography, elemental composition, ionic oxidation states, and local atomic environments. Furthermore, it lays the foundation for developing high-efficiency machine learning algorithms that enable real-time spectra matching and autonomous materials characterization. Workflow ; Metadata ; Spectroscopy ; X-ray absorption spectroscopy ; Electron spectroscopy ; Characterization (materials science) ; Construct (python library) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Zhihong Hu | Zongyu Huang | Bin‐Bin He | Siwei Luo | Xiang Qi
Source: Journal of Materials Science (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Machine learning-assisted shape prediction for two-dimensional transition metal dichalcogenides growth Chemical vapor deposition ; Process (computing) ; Support vector machine ; Transition metal ; Evaporation ; Boosting (machine learning) ; Process control ; Gradient boosting 2D Materials and Applications ; Machine Learning in Materials Science ; Nanoplatforms for cancer theranostics
Authors: Tianlu Zheng | Chaocan Cai | Fan Yang | Rongguo Wang | Wei Liu
Source: Polymers (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural-Network-Based Generation of Composite Representative Volume Elements with Non-Uniform Distribution and High-Volume Fractions To reduce the reliance on large training sets for representative volume element (RVE) generation using machine learning, this work presents a novel approach based on physics-informed neural network (PINN) to generate RVEs for unidirectional fiber-reinforced composites with non-uniform fiber distributions and high-volume fractions. The method embeds physical constraints including fiber non-overlap, volume fraction, and boundary conditions directly into the neural network's loss function. This integration eliminates the need for large training datasets, which is typically required by traditional machine learning methods. Moreover, it achieves volume fractions exceeding 0.8, surpassing the jamming limit of conventional generation techniques. Exhaustive statistical measurements taken at different scales confirm that the proposed method could accurately reproduce local fiber distribution patterns in realistic microstructures while maintaining complete randomness at larger scales. Finite element analysis was employed on the generated RVEs to predict the elastic properties and damage behavior that taking into account the interfacial debonding and nonlinear damage in matrix. The predictions of both macroscopic mechanical properties (elastic properties and strength) and microscopic damage patterns show good agreement with experimental results. The proposed PINN-based framework provides an efficient and reliable tool for computational micromechanics of polymer matrix composites. Micromechanics ; Randomness ; Representative elementary volume ; Artificial neural network ; Finite element method ; Fiber ; Matrix (chemical analysis) ; Work (physics) ; Volume (thermodynamics) ; Limit (mathematics) Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Huimin Lu | Lei Zhang | Jingyan Jiang | Jian Song | Zhongchao Zhou | Wujian Wu | Ziqian Cheng | Tengfei Yan | Hong Hu | Tingting Zhao | Zhi Xu | Siyi Luo | Hui Li | Jianhua Zhang | Charles H. Lawrie
Source: Advanced Materials (article, 2025-07-01)
Field: Materials Science — Polymers and Plastics

Pressure Induced Molecular‐Arrangement and Charge‐Density Perturbance in Doped Polymer for Intelligent Motion and Vocal Recognitions (Adv. Mater. 27/2025) Pressure Sensors The cover image illustrates how side-chain-doped polymers undergo molecular rearrangement and charge-density perturbation under pressure. This pressure-induced phenomenon enhances carrier lifetime, leading to improved electrical conductivity in the material. The sensor is further integrated with convolutional neural networks to create an intelligent vocal recognition system, achieving over 96% classification accuracy across diverse vocal profiles. More details can be found in article number 2500077 by Jian Song, Hui Li, Jianhua Zhang, Charles H. Lawrie, and co-workers. Materials science ; Doping ; Charge (physics) ; Polymer ; Optoelectronics ; Nanotechnology ; Composite material ; Physics Conducting polymers and applications ; Analytical Chemistry and Sensors ; Advanced Sensor and Energy Harvesting Materials
Authors: Steffen Jeschke (4630342) | Karen Wilson (459204) | Adam F. Lee (1360698)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Accelerating ComputationalModeling of Reactant Adsorptionthrough a Combined MACE+DFT Approach: Furfural on Cu Surfaces Machine learning has great potential to accelerate computational discovery of new materials and catalytic reactions but is challenging to implement with quantum chemical accuracy for diverse chemical systems. Here, we explore the pretrained foundation model MACE-MP-0, a machine-learned interatomic potential (MLIP) using the MACE architecture, as a low-cost computational method for structure relaxation prior to density functional theory (DFT) computations of furfural (FUR) adsorption over single-crystal copper surfaces. A data set of 240 adsorption configurations of FUR, including both <i>trans</i>- and <i>cis</i>-FUR rotamers, was created using an automated workflow to randomly orientate the bioderived platform chemical at heuristic adsorption sites on Cu(111), (100), and (110) surfaces. The performances of DFT alone versus a MACE+DFT approach were compared, with common local minima geometries identified through structure relaxation followed by hierarchical agglomerative clustering using the smooth overlap of atomic positions (SOAPs) as a descriptor of the chemical environment. New adsorption configurations were discovered for FUR on Cu(110). Combining MACE-MP-0 prerelaxation with agglomerative clustering to preselect energetically stable configurations for subsequent DFT relaxation eliminated human bias in the starting geometries and reduced the computational cost by 75%. Density functional theory ; Adsorption ; Maxima and minima ; Furfural ; Relaxation (psychology) ; Cluster analysis ; Molecular dynamics ; Workflow Machine Learning in Materials Science ; Catalysis for Biomass Conversion ; Surface Chemistry and Catalysis
Authors: Kwangnam Kim (7512017) | Matthew P. Kroonblawd (4733622) | Nir Goldman (2725504)
Source: Figshare (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Similarity Metricfor Data Optimization and EfficientTraining of Reactive Machine Learning Force Fields for HydrocarbonRadiolysis Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. Our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments. Radiolysis ; Metric (unit) ; Set (abstract data type) ; Similarity (geometry) ; Field (mathematics) ; Chebyshev filter ; Force field (fiction) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Peng Liu (120506) | Bo Dong (218172) | Donghai Mei (1272966)
Source: Figshare (article, 2025-09-16)
Field: Chemistry — Inorganic Chemistry

MethylnaphthaleneIsomerization and Diffusion Behaviorover H‑BEA Zeolites Using Molecular Dynamics Simulations withMachine Learning Potentials The monomolecular isomerization of methylnaphthalene, which is crucial for understanding alkyl aromatic ring reactions in the petrochemical industry, has been studied using ab initio and machine learning-based molecular dynamics simulations. The diffusional free energy landscapes suggest that the free energy barriers for methylnaphthalene translocation between straight channels are comparable when either both channels share a Brønsted acid site or neither possesses one. Furthermore, ab initio molecular dynamics simulations coupled with metadynamics provided detailed free energy landscapes for the monomolecular isomerization reaction network. The methyl transfer process is identified as the most feasible pathway, representing the rate-limiting step with a maximum free energy barrier of 124 kJ/mol. Interestingly, the more stable Wheland intermediate does not participate in the predominant reaction pathway. Utilizing eXtreme Gradient Boosting and Shapley Additive exPlanations, a strong correlation between the charges of molecular moieties and free energy barriers has been established. The present study provides significant insights into the mechanisms underlying the isomerization reactions of polyaromatics, which are pivotal in the development of efficient zeolite catalysts for petrochemical processes. Isomerization ; Metadynamics ; Molecular dynamics ; Ab initio ; Petrochemical ; Energy landscape ; Ab initio quantum chemistry methods ; Energy profile Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Liang Zhang | Kong Chen | Wu, Yuen
Source: ArXiv.org (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Universal crystal material property prediction via multi-view geometric fusion in graph transformers Accurately and comprehensively representing crystal structures is critical for advancing machine learning in large-scale crystal materials simulations, however, effectively capturing and leveraging the intricate geometric and topological characteristics of crystal structures remains a core, long-standing challenge for most existing methods in crystal property prediction. Here, we propose MGT, a multi-view graph transformer framework that synergistically fuses SE3 invariant and SO3 equivariant graph representations, which respectively captures rotation-translation invariance and rotation equivariance in crystal geometries. To strategically incorporate these complementary geometric representations, we employ a lightweight mixture of experts router in MGT to adaptively adjust the weight assigned to SE3 and SO3 embeddings based on the specific target task. Compared with previous state-of-the-art models, MGT reduces the mean absolute error by up to 21% on crystal property prediction tasks through multi-task self-supervised pretraining. Ablation experiments and interpretable investigations confirm the effectiveness of each technique implemented in our framework. Additionally, in transfer learning scenarios including crystal catalyst adsorption energy and hybrid perovskite bandgap prediction, MGT achieves performance improvements of up to 58% over existing baselines, demonstrating domain-agnostic scalability across diverse application domains. As evidenced by the above series of studies, we believe that MGT can serve as useful model for crystal material property prediction, providing a valuable tool for the discovery of novel materials. Crystal (programming language) ; Scalability ; Topology (electrical circuits) ; Property (philosophy) ; Graph ; Equivariant map ; Granularity ; Invariant (physics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Graph Neural Networks
Authors: Ahmed Nazmus Sakib | Md Monjur Hossain Bhuiyan | Alfredo Becerril Corral | Monsur Chowdhury | Zahed Siddique
Source: Advances in Polymer Technology (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Comprehensive Evaluation of NBR Sealing Performance Under Medium‐Pressure Hydrogen Aging: Experimental Analysis and Predictive Model Using Convolutional Neural Network The study investigates the sealing performance of nitrile butadiene rubber (NBR) seals in hydrogen facilities, with a focus on safety and reliability through extensive experimental analysis and accelerated aging simulations. This research involved a range of characterization tests, including tensile strength, swelling, hardness, and resistivity measurements, as well as scanning electron microscopy (SEM) morphological examinations, to assess material degradation under hydrogen pressures up to 5000 psi at room temperature. The results revealed significant changes in mechanical and physical properties, including reductions in tensile strength and hardness, increased resistivity, and material swelling, all of which were attributed to hydrogen‐induced structural degradation. Morphological analysis via SEM highlighted surface roughening and microcrack formation at higher pressures. A Multivariate regression model was developed to predict key properties such as tensile strength, resistivity, weight, and hardness as functions of hydrogen pressure. Additionally, a hybrid machine learning model incorporating convolutional neural networks (CNNs) for feature extraction with fully connected layers for regression was also developed, demonstrating strong predictive performance for NBR’s material properties using both numerical features and SEM images. These findings contribute to the development of safer and more reliable sealing materials, crucial for the hydrogen energy industry, ensuring that seals can withstand the unique challenges posed by hydrogen storage and transportation. Ultimate tensile strength ; Convolutional neural network ; Scanning electron microscope ; Hydrogen ; Characterization (materials science) ; Natural rubber ; Hydrogen storage ; Artificial neural network ; Reliability (semiconductor) Silicone and Siloxane Chemistry ; Tribology and Lubrication Engineering ; Corrosion Behavior and Inhibition