jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elnaz Bangian Tabrizi | Mehrdad Jalali | Mahboobeh Houshmand
Source: Journal Of Big Data (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Inverse link prediction with graph convolutional networks for knowledge-preserving sparsification in cheminformatics Abstract Large-scale cheminformatics datasets, such as those used in drug discovery and materials science, are often represented as dense similarity graphs; however, their complexity hinders scalable analysis and interpretability. We propose a novel Inverse Link Prediction (ILP) framework, powered by Graph Neural Networks (GNNs), for knowledge-preserving graph sparsification, using Metal–Organic Framework (MOF) datasets as a case study. The framework comprises four key components: (1) Graph Convolutional Networks (GCNs) to predict edge importance based on node features, (2) ILP to compute inverse weights identifying redundant edges, (3) dual-weight analysis to integrate initial similarity weights with GCN-derived weights, and (4) modularity optimization to prune edges while preserving community structures and domain knowledge. Validated on MOF similarity graphs, the sparsified graphs maintain structural integrity and support robust performance across both graph-based (GCN, GraphRAGE) and non-graph-based (Gradient Boosting Trees, Logistic Regression, Naïve Bayes, Deep Neural Networks) machine learning models for tasks such as pore limiting diameter prediction. This Inverse Link Prediction with Graph Convolutional Networks (ILP-GCN) framework offers a scalable and interpretable solution for cheminformatics, with broad applications in material discovery and beyond. Graphical Abstract Cheminformatics ; Computer science ; Scalability ; Interpretability ; Graph ; Machine learning ; Theoretical computer science ; Artificial intelligence ; Convolutional neural network ; Inverse ; Data mining ; Mathematics ; Bioinformatics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Graph Neural Networks
Authors: Xiaoqi Zhao | Bao Wang | Kun Zhou | Jiangjiexing Wu | Kai Song
Source: Journal of Chemical Information and Modeling (article, 2025-02-14)
Field: Materials Science — Materials Chemistry

High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework Metal-embedded complexes (MECs), including transition metal complexes (TMCs) and metal-organic frameworks (MOFs), are important in catalysis, materials science, and molecular devices due to their unique metal atom centrality and complex coordination environments. However, modeling and predicting their properties accurately is challenging. A new metal attention (MA) framework for graph neural networks (GNNs) was proposed to address the limitations of traditional methods, which fail to differentiate core coordination structures from ordinary covalent bonds. This MA framework converts heterogeneous graphs of complexes into homogeneous ones with distinct metal features by highlighting key metal-feature coordination through hierarchical pooling and a metal cross-attention. To assess its performance, 11 widely used GNN algorithms, three of which are heterogeneous, were compared. Experimental results indicate significant improvements in accuracy: an average of 32.07% for predicting TMC properties and up to 23.01% for MOF CO<sub>2</sub> absorption. Moreover, tests on the framework's robustness regarding data set size variation and comparison with a larger non-MA model show that the enhanced performance stems from the architecture, not merely increasing model capacity. The MA framework's potential in predicting metal complex properties offers a potent statistical tool for optimizing and designing new materials like catalysts and gas storage systems. Throughput ; Metal ; Computer science ; Materials science ; Metallurgy ; Operating system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Sunao Nakanowatari | Keisuke Takahashi | Hieu‐Chi Dam | Toshiaki Taniike
Source: ACS Catalysis (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity Predicting the performance of heterogeneous catalysts is difficult because it involves complex interactions and unknown elementary reactions; hence, traditional catalyst development relies on trial and error. Machine learning offers a structured approach to address these issues. However, this approach is limited by challenges such as descriptor design, sparse data, and context-dependent interactions. In this study, two machine learning systems were developed to address these challenges in catalyst discovery: a recommender system that balances exploration and exploitation, and a serendipiter that identifies unexpected discoveries for the recommender─catalysts expected to exhibit high performance despite being predicted as most likely non-high-performing. These systems were tested on the oxidative coupling of methane, and the results demonstrated a promising improvement in the efficiency of catalyst discovery. The recommender, based on evidence theory, uses binary combinations of catalyst components as descriptors to predict performance. It handles incomplete data by quantifying contradictions and uncertainty, facilitating a balance between exploration (testing unevidenced catalysts) and exploitation (refining known high-performing ones). The recommender efficiently identified a diverse range of high-performing catalysts through adaptive sampling with 160 catalysts. The serendipiter, a meta-learner, identifies unexpected high-performing catalysts by leveraging different machine learning models. It increased the occurrence of serendipitous discoveries to 50%, compared to 3% with the recommender alone. In summary, these systems improve the efficiency and reproducibility of catalyst discovery by balancing exploitation, exploration, and serendipity. Serendipity ; Catalysis ; Computer science ; Data science ; Nanotechnology ; Biochemical engineering ; Chemistry ; Materials science ; Engineering ; Epistemology ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: E. Antillon | Noam Bernstein
Source: Physical Review Materials (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Thermodynamical phase-stability of a Cu-Al binary system using machine-learning interatomic potentials The mechanical properties of structural materials are critically affected by the solid phases that are present in their microstructure, including both their crystallographic structure and chemical order. Predicting the stability of these phases as a function of thermodynamic state variables such as temperature and composition is therefore essential for the study of mechanical properties. In equilibrium, these stabilities are determined by the free energies of the various phases, which we compute from first principles as a function of composition and temperature for the Cu-Al binary alloy. Using two types of machine-learning interatomic potentials fitted to density functional theory calculations, we perform thermodynamic integration across various paths to determine the relative free energies of liquid, face-centered cubic, body-centered cubic, and hexagonal close-packed structures, both with and without chemical ordering. We compare the resulting stable phases and phase boundaries to experimental observations in the high-temperature copper-rich region of the phase diagram. Our results demonstrate that MLIPs fitted without prior knowledge of the material's crystal structure can reproduce the first principles reference potential energy surface with sufficient accuracy and speed to bridge the length and time scales necessary for calculation of free energies, enabling phase stability calculations. Materials science ; Binary number ; Stability (learning theory) ; Phase (matter) ; Thermodynamics ; Interatomic potential ; Chemical physics ; Molecular dynamics ; Computational chemistry ; Machine learning ; Computer science ; Quantum mechanics ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Intermetallics and Advanced Alloy Properties
Authors: Fatema Tuz Zohura | Mariam Akter Mimona | Md Mir Shakib Ahmed | Md Israfil Hossain Rimon | Md. Aminul Islam | Nayem Hossain | Md Hosne Mobarak
Source: Nano LIFE (review, 2025-04-26)
Field: Materials Science — Materials Chemistry

Application of Machine Learning in Nano Research — A Review Within the field of nanotechnology, the incorporation of machine learning has introduced unparalleled prospects for breakthroughs at the atomic and molecular scale. This paper examines the intricate interplay between machine learning and nanoparticle research, highlighting the profound possibilities that arise from this synergistic partnership. Machine learning has revolutionized the field of nanoparticle research by providing an unprecedented level of accuracy in traversing the various intricacies associated with these particles. It accomplishes this by effectively recognizing and analyzing complicated patterns within extensive datasets. The applications of nanotechnology encompass a wide range of areas, such as the improvement of drug delivery systems and the advancement of catalytic processes. These applications have the potential to bring forth groundbreaking innovations by integrating computational capabilities with research conducted at the nanoscale. Nevertheless, there are several obstacles that need to be addressed in this context. One such challenge is the limited availability of annotated data in specific domains. Additionally, there is a requirement for robust models that can effectively understand the intricate nature of nanomaterials. In order to address these limitations, current endeavors are centered on enhancing the interpretability of models and augmenting the availability of annotated datasets. Machine learning could improve nanoparticle research and create new avenues. These advances include energy-efficient catalysts and tailored pharmaceutical delivery systems. In this review, machine learning applications in nanoparticle synthesis and characterization are critically examined. It outlines the obstacles and suggests more research. These undiscovered possibilities could lead to groundbreaking nanotechnology research. Nano- ; Artificial intelligence ; Computer science ; Machine learning ; Nanotechnology ; Engineering ; Materials science ; Chemical engineering Machine Learning in Materials Science ; Machine Learning and ELM ; Mineral Processing and Grinding
Authors: Atwakyire Moses | Ying Gui | Ding Chen
Source: Materials Today Communications (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization Bayesian optimization ; Materials science ; Throughput ; Bayesian probability ; Machine learning ; Process engineering ; Artificial intelligence ; Computer science Titanium Alloys Microstructure and Properties ; Aluminum Alloy Microstructure Properties ; Magnesium Alloys: Properties and Applications
Authors: Srivathsan Badrinarayanan | Rishikesh Magar | Akshay Antony | Radheesh Sharma Meda | Amir Barati Farimani
Source: ArXiv.org (preprint, 2025-05-30)
Field: Chemistry — Inorganic Chemistry

MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational screening techniques such as molecular simulations and density functional theory (DFT), while accurate, are computationally prohibitive at scale. Machine learning offers an exciting alternative by leveraging data-driven approaches to accelerate materials discovery. The complexity of MOFs, with their extended periodic structures and diverse topologies, creates both opportunities and challenges for generative modeling approaches. To address these challenges, we present a reinforcement learning-enhanced, transformer-based framework for the de novo design of MOFs. Central to our approach is MOFid, a chemically-informed string representation encoding both connectivity and topology, enabling scalable generative modeling. Our pipeline comprises three components: (1) a generative GPT model trained on MOFid sequences, (2) MOFormer, a transformer-based property predictor, and (3) a reinforcement learning (RL) module that optimizes generated candidates via property-guided reward functions. By integrating property feedback into sequence generation, our method drives the model toward synthesizable, topologically valid MOFs with desired functional attributes. This work demonstrates the potential of large language models, when coupled with reinforcement learning, to accelerate inverse design in reticular chemistry and unlock new frontiers in computational MOF discovery. Generative grammar ; Reinforcement learning ; String (physics) ; Generative Design ; Scalability ; Generative model ; Encoding (memory) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Han-Bin Liu | Xiaoyu Cheng | Guohua Zhou | Juan Yang | Feng Tan | Donghui Lan | Jian‐Ping Tan | Bing Yi | Weixin Zhai | Qing‐Hui Guo
Source: Chinese Chemical Letters (article, 2025-03-28)
Field: Chemistry — Spectroscopy

CrownBind-IA: A machine learning model predicting binding constants between crown ethers and alkali metal ions Alkali metal ; Crown (dentistry) ; Chemistry ; Ion ; Crown ether ; Metal ; Metal ions in aqueous solution ; Computational chemistry ; Inorganic chemistry ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Composite material Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Molecular Sensors and Ion Detection
Authors: Xinqi Chen | Haiyang Cheng | Ran Shi | Tong Zhou | Tianwei He | Qingju Liu
Source: Advanced Functional Materials (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Recent Advances in Data‐Driven Design of Dual‐Atom Catalysts Abstract Dual‐atom catalysts (DACs) have emerged as a cutting‐edge research frontier in energy and environmental catalysis. With the integration of high‐throughput density functional theory calculations and machine learning (ML) algorithms, data‐driven design strategies are reshaping traditional material discovery workflows. These approaches enable the establishment of structure–property relationships that guide the rational construction of efficient active sites in DACs. This review highlights the advantages of data‐driven methodologies in DAC design, focusing on structural optimization strategies including dual‐site configuration tuning and microcoordination environment regulation. It further outlines typical ML workflows for DAC screening and showcases representative applications in energy conversion and environmental remediation. Finally, the significance of developing a closed‐loop system that integrates theoretical calculations, intelligent prediction, and experimental validation is underscored, and perspectives for advancing this evolving research paradigm are offered. Materials science ; Dual (grammatical number) ; Catalysis ; Atom (system on chip) ; Nanotechnology ; Computer science ; Organic chemistry ; Parallel computing Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Huabing Cai | Yujing Zhang | Qingli Tang | Zhongkang Han | Yi Gao
Source: Applied Surface Science (article, 2025-02-22)
Field: Materials Science — Materials Chemistry

Exploring structures of Pd clusters on hydrogenated ceria surface using High-Dimensional neural network potential Materials science ; Surface (topology) ; Nanotechnology ; Geometry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Guangxuan Song | Dongmei Fu | Zhongwei Qiu | Zijiang Yang | Jiaxin Dai | Lingwei Ma | Dawei Zhang
Source: Applied Soft Computing (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Bridging the semantic-numerical gap: A numerical reasoning method of cross-modal knowledge graph for material property prediction Property (philosophy) ; Bridging (networking) ; Graph ; Artificial neural network ; Numerical analysis ; Semantic annotation ; Projection (relational algebra) ; Numerical models ; Semantic network Machine Learning in Materials Science ; Topic Modeling
Authors: Souraya Goumri‐Said | Houssam Eddine Hailouf | Khalid Reggab | Kingsley Onyebuchi Obodo | Mohammed Benali Kanoun
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven exploration of Na–Bi compounds: a first-principles and machine learning approach to topological thermoelectrics We conduct an in-depth investigation of the structural, electronic, vibrational, thermodynamic, and thermoelectric characteristics of Na-Bi-based compounds, specifically tetragonal NaBi, hexagonal NaBi<sub>3</sub>, and cubic Na<sub>3</sub>Bi, using advanced first-principles calculations in conjunction with machine learning (ML) models. We used density functional theory (DFT) with spin-orbit coupling (SOC) to figure out the electronic structure, phonon dispersions, and thermoelectric transport using Boltzmann transport theory. Our findings validate the Dirac semimetal nature of cubic Na<sub>3</sub>Bi and demonstrate varied topological and thermodynamic properties within the Na-Bi family. To speed up the prediction of the thermoelectric figure of merit (<i>ZT</i>) while enhancing interpretability to understand at the feature level, we trained supervised ML models [Random Forest (RF) and Neural Network (NN)] on thermoelectric results from DFT. It is possible to directly compute the figure of merit (<i>ZT</i>) from DFT-derived transport coefficients such as the Seebeck coefficient, electrical conductivity, and thermal conductivity. However, machine learning (ML) models serve as powerful surrogate predictors, enabling rapid screening of derivative compounds and quantitative assessment of feature importance through SHAP (SHapley Additive exPlanations) analysis. At low temperatures, RF models consistently outperformed NN models, but both performed well at high temperatures. SHAP analysis showed that the Seebeck coefficient has the biggest effect on <i>ZT</i> in all regimes. This integrated, physics-informed, and data-driven methodology demonstrates that machine learning can significantly augment first-principles approaches. It accelerates predictions, guides feature prioritisation, and enhances design capabilities. The developed workflow provides a generalizable and interpretable framework for the predictive modeling of advanced topological thermoelectric materials. Thermoelectric effect ; Interpretability ; Figure of merit ; Thermoelectric materials ; Topology (electrical circuits) ; Tetragonal crystal system ; Artificial neural network ; Feature (linguistics) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Jiahao Jiang | Shuai Zhao | Yanqiu Sun | Xuedong Wang
Source: Angewandte Chemie (article, 2025-06-05)
Field: Chemistry — Physical and Theoretical Chemistry

From Binary to Higher‐Order Organic Cocrystals: Design Principles and Performance Optimization Abstract Organic cocrystals, particularly the evolution from binary to higher‐order structures, have garnered considerable attention due to their tunable intermolecular interactions and unique material properties. Binary cocrystals, formed through π‐π stacking, charge transfer, and hydrogen/halogen bonding, allow for precise control over molecular packing and enhanced optoelectronic properties. In contrast, higher‐order cocrystals, incorporating three or more components, enable greater complexity and functional diversity. Strategies such as homologation via isostructural substitution, hierarchical intermolecular interactions, and long‐range Synthon Aufbau Modules facilitate the synthesis of these advanced materials. The shift toward higher‐order cocrystals paves the way for novel applications in fields such as deep learning for cocrystal prediction, drug design, organic solar cells, and NIR‐II photothermal conversion. However, challenges related to molecular screening, ratio optimization, scalable synthesis, and long‐term stability remain critical hurdles for the broader implementation of these materials in practical applications. Order (exchange) ; Binary number ; Chemistry ; Materials science ; Nanotechnology ; Mathematics Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: P. D. Varuna S. Pathirage | Brody Quebedeaux | Shahzad Akram | Konstantinos D. Vogiatzis
Source: The Journal of Physical Chemistry A (review, 2025-03-25)
Field: Materials Science — Materials Chemistry

Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme Machine learning has recently been introduced into the arsenal of tools that are available to computational chemists. In the past few years, we have seen an increase in the applicability of these tools on a plethora of applications, including the automated exploration of a large fraction of the chemical space, the reduction of repetitive computational tasks, the detection of outliers on large databases, and the acceleration of molecular simulations. An attractive application of machine learning in molecular electronic structure theory is the "recycling" of molecular wave functions for faster and more accurate completion of complex quantum chemical calculations. Along these lines, we have developed hybrid quantum chemical/machine learning workflows that utilize information from low-level wave functions for the accurate prediction of higher-level wave functions. The <i>data-driven coupled-cluster</i> (DDCC) family of methods is discussed in this article together with the importance of the inclusion of physical properties in such hybrid workflows. After a short introduction to the philosophy and the capabilities of DDCC, we present our recent progress in extending its applicability to larger and more complex molecular structures and data sets. A significant advantage offered by DDCC is its transferability, with respect to different molecular systems and different excitation levels. As we show here, predicted wave functions at the coupled-cluster singles and doubles level of theory can be used for the accurate prediction of the perturbative triples of the CCSD(T) scheme. We conclude with some personal considerations with respect to future directions related to the development of the next generation of such hybrid quantum chemical/machine learning models. Transferability ; Cluster (spacecraft) ; Scheme (mathematics) ; Coupled cluster ; Computer science ; Data mining ; Mathematics ; Chemistry ; Machine learning ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yuanyuan Xia | Jianping Tian | Yifei Zhou | Dan Huang | Liangliang Xie | Xinjun Hu | Haili Yang | Jie Shang
Source: Food Control (article, 2025-09-17)
Field: Chemistry — Analytical Chemistry

Rapid multi-task detection of rice components using convolutional bidirectional long short-term fusion network combined with hyperspectral imaging Hyperspectral imaging ; Residual ; Convolutional neural network ; Partial least squares regression ; Pattern recognition (psychology) ; Artificial neural network ; Fusion Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research