jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuancheng Sun | Kai Chen | Kang Liu | Qiwei Ye
Source: Pattern Recognition (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Tetrahedral molecular pretraining for enhanced property prediction • Novel tetrahedra-centric pretraining for 3D molecular representation learning. • Geometric patterns and chemical prior introduced in modeling and pretraining proves beneficial. • Consistent performance gains across biochemical and quantum property predictions. • Scalable to complex protein-ligand systems with robust performance. • Improved interpretability validated by a probing task and embedding visualization. Self-supervised learning on 3D molecular structures has emerged as a promising direction in data-driven scientific research, addressing the significant challenge of limited annotated biochemical data. While the identification of semantic units for pretraining has been well-established in natural language processing and computer vision, determining optimal building blocks for characterizing 3D molecular architectures remains underexplored. We present Tetrahedral Molecular Pretraining (TMP), a novel approach that recognizes tetrahedrons as fundamental building blocks, leveraging their geometric simplicity and recurring presence across chemical functional groups. Through systematic perturbation and reconstruction of tetrahedral substructures, TMP implements a self-supervised learning strategy that recovers both their global arrangements and local patterns, learning rich molecular representations that encode multi-scale structural information. Extensive evaluations on 24 benchmark datasets demonstrate that TMP consistently outperforms existing methods in tasks ranging from biochemical property prediction to quantum property prediction. Notably, the tetrahedra-based modeling successfully scales from small molecules to complex protein-ligand systems, achieving new state-of-the-art results in binding affinity prediction. Our findings highlight how the identification of representative structural patterns can lead to more expressive and interpretable neural networks for scientific applications. Interpretability ; Property (philosophy) ; Embedding ; Tetrahedron ; ENCODE ; Scalability ; Identification (biology) ; Benchmark (surveying) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tatiana S. Ilina | Sofia V. Torianik | Dmitrii S. Zakhidov | Evgeniya L. Buryanskaya | Anatoly V. Pavlenko | Ekaterina V. Barabanova
Source: Modern Electronic Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Modeling of ferroelectric properties of KNN-based ceramics Replacement of highly noxious led zirconate titanate based materials in MEMS applications for environmentally safe materials remains an important task. Complex multicomponent doping of KNN ceramics provides for a significant improvement of their ferroelectric properties. The efficiency of different machine learning (ML) methods in the prediction of piezoelectric coefficient of KNN-based ceramics has been analyzed. ML integration allows one to accelerate the development of new materials and identify latent interdependences between their composition, structure, and properties. To understand the mechanisms of relationship between the composition and properties, we used the Sure Independence Screening and Sparsifying Operator (SISSO) method in combination with regression ML methods, such as Support Vector Regression, Ridge, Lasso, Elastic Net, Xtreme Gradient Boosting, Random Forest, Gradient Boosting Regression, and Extreme Tree Regression. The Gradient Boosting Regression (GBR) method proved to be the most efficient in combination with SISSO. The test sampling accuracy reached 81 %, and GBR validation for experimental data delivered a mean absolute error of 26 pC/N. Lead zirconate titanate ; Ceramic ; Piezoelectricity ; Gradient boosting ; Dielectric ; Ferroelectricity ; Ferroelectric ceramics ; Boosting (machine learning) Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Huy Duy Nguyen | Phong H. Nguyen | Giang H. Bach | Oanh T. K. Nguyen
Source: Research Square (preprint, 2025-09-16)
Field: Materials Science — Materials Chemistry

Accelerating the search for carbon cluster isomers via machine-learning potential Cluster (spacecraft) ; Infrared ; Density functional theory ; Infrared spectroscopy ; Carbon fibers ; Spectral line ; Distortion (music) Machine Learning in Materials Science ; Boron Compounds in Chemistry ; History and advancements in chemistry
Authors: Shuhui Qu | Cheolwoo Park
Source: ArXiv.org (preprint, 2025-12-13)
Field: Materials Science — Materials Chemistry

GoMS: Graph of Molecule Substructure Network for Molecule Property Prediction While graph neural networks have shown remarkable success in molecular property prediction, current approaches like the Equivariant Subgraph Aggregation Networks (ESAN) treat molecules as bags of independent substructures, overlooking crucial relationships between these components. We present Graph of Molecule Substructures (GoMS), a novel architecture that explicitly models the interactions and spatial arrangements between molecular substructures. Unlike ESAN's bag-based representation, GoMS constructs a graph where nodes represent subgraphs and edges capture their structural relationships, preserving critical topological information about how substructures are connected and overlap within the molecule. Through extensive experiments on public molecular datasets, we demonstrate that GoMS outperforms ESAN and other baseline methods, with particularly improvements for large molecules containing more than 100 atoms. The performance gap widens as molecular size increases, demonstrating GoMS's effectiveness for modeling industrial-scale molecules. Our theoretical analysis demonstrates that GoMS can distinguish molecules with identical subgraph compositions but different spatial arrangements. Our approach shows particular promise for materials science applications involving complex molecules where properties emerge from the interplay between multiple functional units. By capturing substructure relationships that are lost in bag-based approaches, GoMS represents a significant advance toward scalable and interpretable molecular property prediction for real-world applications. Molecular graph ; Substructure ; Property (philosophy) ; Graph ; Scalability ; Topology (electrical circuits) ; Graph theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jin Young Choi | Sina Malakpour Estalaki | Daniel Quispe | Rujing Zha | Rowan Rolark | Mojtaba Mozaffar | Jian Cao
Source: Additive manufacturing (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Transfer learning enabled geometry, process, and material agnostic RGNN for temperature prediction in directed energy deposition Materials science ; Process (computing) ; Deposition (geology) ; Energy transfer ; Engineering physics ; Mechanical engineering ; Computer science Machine Learning in Materials Science ; Nuclear Materials and Properties ; Thermal properties of materials
Authors: Panpan Xie | Yanqing Hu | Jiawei Li | Yunpeng Wang | Li Zhang | Xiangyong Zhang | Mingyu Li | Jiayan Fang | Changping Huang | James C. Hower
Source: Powder Technology (article, 2025-07-26)
Field: Chemistry — Spectroscopy

Predicting and evaluating synthesis factors responsible for the property of silica aerogel using ensemble learning: Experiment verification and mechanism elaboration Elaboration ; Aerogel ; Mechanism (biology) ; Property (philosophy) ; Computer science ; Materials science ; Nanotechnology ; Physics Aerogels and thermal insulation ; Catalysis and Oxidation Reactions ; Mesoporous Materials and Catalysis
Authors: Xu-Yuan Zhou | Junzhong Xie | Hong Jiang
Source: Chinese Journal of Chemical Physics (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Theoretical study of configurationally disordered bimetallic surfaces based on machine learning force field aided cluster expansion approach Bimetallic surfaces play a pivotal role in heterogeneous catalysis, yet their theoretical modeling has long been hindered by the computational challenges of capturing configurational disorder, a critical feature governing their catalytic properties. Traditional approaches rely on oversimplified ordered surface models or restrict disorder to a few atomic layers, limiting their predictive power. Here, we present an accurate and efficient computational framework that integrates machine learning force fields (MLFFs) with the cluster expansion (CE) method to study configurationally disordered bimetallic surfaces at finite temperatures. We have developed an efficient workflow in which the MLFF is first trained iteratively via an active learning protocol, and then used to generate accurate energetic data for thousands of configurations that enable robust CE model construction. By treating bulk and surface clusters separately, we can build CE models for surface slabs with an arbitrary number of layers. Using Cu1−xZnx as a case study, our CE-based Monte Carlo simulations reveal key structural insights that are relevant to the understanding of catalytic properties of Cu1−xZnx surfaces. This work demonstrates how MLFF-aided CE can overcome traditional limitations in theoretical modeling of bimetallic surfaces and highlights pathways toward more realistic modeling of heterogeneous catalysts. Bimetallic strip ; Workflow ; Cluster (spacecraft) ; Field (mathematics) ; Surface (topology) ; Feature (linguistics) ; Limiting ; Force field (fiction) Machine Learning in Materials Science ; Thermal properties of materials ; nanoparticles nucleation surface interactions
Authors: Wenlong Cai | Xinmiao Xie | Zezhou Yang | Xuefeng Guo
Source: Angewandte Chemie (article, 2025-03-24)
Field: Chemistry — Spectroscopy

Stereochemistry at the Single‐Molecule Level: From Monitoring to Regulation Abstract Traditional stereochemistry analysis is crucial for understanding the molecular behavior, but relies on measurements that encompass multiple molecules and obscure individual molecular dynamics. Single‐molecule techniques enable real‐time tracking of stereochemical transformations. These techniques include electrical methods (such as scanning probe microscopy, single‐molecule junction techniques, and nanopore technology) and non‐electrical approaches (such as circular dichroism spectroscopy and surface‐enhanced Raman spectroscopy). This review highlights recent advances in monitoring and regulation of stereochemical properties at the single‐molecule level. Techniques that bridge macroscopic observations with molecular‐scale dynamics are emphasized. Key isomerization phenomena (constitutional, configurational, and conformational isomerizations) are explored to demonstrate how light, electric field, and mechanical force regulate molecular states. The use of chiral molecules in optical tweezers, chiral‐modified scanning tunneling microscopies, and graphene‐based single‐molecule junctions to leverage the chirality‐induced spin selectivity effect for enantiomer discrimination and manipulation is highlighted. Despite progress in this field, challenges persist in resolving ultrafast isomerization pathways, understanding chiral origin mechanisms, and integrating single‐molecule devices. Emerging strategies combining multimodal stimuli, machine learning, and nanofabrication are promising for advancing stereochemical research and applications in molecular electronics and nanotechnology. This work underscores the transformative potential of single‐molecule techniques in unveiling fundamental chemical dynamics and designing functional molecular systems. Molecule ; Chemistry ; Stereochemistry ; Organic chemistry Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; Chemical Reaction Mechanisms
Authors: Wolter, Mario
Source: LeoPARD - TU Braunschweig Publications And Research Data (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

(Towards) An Ontology for Theoretical Chemistry Ontologies, as formal and structured representations of knowledge within a domain, have been instrumental in enabling data integration, knowledge sharing, and interoperability. For instance in chemistry experimental techniques, instrumentation, and chemical entities are codified in ontologies such as the Chemical Methods Ontology[1] and the Chemical Information Ontology[2]. Surprisingly, no comparably comprehensive ontology exists for theoretical chemistry. An ontology is the foundation of the necessary standardization for building reusable software libraries, workflow engines, and data repositories. Classes in software code could be directly derived from the ontology and therefore facilitate the modular construction of software components. Thereby greatly enhancing the efficiency and reproducibility of computational workflows. Creating this ontology, however, is intrinsically a community endeavour. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem[3] or MolSSI QCSchema[4]). In the process of building the “comprehensive ontology” we should align with these existing vocabularies to maximise interoperability. For community inclusion an iterative, open development on collaborative platforms (e.g., GitHub) employing continuous integration should be enfostered. Early adoption can be catalysed by embedding identifiers into widely used computational packages (e.g., ORCA) and data repositories (e.g., Chemotion, NOMAD, QCArchive), thus ensuring immediate practical value. By uniting theoretical chemists around a shared semantic framework, the proposed ontology will accelerate method innovation, improve software sustainability, and unlock new possibilities for machine learning. [1] https://github.com/rsc-ontologies/rsc-cmo [2] Hastings J., Chepelev L., Willighagen E., Adams N., Steinbeck C., Dumontier M., PLoS One, 6(10):e25513 (2011) [3] Krdzavac N., Mosbach S., Nurkowski D., Buerger P., Akroyd J., Martin J., Menon A., Kraft M., J Chem Inf Model., 22;59(7):3154-3165 (2019) [4] https://molssi.org/software/qcschema-2/ &amp; https://github.com/MolSSI/QCSchema Ontology ; Workflow ; Process ontology ; Identifier ; Software ; Ontology-based data integration ; Process (computing) ; Semantic Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Biomedical Text Mining and Ontologies
Authors: Supratim Ghosh (1267269) | Nupur Jain (787943) | Raghavan B. Sunoj (1392208)
Source: Figshare (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Efficient MachineLearning for Yield Prediction inTransition Metal-Catalyzed <i>meta</i>-C(sp<sup>2</sup>)–HBond Activation and Other Reactions Developing machine learning (ML) models for yield prediction of chemical reactions has emerged as an important use case scenario in very recent years. In this space, reaction datasets present a range of challenges mostly stemming from imbalance and sparsity. Herein, we consider chemical language representations for reactions to tap into the potential of natural language processing models such as the ULMFiT (Universal Language Model Fine-Tuning) for yield prediction, which is customized to work across such distribution settings. We contribute a reaction dataset with more than 860 manually curated reactions collected from literature spanning over a decade, belonging to a family of catalytic <i>meta</i>-C(sp<sup>2</sup>)–H bond activation reactions of high contemporary importance. Taking cognizance of the dataset size, skewness toward the higher yields, and the sparse distribution characteristics, we developed (i) a time- and resource-efficient pretraining strategy for downstream transfer learning, and (ii) the CFR (classification followed by regression) model, which provided more accurate yield predictions than the traditional direct regression (DR) methods. Instead of the prevailing pretraining practice of using a large number of unlabeled molecules (1.4 million) from the ChEMBL dataset, we first created a pretraining dataset SSP1 (0.11 million) by using substructure-based mining from the PubChem database, which is found to be equally effective and more time-efficient in offering enhanced performance. The CFR model with the ULMFiT-SSP1 regressor achieved a notable RMSE of 8.40 ± 0.12 for the CFR-major and 6.48 ± 0.29 for the CFR-minor class in yield prediction on the title reaction, with a class boundary of yield at 53%. Furthermore, the CFR model is highly generalizable as evidenced by the significant improvement over the previous benchmarks on Buchwald–Hartwig coupling, Suzuki coupling, nickel-catalyzed C–O coupling, and USPTO reaction datasets. Our model provides a scalable and efficient framework that can assist in reaction discovery. Yield (engineering) ; chEMBL ; PubChem ; Class (philosophy) ; Support vector machine ; Probability distribution ; Distribution (mathematics) ; Regression Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Shaowen Xu | Fanhao Jia | Ning Dai
Source: npj Computational Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

High throughput discovery of 2D ferromagnetic and multiferroic transition metal oxyhalides and nitrogen halides Two-dimensional (2D) transition metal oxyhalides and nitrogen-halides (TMBXs, where TM = transition metal, B = O-group and N-group elements, X = halogen) have emerged as promising candidates for exploring multiferroic orders and spintronic applications. In this study, we conduct a systematic first-principles high-throughput screening combined with machine learning to identify novel 2D ferromagnetic and multiferroic materials within TMBX family. From a comprehensive dataset comprising 672 TMBX monolayers, we identify 78 ferromagnetic systems, of which 38 exhibit high Curie temperatures (TC ≥ 200 K), significantly expanding the known library of 2D magnetic materials. A machine learning model is developed to elucidate the key factors governing ferromagnetism, revealing that the second-nearest neighbor exchange interaction (J2) plays a dominant role in determining TC. Furthermore, we discovered seven ferromagnetic-ferroelectric multiferroic systems, revealing unique polarization switching pathways. Notably, spin transport simulations using the nonequilibrium Green’s function formalism demonstrate exceptional spin filtering capabilities (~100%) and giant bias-dependent tunneling magnetoresistance (>105%). These findings deepen the fundamental understanding of 2D multiferroics and establish a desired platform for future experimental exploration and the development of next-generation spintronic devices. Multiferroics ; Spintronics ; Ferromagnetism ; Transition metal ; Magnetism ; Curie temperature ; Spin (aerodynamics) 2D Materials and Applications ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: Benhour Amirian | Ashley S. Dale | Sergei V. Kalinin | Jason Hattrick‐Simpers
Source: ArXiv.org (preprint, 2025-11-30)
Field: Materials Science — Materials Chemistry

Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores Accelerated material discovery increasingly relies on artificial intelligence and machine learning, collectively termed "AI/ML". A key challenge in using AI is ensuring that human scientists trust the models are valid and reliable. Accordingly, we define a trustworthy AI framework GIFTERS for materials science and discovery to evaluate whether reported machine learning methods are generalizable, interpretable, fair, transparent, explainable, robust, and stable. Through a critical literature review, we highlight that these are the trustworthiness principles most valued by the materials discovery community. However, we also find that comprehensive approaches to trustworthiness are rarely reported; this is quantified by a median GIFTERS score of 5/7. We observe that Bayesian studies frequently omit fair data practices, while non-Bayesian studies most frequently omit interpretability. Finally, we identify approaches for improving trustworthiness methods in artificial intelligence and machine learning for materials science by considering work accomplished in other scientific disciplines such as healthcare, climate science, and natural language processing with an emphasis on methods that may transfer to materials discovery experiments. By combining these observations, we highlight the necessity of human-in-the-loop, and integrated approaches to bridge the gap between trustworthiness and uncertainty quantification for future directions of materials science research. This ensures that AI/ML methods not only accelerate discovery, but also meet ethical and scientific norms established by the materials discovery community. This work provides a road map for developing trustworthy artificial intelligence systems that will accurately and confidently enable material discovery. Trustworthiness ; Bridge (graph theory) ; Key (lock) ; Scientific discovery ; Bayesian network ; Work (physics) Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Artificial Intelligence in Healthcare and Education
Authors: Adel Alshehri | Aali Alqarni | Abdulrahman Binobaid
Source:  (article, 2025-08-13)
Field: Chemistry — Analytical Chemistry

AI-Driven Milk Fat Classification: Advancing Dairy Analytics in Saudi Arabia This study introduces an innovative use of Artificial Intelligence (AI) applications, such as Machine Learning (ML) and Deep Learning (DL), to accurately and efficiently classify milk samples by their fat content. Utilizing advanced methods such as KMeans Clustering, Logistic Regression, and Multilayer Perceptron (MLP), we developed a comprehensive framework for analyzing Near Infra-Red (NIR) spectral data. Our results demonstrate the superior accuracy of Logistic Regression in binary classification and the remarkable capability of MLP to process complex NIR spectra, outperforming traditional chemometric techniques. Integrating portable spectrometers facilitated large-scale data collection, reducing dependence on costly chemical referencing and enabling model robustness in resource-constrained settings. The proposed approach improves dairy quality control by ensuring consistent products, meeting compliance standards, boosting operational efficiency, and effectively tackling key challenges in the industry. The methodology's adaptability and scalability make it a valuable tool for specialized applications, such as real-time monitoring in embedded systems. This study connects advanced AI innovation with practical applications. It offers a cost-effective and scalable solution to improve milk quality assessment and encourages the broader use of AI in agriculture and food sciences. Adaptability ; Boosting (machine learning) ; Scalability ; Multilayer perceptron ; Analytics ; Logistic regression ; Robustness (evolution) ; Classifier (UML) Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Melamine detection and toxicity
Authors: Menglin Gao | Shengjun Xia | Chunfa Huang | Xing Hu | S.X. Liang | Wenlu Zhang | Ziqiang Wang | Wei Liu | Qiulin Li
Source: International Journal of Pressure Vessels and Piping (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Inferring mechanical behavior from physical properties based on multiscale microstructure evolution and deep learning Workflow ; Creep ; Characterization (materials science) ; Microstructure ; Artificial neural network ; Deep learning ; Multiscale modeling Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Composite Material Mechanics