jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuhao Zhang (3804106) | Michael Chigaev (6486260) | Olexandr Isayev (1352004) | Richard A. Messerly (3113907) | Nicholas Lubbers (5436017)
Source: Figshare (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Including Physics-InformedAtomization Constraintsin Neural Networks for Reactive Chemistry Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural network (NN) MLIP models is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered NN architecture to account for the energies of isolated atoms. The result produces a consistent prediction of the atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture that we call hierarchically interacting particle neural network (HIP-NN)-AE, an AE-constrained version of the HIP-NN, as well as ANI-AE, the AE-constrained version of the accurate NN engine for molecular energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks. Artificial neural network ; Dissociation (chemistry) ; Consistency (knowledge bases) ; Bond-dissociation energy ; Constraint (computer-aided design) ; Mathematical model ; Deep neural networks Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: María C. Moreno | Brayan Daniel Sarmiento | Adrian J. Gil Rojano | Oscar J. Suarez | Aldo Pardo Garcia
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Graph Neural Network-Based Estimation of Elastic Modulus in Aerospace Polymers Under Variable Environmental Conditions Accurate prediction of the mechanical properties of aerospace polymers is essential for material selection and structural reliability under extreme operational environments. Therefore, this paper presents a framework based on Graph Neural Networks (GNNs) to estimate the elastic modulus of polymer materials subjected to variable thermal and mechanical conditions. Synthetic data sets are generated by replicating chemically valid monomer units into polymer chains, enriched with atomic-level features, and conditioned on temperature and strain-rate input. Each sample is depicted as a graph, with nodes corresponding to atoms and edges representing molecular bonds. The proposed GNN model integrates graph convolutions and global grouping with environmental conditioning to predict the effective elastic response. Evaluation of three aerospace-relevant polymers, polyethylene, nylon 6, and Kevlar, demonstrated high predictive precision, with root mean square errors below 0.11 GPa and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.99. These results highlight the potential of GNNs in soft computing frameworks for intelligent material modeling in aerospace system design. Aerospace ; Graph ; Polymer ; Elastic modulus ; Artificial neural network ; Modulus ; Variable (mathematics) ; Structural health monitoring ; Reliability (semiconductor) Machine Learning in Materials Science ; Polymer crystallization and properties ; Model Reduction and Neural Networks
Authors: Dengale Pravin Balasaheb | Pooja Khatri | Dr. Mrs. S. D. Gunjal | Prof. S. B. Bhosale
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2025-11-08)
Field: Materials Science — Materials Chemistry

A Comprehensive Review on AI-Based Material Selection and Property Prediction Using Artificial Neural Networks The integration of Artificial Intelligence (AI) in materials engineering has revolutionized the process of material selection and property prediction. Among various AI methods, Artificial Neural Networks (ANNs) have emerged as powerful tools capable of modeling complex nonlinear relationships between material composition, processing parameters, and mechanical or thermal properties. This review presents a comprehensive analysis of recent advances in AI-based material informatics, focusing on ANN-driven approaches for predicting properties such as tensile strength, hardness, and thermal conductivity, and for recommending suitable materials in engineering design. The study examines key methodologies, dataset sources, preprocessing techniques, and ANN architectures used across the literature. Additionally, it compares hybrid frameworks that integrate ANNs with multi-criteria decision-making (MCDM) techniques for intelligent material recommendation. Challenges such as data scarcity, model interpretability, and generalization across diverse material classes are critically discussed. Finally, the paper outlines potential research directions, including the integration of deep learning, uncertainty quantification, and cloud-based deployment for scalable material intelligence systems. This review aims to provide insights into current trends, technological developments, and future opportunities for AI-driven material selection and property prediction systems in mechanical and materials engineering. Artificial neural network ; Property (philosophy) ; Process (computing) ; Material selection ; Selection (genetic algorithm) ; Scalability ; Preprocessor ; Software deployment Machine Learning in Materials Science ; Material Selection and Properties ; Microstructure and Mechanical Properties of Steels
Authors: Md Amirul Islam | Razaul Islam | Umme Saima Borsha
Source: International Journal of Scientific Research and Modern Technology. (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Accelerating Ceramic Materials Development via Machine Learning: A Critical Review Ceramics are inorganic, non-metallic materials renowned for their exceptional thermal, mechanical, chemical, and functional properties, making them indispensable across aerospace, biomedical, electronic, and energy applications. Despite their advantages, optimizing ceramic processing remains a major challenge due to the complex interplay of temperature, particle characteristics, atmosphere, and additives, where minor deviations can significantly affect microstructure and performance. Traditional experimental and computational approaches are often time-consuming or resource intensive. Machine learning (ML) offers a transformative alternative by enabling accurate property prediction, process optimization, materials discovery, and degradation analysis. This review presents a comprehensive overview of ML applications in ceramics, literature examples, and discusses current challenges and future directions, highlighting the potential of ML to accelerate the design and development of high-performance ceramic materials. Ceramic ; Process (computing) ; Resource (disambiguation) ; Energy resources ; Property (philosophy) ; Transformative learning Machine Learning in Materials Science ; High-Temperature Coating Behaviors ; Advanced ceramic materials synthesis
Authors: Haoke Qiu (18360304) | Zhao-Yan Sun (1468582)
Source: Figshare (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Exploring SMILES and BigSMILES: Toward Efficient andScalable Polymer Representations for Machine Learning A simple yet versatile polymer representation serves as a cornerstone for the large-scale adoption of polymer machine learning (ML), especially in the regime of large language models (LLMs). Among emerging approaches, BigSMILES stands out as an elegantly crafted representation, designed with a refined syntax to transcend the limitations of SMILES, most notably its struggle to capture the stochastic nature of polymers. However, in practical polymer ML tasks, the performance comparison between BigSMILES-based and SMILES-based ML models remains an open question, posing a challenge to the further development and adoption of advanced polymer representations. In this study, we systematically evaluate SMILES and BigSMILES across 12 polymer-related tasks, including forward property prediction and reverse design, using convolutional neural networks and LLMs. Our results not only show that BigSMILES achieves comparable or superior performance to SMILES, but also first demonstrate its ability to accelerate LLMs training due to its reduced token complexity. This finding is anticipated to reduce energy and cost expenditures in the training of polymer LLMs, while also facilitating the expedited development of more advanced polymer LLM agents, as we have further demonstrated that BigSMILES can more accurately encode chemical information and monomer connectivity for copolymers. This work serves as a starting point for highlighting the potential of BigSMILES to streamline and accelerate polymer ML workflows. Looking ahead, advancing polymer representations to integrate polymer chain structure, phase morphology, and processing parameters will be crucial for capturing the multifaceted relationships between polymer structure and properties, driving more accurate, efficient, and scalable modeling. Scalability ; Representation (politics) ; Convolutional neural network ; Polymer architecture ; Set (abstract data type) ; Deep learning ; ENCODE ; Polymer Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Zhili Chang | Guangquan Li | Wei‐Jun Cai | Haolan Liu | Guangcheng Zhang | Wei‐Tao Ou
Source: Research Square (preprint, 2025-08-29)
Field: Materials Science — Materials Chemistry

Application of machine learning in high-throughput screening of binary alloys for the hydrogenation of benzene Benzene ; Throughput ; Binary number ; High-throughput screening ; Computer science ; Binary classification ; Materials science ; Artificial intelligence ; Chemistry ; Organic chemistry ; Mathematics ; Arithmetic ; Operating system ; Support vector machine Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Paweł Wojciechowski | A. Bobyk | Mariusz Krawiec
Source: Materials (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Structure and Energetics of Chemically Functionalized Silicene: Combined Density Functional Theory and Machine Learning Approach It is crucial to control and comprehend the interaction between elemental adsorbates and two-dimensional materials to drive future generations of electronic, sensing, and energy applications. One such material, particularly interesting from the perspective of tunability, is silicene-the silicon-based cousin of graphene. In this work, we investigate nearly 2000 atomic adsorption models on silicene via a combination of density functional theory (DFT) and machine learning (ML). Different systems with varied adsorption geometries, element identities, and surface coverages were optimized using spin-polarized DFT, and the most stable configurations were selected based on adsorption energy. This information was used to train various ML models, including tree-based models and artificial neural networks, to predict adsorption geometry (classification) and adsorption energy (regression). The current hybrid DFT + ML approach provides a transferable framework for high-throughput screening of element-functionalized silicene and other 2D surfaces, which is of immense importance in directing surface modification strategies in electronic and catalytic device engineering. Density functional theory ; Adsorption ; Energetics ; Surface (topology) ; Artificial neural network ; Perspective (graphical) Graphene research and applications ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Yongzhi Wu (1846510) | Haoxiang Zhang (2744059) | Hanqing Li (400872) | Keyuan Chen (14053232) | Jinlong Chen (1333647) | Xingkao Zhang (21221433) | Ju Rong (740108) | Yudong Sui (21221436) | Xiaohua Yu (1442977) | Jing Feng (177843)
Source: Figshare (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

Deep Learning-EnhancedTitanium Potential Models forAccurate Molecular Dynamics Deep potential (DP) is an advanced deep learning-based method that can extract complex material interactions from density functional theory (DFT) calculations to construct highly accurate potential energy functions. Among various material systems, the transition metal titanium (Ti) has garnered significant attention in fields such as energy storage and conversion and catalysis, due to its unique electronic structure and catalytic activity. However, the complex interatomic interactions of Ti pose challenges for traditional potential models, making accurate descriptions difficult. To address this challenge, we developed a DP method capable of effectively establishing the interatomic potential functions required for molecular dynamics (MD) simulations of Ti, thereby enhancing simulation accuracy. This study systematically compares the predictions of the DP model with results from DFT, demonstrating that the DP model achieves DFT-level accuracy in predicting key properties. These properties include crystal structural characteristics, defect properties, surface and interface properties, as well as mechanical properties. The α-phase Hexagonal Close-Packed structure automatically transforms into the β-phase Body-Centered Cubic structure at 1189 K, exhibiting a melting temperature of 1864 K. These values closely align with the experimental phase transition temperature of 1209 K and the melting point of 1941 K, confirming the DP model’s accuracy under high-temperature conditions. Increasing temperature enhances Ti atom diffusivity, with a self-diffusion coefficient observed at the melting temperature of 2.51 × 10<sup>–10</sup> m<sup>2</sup>/s, matching those of other Face-Centered Cubic metals. The successful application of this method lays a foundation for multiscale simulations and performance predictions of complex materials, with potential to advance high-throughput screening and precise modeling of new materials. Interatomic potential ; Molecular dynamics ; Embedded atom model ; Potential energy ; Titanium ; Potential method ; Melting point ; Crystal (programming language) ; Atom (system on chip) ; Titanium alloy Machine Learning in Materials Science ; Nuclear Materials and Properties ; Electrocatalysts for Energy Conversion
Authors: Natalie E. Hooven | Arthur Lin | Rose K. Cersonsky
Source: Research Square (preprint, 2025-10-27)
Field: Materials Science — Materials Chemistry

Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems Extrapolation ; Transferability ; Construct (python library) ; Convergence (economics) ; Popularity ; Macromolecule ; Computation ; Interatomic potential Machine Learning in Materials Science
Authors: Liao Zhang | Xiangning Song | Peng Li | Yuan Yuan | Kefeng Wan | Fei Huang | Yafeng Guo | Hongzhe Zhang
Source: Arabian Journal of Chemistry (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Predictive methods for the heat of decomposition of reactive chemicals: From CHETAH, QSPR, and quantum chemical calculations to deep learning The heat of decomposition of reactive chemicals is a critical parameter for characterizing their thermodynamic properties and has broad application across various essential fields, including hazard identification, chemical classification, and safety risk assessment of chemical reactions. Currently, the primary method for determining the heat of decomposition of compounds relies on experimental techniques, which are not only complex but also inherently associated with significant potential hazards. The existing predictive methodologies are still in the developmental stage. This paper first analyzes and summarizes the current research progress on the prediction of heat of decomposition, then provides an in-depth exploration of the three most prominent predictive methods: the Chemical Thermodynamic and Energy Release Computer Program (CHETAH) program, quantum chemistry calculation, and Quantitative Structure-Property Relationship (QSPR) methods. It focuses on their underlying prediction mechanisms, accuracy, advantages, and limitations. Among these, quantum chemical computation methods and the QSPR approach have demonstrated superior predictive performance. The study specifically emphasizes that integrating deep learning techniques can overcome existing bottlenecks: transfer learning can mitigate the challenge of limited samples in QSPR modeling, while large language models (LLMs) in chemistry can address the prediction difficulties of decomposition reaction equations. These innovative directions are expected to significantly enhance predictive accuracy and provide crucial technical pathways for future research. Chemistry ; Quantitative structure–activity relationship ; Quantum chemical ; Decomposition ; Computational chemistry ; Biochemical engineering ; Organic chemistry ; Stereochemistry ; Molecule Thermal and Kinetic Analysis ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Jimin Park | Min‐Gi Jeong | Muhammad Hilmy Alfaruqi | Muhamad Kurniawan | Shizhao Xiong | Shivam Kansara | Hyerim Kim | Hyeona Park | Jaekook Kim | Chan-Woo Lee | Hun‐Gi Jung | Jang‐Yeon Hwang
Source: Chemical Engineering Journal (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Locking of dendrites in potassium metal batteries: A potassiophilic 3D host discovered with machine learning screening Nucleation ; Overpotential ; Anode ; Multiphysics ; Current density ; Dendrite (mathematics) ; Porosity ; Crystallization Machine Learning in Materials Science ; Advancements in Battery Materials ; Ionic liquids properties and applications
Authors: Haitang Wang (8203125) | Wenbin Liu (174604) | Fang Fang (38179) | Qiuyi Fu (4809561) | Guobing Zhou (1543390) | Zhen Yang (107412)
Source: Figshare (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Atomic-LevelExploration of Local Structural Heterogeneityin Liquid Ga–In Alloys Using a Machine Learning Potential Liquid gallium–indium (Ga–In) alloys have emerged as an intriguing class of materials that combine the advantages of both metals and liquids. Their unique properties enable a range of potential applications in chemistry, physics, materials, and engineering. Thus, elucidating the local structural properties of liquid Ga–In alloys at the nanoscale is essential for understanding the processes occurring within them and for advancing new applications. Here, we performed an atomic-level investigation of local coordination behavior and structural ordering around Ga and In atoms in five liquid Ga–In alloys (i.e., Ga<sub>79.3</sub>In<sub>20.7</sub>, Ga<sub>83.2</sub>In<sub>16.8</sub>, Ga<sub>85.8</sub>In<sub>14.2</sub>, Ga<sub>91.8</sub>In<sub>8.2</sub>, and Ga<sub>96.9</sub>In<sub>3.1</sub>) using machine learning force field molecular dynamics simulations. Our findings reveal that the Ga<sub>79.3</sub>In<sub>20.7</sub>, Ga<sub>83.2</sub>In<sub>16.8</sub>, and Ga<sub>85.8</sub>In<sub>14.2</sub> alloys exhibit a continuous and predominant Ga–In phase, which is characterized by a spatial distribution of clustered In atoms. In stark contrast, the Ga<sub>91.8</sub>In<sub>8.2</sub> and Ga<sub>96.9</sub>In<sub>3.1</sub> alloys demonstrate the coexistence of pure Ga and Ga–In alloy phases, with In atoms forming dimers in the former and being atomically dispersed in the latter. More importantly, it is shown that there is a prominent difference in the local structural ordering surrounding Ga and In atoms in the first three Ga–In alloys, following a specific order of In–In > Ga–In > Ga–Ga, which is ascribed to the differences in interaction strengths. Overall, this study presents the first theoretical evidence of local structural heterogeneity in liquid Ga–In alloys and offers new insights into the nanoscale structural details in liquid Ga-based alloys. Nanoscopic scale ; Molecular dynamics ; Alloy ; Field (mathematics) ; Local structure ; Order (exchange) ; Range (aeronautics) Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys ; Material Dynamics and Properties
Authors: V. Narayanan | Vidya Francis | Roshini K. Thumpakara | Seema Appukuttan | Albin Kurian | David I. Joseph
Source: Multidisciplinary Reviews (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Bridging quantum and computation: A bibliometric analysis of machine learning in quantum chemistry This bibliometric study systematically explores the integration of Machine Learning (ML) into quantum chemistry, a multidisciplinary approach aimed at addressing complex chemical problems through the advanced computational capabilities of ML algorithms. Utilizing Scopus for comprehensive bibliographic data collection, the study adheres to the PRISMA flowchart methodology for rigorous screening, inclusion, and exclusion of research articles. Analytical processing and visualization of the data are accomplished using two prominent bibliometric analysis tools, Biblioshiny and CiteSpace, facilitating a multifaceted examination of the field's scholarly output. The study's findings are extensive, covering annual scientific production, which unveils the field's growth trajectory and burgeoning interest over time. It identifies the most productive author, thereby highlighting the leading contributors to the field and setting a benchmark for academic output. Coauthorship analysis reveals the collaborative networks within the community, illustrating the interconnectedness of researchers globally. Examination of the most globally cited documents provides insight into the foundational works and seminal papers that have shaped the field. This research further explored trend topics, thematic maps, and conceptual structure maps using Multiple Correspondence Analysis to discover innovative themes and the fields at the center of attention in the literature. In addition, a timeline network visualization of keyword co-occurrence and a cluster network visualization of cited authors illustrated a visual dynamic development of the research field and the scientific inheritance. Network visualization of cited journals and a timezone network visualization of countries’ cooperation demonstrated the interdisciplinary nature and the research being conducted globally. By examining research gaps and the scope of practical application, the present study not only described the current standing of knowledge but also guided future research paths. Finally, the findings support the transformative role of ML in quantum chemistry. Visualization ; Bridging (networking) ; Field (mathematics) ; Multidisciplinary approach ; Graph drawing ; Data visualization ; Timeline ; Information visualization ; Scope (computer science) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics