jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H Park | Gi Beom Sim | Jung Woon Yang | Soohaeng Yoo Willow | D. ChangMo Yang | Chang Woo Myung
Source: The Journal of Physical Chemistry A (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

A Bayesian Committee Machine-Based Force Field for Organic Nitrogen Compounds Large-scale simulations of atomic systems play a vital role in fields, including chemistry, materials science, and more. However, they often struggle with efficiency due to high computational costs. To overcome this, machine learning force fields (MLFFs) have emerged as a promising alternative, providing accuracy comparable to density functional theory (DFT) while lowering costs. Nonetheless, variations in potential energy based on atomic environments make it challenging to apply kernel-based models across different compounds. To address this issue, we present an MLFF using the Robust Bayesian Committee Machine (RBCM) for various compounds containing carbon, nitrogen, and hydrogen (C-N-H). The MLFF is trained with first-principles calculations and molecular dynamics simulations of various C-N-H molecules. Testing these models with longer amine structures and two Diels-Alder reactions shows excellent agreement with DFT results, demonstrating that machine learning models accurately predict potential energy surfaces of organic molecules and offer opportunity for studying a wide range of C-N-H compounds. Field (mathematics) ; Bayesian probability ; Nitrogen ; Force field (fiction) ; Environmental science ; Computer science ; Artificial intelligence ; Environmental chemistry ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Sonam Khattar | Harshit Kumawat | Aryan
Source:  (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Predicting Molecular Properties With Quantum Kernels: a Study on the Qm9 Dataset Predicting molecular properties is a cornerstone of drug discovery, materials science, and quantum chemistry, where accuracy and speed are critical. Classical machine learning mod- els offer rapid predictions but often fail to capture the intricate quantum interactions governing molecular behavior. Our team investigated quantum kernel methods to predict properties of the QM9 dataset, a widely recognized benchmark containing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 3 4, 0 0 0}$</tex> organic molecules with diverse chemical structures. By encoding molecular features into quantum circuits, we mapped classical data into highdimensional Hilbert spaces to enhance prediction accuracy. Our pipeline encompasses data preprocessing, quantum circuit design, model training with support vector regression, and evaluation of ten key molecular properties. The quantum kernel approach delivered promising results, achieving mean absolute errors of 0.13 Debye for dipole moment and 0.08 eV for HOMOLUMO gap, even when executed on noisy quantum devices. This work provides a comprehensive, reproducible pipeline, benchmarks quantum and classical models, and discusses practical challenges, contributing significantly to the field of quantumenhanced computational chemistry. Quantum ; Kernel (algebra) ; Quantum algorithm ; Support vector machine ; Quantum state ; Field (mathematics) ; Encoding (memory) ; Benchmark (surveying) ; Dipole Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Organic Electronics and Photovoltaics
Authors: Shah, Aarav | Sankhala, Vikram Singh
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-11-17)
Field: Materials Science — Materials Chemistry

Mathematical Frameworks and Artificial Intelligence Applications in Drug Discovery and Materials Science Modern artificial intelligence methods increasingly rely on mathematical frameworks that transform complex molecular and material data into computationally manageable representations. Byencoding chemical compounds, biological entities, and material structures into vector, geometric, and probabilistic spaces, these approaches enable efficient model training, predictive analysis,and generative design. This integration supports applications in drug discovery and materialsscience, where capturing spatial arrangements, symmetry invariances, and uncertainty quantification is essential. Advanced techniques such as graph neural networks, equivariant architectures,and topological data analysis contribute to encoding multi-scale structural and functional information. Multimodal data integration combines chemical, biological, and phenotypic inputs toimprove prediction accuracy and interpretability. Challenges arise in constructing shared representation spaces that accommodate domain-specific features while enabling cross-domain transferlearning. Ethical considerations emphasize transparency, interpretability, and risk mitigation inAI-driven pipelines. The synthesis of algebraic, geometric, probabilistic, and topological methodswithin AI frameworks offers a comprehensive foundation for accelerating discovery and innovationin molecular and material sciences. Drug discovery ; Generative grammar ; Probabilistic logic ; Representation (politics) ; Topological data analysis ; Data integration ; Artificial life ; Graph Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Computational Drug Discovery Methods
Authors: Gond, Dominik | Arweiler, Justus | Specht, Thomas | Hasse, Hans | Jirasek, Fabian
Source: arXiv (Cornell University) (preprint, 2025-11-24)
Field: Materials Science — Materials Chemistry

CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, existing sigma-profile libraries are limited in size and inconsistent in quality, which restricts their utility. In this work, we introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a large-scale and internally consistent database providing sigma-profiles for 53091 molecules, along with additional quantum-chemical observables including gas-phase geometries, single-point conductor-like polarizable continuum (C-PCM) data, infrared spectra, ideal-gas heat capacities and entropies, and atomic orbital nuclear magnetic resonance (NMR) shielding tensors. All data were generated using a standardized quantum-chemical workflow based on an wB97X-D/def2-TZVP level of theory. The CHAOS database covers molecules composed of a diverse set of elements, with molecular masses up to 400 amu and dipole moments up to 15 D, and is freely available on Zenodo under an open license. It extends the number of molecules for which sigma-profiles are publicly available by more than an order of magnitude and systematically links them to a broad range of other quantum-chemical molecular descriptors. CHAOS provides a comprehensive and consistent foundation for developing models of molecular and thermodynamic properties -- both physics-based and machine-learning approaches -- across chemistry, chemical engineering, and materials science, greatly extending the possibilities and the available quantum-chemical data basis. Observable ; Dipole ; Molecular dynamics ; Workflow ; Set (abstract data type) ; Dimension (graph theory) ; Polarizability ; Range (aeronautics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: D. Baum | A. Förster | Lucas Visscher
Source: ArXiv.org (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

qs$GW$ quasiparticle and $GW$-BSE excitation energies of 133,885 molecules Machine learning applications in the chemical sciences, especially when based on neural networks, critically depend on the availability of large quantities of high quality data. As they provide excellent accuracy for both charged and neutral excitations, a large dataset containing quasiparticle self-consistent GW (qs$GW$) and Bethe-Salpeter equation (BSE) data would be highly desirable to model excited state energies and properties. In this work, we introduce a dataset for qs$GW$-BSE excitation energies and qs$GW$ quasiparticle energies of unprecedented size. Our dataset, denoted QM9GWBSE, supplies $GW$-BSE singlet-singlet and singlet-triplet excitation energies, corresponding transition dipole moments and oscillator strengths as well as qs$GW$ quasiparticle energies for all molecules from the popular QM9 dataset. We anticipate that QM9GWBSE will provide a solid foundation to train highly accurate machine learning models for the prediction of molecular excited state properties. Quasiparticle ; Excited state ; Excitation ; Dipole ; Molecule ; Foundation (evidence) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Gond, Dominik | Arweiler, Justus | Specht, Thomas | Hasse, Hans | Jirasek, Fabian
Source: ArXiv.org (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

CHAOS - A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, existing sigma-profile libraries are limited in size and inconsistent in quality, which restricts their utility. In this work, we introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a large-scale and internally consistent database providing sigma-profiles for 53091 molecules, along with additional quantum-chemical observables including gas-phase geometries, single-point conductor-like polarizable continuum (C-PCM) data, infrared spectra, ideal-gas heat capacities and entropies, and atomic orbital nuclear magnetic resonance (NMR) shielding tensors. All data were generated using a standardized quantum-chemical workflow based on an wB97X-D/def2-TZVP level of theory. The CHAOS database covers molecules composed of a diverse set of elements, with molar masses up to 400 amu and dipole moments up to 15 D, and is freely available on Zenodo under an open license. It extends the number of molecules for which sigma-profiles are publicly available by more than an order of magnitude and systematically links them to a broad range of other quantum-chemical molecular descriptors. CHAOS provides a comprehensive and consistent foundation for developing models of molecular and thermodynamic properties -- both physics-based and machine-learning approaches -- across chemistry, chemical engineering, and materials science, greatly extending the possibilities and the available quantum-chemical data basis. Observable ; Dipole ; Molecular dynamics ; Workflow ; Set (abstract data type) ; Dimension (graph theory) ; Polarizability ; Range (aeronautics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Diego Ibarra Hoyos | Peter G. Connors | Ho Jang | Nathan Grain | Israel Klich | Gia-Wei Chern | Peter K. Liaw | John R. Scully | S. J. Poon
Source: Research Square (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

From Quantum Annealing to Alloy Discovery: Towards Accelerated Design of High-Entropy Alloys Quantum annealing ; Maxima and minima ; Quantum ; Annealing (glass) ; Simulated annealing ; Alloy ; Qubit Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: D. Li | Zuqi Tang | Chenchen Wang | Jie Mao | Yaohui Bai | Jiuhui Qu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Critical Factors for Carbon Fixation Efficiency in Semiconductor-Biohybrid Systems: Insights from a Meta-Analysis Meta-analysis ; Semiconductor ; Fixation (population genetics) ; Psychology ; Nanotechnology ; Computer science ; Materials science ; Chemistry ; Optoelectronics ; Medicine Electron and X-Ray Spectroscopy Techniques ; Catalytic Processes in Materials Science
Authors: Mustafa Dağ | Ercan Aydoğmuş | Hasan Arslanoğlu | Zehra Gülten Yalçın | Semahat Barlak
Source: Journal of Vinyl and Additive Technology (article, 2025-05-27)
Field: Materials Science — Polymers and Plastics

Development of Polyurethane‐Based Composites With Salt Clay and Industrial Wastes as Fillers: Corrosion, Mechanical Properties, and Machine Learning Insights ABSTRACT In this study, a polyurethane‐based composite is developed by incorporating salt clay, ulexite, colemanite, and various other industrial waste materials. The effects of these fillers on the composite are evaluated and modeled using machine learning techniques. Among the tested models, random forest and neural network demonstrate the highest performance in predicting changes in compressive strength, hardness, and thermal conductivity. The dispersion of salt clay within the polyurethane matrix provides a 300%–500% increase in compressive strength and a 25%–40% improvement in hardness. Ulexite enhances compressive strength by 250%–350% and increases hardness by up to 30%, while colemanite contributes to a 400%–500% rise in compressive strength and a 35%–40% improvement in hardness. The addition of Kırka clay waste and tincal further improves the composite's hardness and overall durability. Fly ash significantly increases compressive strength, although its effect on hardness is limited. The machine learning models effectively capture the relationship between input parameters and composite performance. The random forest model achieves a mean squared error (MSE) of 0.15 for compressive strength and 0.20 for hardness, while the neural network model yields the best results for thermal conductivity prediction with an MSE of 0.12. These findings highlight the potential of the developed composite for industrial applications, particularly in thermal insulation and low‐load structural components. Future studies will focus on evaluating its performance under real‐world conditions and further assessing its long‐term durability. Materials science ; Polyurethane ; Composite material ; Corrosion ; Salt (chemistry) ; Chemistry Polymer composites and self-healing ; Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation
Authors: Jing Liu | Wenyuan Fang
Source: Biomolecules (article, 2025-12-04)
Field: Chemistry — Organic Chemistry

Synthetic Strategies for Bioactive Oligosaccharides Carbohydrates are essential constituents of numerous biological systems, playing key roles in fundamental processes such as cellular recognition and immunological responses, while also offering significant potential in medical diagnostics and pharmaceutical development. However, the structural complexity of naturally occurring carbohydrates-characterized by heterogeneous glycosylation patterns and diverse branching architectures-poses considerable challenges in the isolation and preparation of homogeneous oligosaccharide samples. Recent advances in synthetic chemistry have led to substantial progress in carbohydrate synthesis, with modern glycosylation methodologies achieving improved stereochemical control and enhanced reaction efficiency, thereby enabling the precise and programmable construction of biologically relevant oligosaccharides. This review provides a systematic evaluation of six major strategies for oligosaccharide assembly: One-pot synthesis strategy, orthoganal protection strategy, preactivation strategy, linear and convergent block strategy, programmable one-pot synthesis strategy, and solid-phase synthesis strategy. For each strategy, we examine the key technological innovations, representative applications, and current limitations. Furthermore, the review discusses emerging trends in the field, emphasizing the transformative role of intelligent automation and machine learning in accelerating the discovery and synthesis of complex carbohydrates. Glycosylation ; Branching (polymer chemistry) ; Drug discovery ; Synthetic biology ; Automation ; Homogeneous Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Seaweed-derived Bioactive Compounds
Authors: Hang Zhou | Jie Wu | Jiangtao Yang | Qingyang Fan
Source: Crystals (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms The third-generation semiconductors have the characteristics of a large bandgap, a high breakdown electric field, a fast electron saturation rate, high-temperature resistance, corrosion resistance, and radiation resistance, making them the preferred core materials and devices for cutting-edge high-tech fields, such as mobile communication, new energy vehicles, and smart grids in the future. The III–V compound semiconductors are a typical representative of them. In order to discover and explore new III–V semiconductor materials more efficiently and accurately, this paper adopts a machine-learning method optimized by the beetle algorithm and combined with first-principle calculation verification to efficiently and accurately predict the performance of III–V nitride materials and study their physicochemical properties. This study improved the prediction efficiency of nitrogen-rich III–V semiconductor materials through the combination of machine learning and first principles, providing a new approach for the efficient and accurate prediction of semiconductor materials. Computer science ; Nitrogen ; Algorithm ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Geraldine Sánchez-Dueñez | Wladimiro Dı́az-Villanueva | Jorge Escorihuela | Laura Francés‐Soriano | Julia Pérez‐Prieto
Source: ACS Omega (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Predicting the Optical Properties of Gold Nanoclusters Using Machine Learning Approach The synthesis of gold nanoclusters (AuNC) is strongly influenced by various reaction conditions, and their optical properties are determined by factors such as the nature of the ligand and the measuring solvent, among others. To improve the efficiency of the synthesis of metallic gold nanoclusters with the desired functionality, the application of machine learning techniques is a smart choice. In this study, a model based on the GXBoost algorithm is proposed to predict the maximum emission wavelength of the AuNC emission from a database that includes more than 200 scientific articles. The validation of the model was carried out through the comparison of prediction versus experimental data (not included in the model) and the training and validation data. The model showed a percentage error of 1.7, 1.6, and 4.9%, respectively, indicating a reasonable return. Additionally, an independent regression was performed when the ligand was a thiolated compound different from GSH, obtaining a training and test error percentage of 0.01 and 3%, respectively. In addition, critical variables affecting the optical properties of nanoclusters were explored, and techniques such as One-Hot Encoding were used to prepare the data. Finally, this work not only underscores the relevance of AuNCs in modern science highlighted but also demonstrates the potential of machine learning as a predicting tool and design of materials in nanoscience, contributing to the optimization of their properties for future applications. Nanoclusters ; Relevance (law) ; Experimental data ; Training set ; Test data ; Gold standard (test) Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science
Authors: Wanhui Wen | Fanda Zeng | Ben Xu | Ke Bi | Zhipeng Xing | Hao‐Cheng Thong | Ke Wang
Source: ArXiv.org (preprint, 2025-05-08)
Field: Materials Science — Materials Chemistry

Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics Atomistic control of phase boundaries is crucial for optimizing the functional properties of solid-solution ferroelectrics, yet their microstructural mechanisms remain elusive. Here, we harness machine-learning-driven molecular dynamics to resolve the phase boundary behavior in the KNbO3-KTaO3 (KNTO) system. Our simulations reveal that chemical composition and ordering enable precise modulation of polymorphic phase boundaries (PPBs), offering a versatile pathway for materials engineering. Diffused PPBs and polar nano regions, predicted by our model, highly match with experiments, underscoring the fidelity of the machine-learning atomistic simulation. Crucially, we identify elastic and electrostatic mismatches between ferroelectric KNbO3 and paraelectric KTaO3 as the driving forces behind complex microstructural evolution. This work not only resolves the longstanding microstructural debate but also establishes a generalizable framework for phase boundary engineering toward next-generation high-performance ferroelectrics. Molecular dynamics ; Phase (matter) ; Boundary (topology) ; Work (physics) ; Phase boundary ; Ferroelectricity ; Dielectric ; Polar Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Mohammadreza Elhaie | Abolfazl Koozari | Hamidreza Koohi | Salman Mohammed Albeshan
Source: Journal of Medical Imaging and Interventional Radiology (review, 2025-10-17)
Field: Materials Science — Materials Chemistry

Amide proton transfer imaging with machine learning and deep learning for malignant tumor diagnosis: a systematic review Abstract Background Amide Proton Transfer (APT) imaging, a molecular MRI technique under the chemical exchange saturation transfer (CEST) framework, offers a non-invasive means of detecting malignant tumors by assessing biochemical alterations without contrast agents. Integration with machine learning (ML) and deep learning (DL) enhances diagnostic precision and tumor characterization. Objective To systematically review and synthesize current evidence on the combined use of APT imaging with ML/DL for the diagnosis and characterization of malignant tumors. Methods A systematic review was conducted following PRISMA guidelines. Five databases and gray literature sources were searched. Studies were included if they involved malignant tumors, utilized APT imaging combined with ML or DL, and reported diagnostic or predictive outcomes. Quality assessment was performed using QUADAS-2 and Radiomics Quality Score (RQS) 2.0. Results Seven studies met inclusion criteria, primarily focusing on gliomas. ML/DL algorithms employed included radiomics-based classifiers, CNNs, and neural networks. APT imaging combined with ML/DL achieved high diagnostic accuracy (AUC: 0.82–0.97) for predicting molecular markers (IDH, 1p/19q, H3K27M), treatment response, and differentiating progression from pseudoprogression. Risk of bias was high in patient selection across studies, with limited generalizability due to retrospective, single-center designs and small sample sizes. Conclusions APT imaging, when integrated with ML/DL, shows promise as a non-invasive, contrast-free method for diagnosing and characterizing gliomas. The combined approach improves accuracy in molecular profiling and treatment assessment. However, broader validation, prospective studies, and application to other tumor types are necessary for clinical translation. Generalizability theory ; Deep learning ; Transfer of learning ; Radiomics ; Cancer imaging ; Diagnostic accuracy Lanthanide and Transition Metal Complexes ; Medical Imaging Techniques and Applications ; Advanced MRI Techniques and Applications