jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manish Tomar | Sunil Prajapat | Dheeraj Kumar | Pankaj Kumar | Rajesh Kumar | Athanasios V. Vasilakos
Source: Mathematics (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Exploring the Role of Material Science in Advancing Quantum Machine Learning: A Scientometric Study Quantum Machine Learning (QML) opens up exciting possibilities for tackling problems that are incredibly complex and consume a lot of time. The drive to make QML a reality has sparked significant progress in material science, inspiring a growing number of research publications in the field. In this study, we extracted articles from the Scopus database to understand the contribution of material science in the advancement of QML. This scientometric analysis accumulated 1926 extracted publications published over 11 years spanning from 2014 to 2024. A total of 55 countries contributed to this domain of QML, among which the top 10 countries contributed 65.7% out of the total number of publications; the USA is on top, with 19.47% of the publications globally. A total of 57 authors contributed to this research area from 55 different countries. From 2014 to 2024, publications had an average citation impact of 32.12 citations per paper; the year 2015 received 16.7% of the total citations, which is the highest in the 11 years, and the year 2014 had the highest number of citations per paper, which is 61.4% of the total. The study also identifies the most significant document in the year 2017, with the source title Journal of Physics Condensed Matter, having a citation count of 2649 and a normalized citation impact index (NCII) of 91.34. Nanotechnology ; Psychology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yangjun Wu | Wenbin Cao | Jiacheng Zhao | Honghui Shang
Source: IEEE Transactions on Parallel and Distributed Systems (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Fast and Scalable Neural Network Quantum States Method for Molecular Potential Energy Surfaces The Neural Network Quantum States (NNQS) method is highly promising for accurately solving the Schrödinger equation, yet it encounters challenges such as computational demands and slow rates of convergence. To address the high computational requirements, we introduce optimizations including a cross-sample KV cache sharing technique to enhance sampling efficiency, Quantum Bitwise and BloomHash methods for more efficient local energy computation, and mixed-precision training strategies to boost computational efficiency. To overcome the issue of slow convergence, we propose a parallel training algorithm for NNQS under second quantization to accelerate the training of base models for molecular potential surfaces. Our approach achieves up to 27-fold acceleration specifically in local energy calculations in systems with 154 spin orbitals and demonstrates strong and weak scaling efficiencies of 98% and 97%, respectively, on the H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> O<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> potential surface training set. The parallelized implementation of transformer-based NNQS is highly portable on various high-performance computing architectures, offering new perspectives on quantum chemistry simulations. Computer science ; Scalability ; Artificial neural network ; Quantum ; Quantum computer ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Yan Wang | Jinxu Liu | Xuemei Liu | Xinya Feng | Yifan Du | Jie Cao
Source: Materials (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Revealing the Influence of Material Properties of Shaped Charge Liner on Penetration Performance via Numerical Simulation and Machine Learning The metallic shaped charge liner (SCL) is widely utilized in the defense industry, oil perforation, cutting, and other industrial fields due to the powerful penetration performance. However, quantitative law and underlying mechanisms of material properties affecting SCL penetration performance are unclear. Based on the real and virtual material properties, by combining numerical simulation with machine learning, the influence of material properties on SCL penetration performance was systematically studied. The findings in the present work provided new insights into the penetration mechanism and corresponding influencing factors of the metal jet. It indicated that penetration depth was dominated by the melting point, specific heat, and density of the SCL materials rather than the conventionally perceived plasticity and sound velocity. Average perforation diameter was dominated by the density and plasticity of the SCL materials. Particularly, the temperature rise and thermal softening effect of the SCL controlled by the melting point and specific heat have a significant effect on the "self-consumption" of the metal jet and further on the penetration ability. Additionally, the density of the SCL influences the penetration depth deeply via dynamic pressure of the jet, but the influence of density on penetration depth decreases with the increase in density. The correlation between the key properties and penetration performance was obtained according to a quadratic polynomial regression algorithm, by which the penetration potential of SCL materials can be quantitatively evaluated. Overall, the present study provides a new SCL material evaluation and design method, which can help to expand the traditional penetration regime of the SCL in terms of the penetration depth and perforation and is expected to be used for overcoming the pierced and lateral enhancement trade-off. Penetration (warfare) ; Materials science ; Shaped charge ; Composite material ; Mechanical engineering ; Engineering ; Chemistry High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Metal and Thin Film Mechanics
Authors: S. Krishnan | Zehua Pan | Zheng Zhong | Zilin Yan
Source: Advanced Theory and Simulations (review, 2025-12-12)
Field: Materials Science — Materials Chemistry

Leveraging DFT Calculations and Machine Learning toward Materials Innovations for Proton Ceramic Fuel Cells (PCFCs): A Comprehensive Review ABSTRACT Protonic ceramic fuel cells (PCFCs) represent a significant advancement in fuel cell technology due to their ability to operate at intermediate temperatures, offering enhanced efficiency and reduced material degradation compared to traditional high‐temperature oxygen ion conducting solid oxide fuel cells (O‐SOFCs). While PCFCs hold immense potential for commercialization, their material innovation remains a critical bottleneck to achieving widespread viability. This comprehensive review explores the synergistic integration of density functional theory (DFT) based calculations with machine learning (ML) methodologies, illuminating their collective impact on accelerating PCFC material development. Combination of DFT's atomic‐level precision in material property prediction with ML's sophisticated predictive algorithms, creates a powerful framework for exploring vast compositional spaces of critical materials, including perovskite oxides, double perovskite oxides, Ruddlesden‐Popper oxides, and other similar systems. The integration not only enhances computational efficiency but also enables the systematic investigation of complex structure–property relationships essential for advancing PCFC technology. The review methodically examines three interconnected themes: First, it delves into the cutting‐edge strategies and material developments that have propelled recent advances in PCFC applications; second, it analyzes DFT's pivotal role in facilitating PCFC progress through accurate atomic‐scale modeling; and third, it elucidates the revolutionary impact of ML integration with DFT methodologies and its implications for PCFC developments. By focusing on seminal contributions within each domain, this work provides a strategic perspective on the convergence of computational chemistry and ML in PCFC's future advancements. Bottleneck ; Fuel cells ; Ceramic ; Perovskite (structure) ; Convergence (economics) ; Work (physics) Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion
Authors: Lion Frangoulis | Zahra Khatibi | Lorenzo A. Mariano | Alessandro Lunghi
Source: JACS Au (article, 2025-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generating New Coordination Compounds via Multireference Simulations, Genetic Algorithms, and Machine Learning: The Case of Co(II) and Dy(III) Molecular Magnets The design of coordination compounds with target properties often requires years of continuous feedback loop between theory, simulations, and experiments. In the case of magnetic molecules, this conventional strategy has indeed led to the breakthrough of single-molecule magnets with working temperatures above liquid nitrogen's boiling point, but at significant costs in terms of resources and time. Here, we propose a computational strategy capable of accelerating the discovery of new coordination compounds with the desired electronic and magnetic properties. Our approach is based on a combination of high-throughput multireference ab initio methods, genetic algorithms, and machine learning. While genetic algorithms allow for an intelligent sampling of the vast chemical space available, machine learning reduces the computational cost by prescreening molecular properties in advance of their accurate and automated multireference ab initio characterization. Importantly, the presented framework is able to generate novel organic ligands and explore chemical motifs beyond those available in pre-existing structural databases. We showcase the power of this approach by automatically generating new Co-(II) and Dy-(III) mononuclear coordination compounds with record magnetic properties in a fraction of the time required by either experiments or brute-force ab initio approaches. In the case of Dy compounds, simulations uncover new nontrivial chemical strategies toward pentagonal bipyramidal complexes with record-breaking values of magnetic anisotropy. Magnet ; Genetic algorithm ; Molecular magnets ; Computer science ; Chemistry ; Physics ; Machine learning ; Quantum mechanics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Advanced NMR Techniques and Applications
Authors: Y YAO | Jun-Tao Zhu | Yan Liu | Guanpeng Ren | Xiaoyan Li | Pengfei Ou
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Large Language Models for Heterogeneous Catalysis ABSTRACT Heterogeneous catalysis has a wide range of applications in chemical manufacturing and sustainable technologies. It uses solid catalysis to enable efficient chemical transformations. Traditional research on active sites and reaction mechanisms relies heavily on experiments and computational methods, such as density functional theory calculations. However, the volume of scientific literature and data is growing fast. This rapid growth has made it increasingly difficult to capture, process, and act on emerging insights systematically. Recently, large language models (LLMs) have emerged as powerful tools to support various stages in catalysis research. Their ability to understand and generate natural language helps them extract useful information from vast amounts of text, assist in catalyst design, aid in planning experiments, and clarify complex descriptors. In this advanced review, we first analyze recent progress in applying LLMs to heterogeneous catalysis, focusing on four key areas: literature mining and knowledge extraction, catalyst design and screening, experiment automation and workflow optimization, and the interpretation of high‐dimensional descriptors. We then highlight the challenges in this field despite these advances, most notably the need for domain‐specific fine‐tuning and the improvement of molecular representation. We conclude by discussing future opportunities for integrating LLMs with complementary machine learning approaches and expert‐in‐the‐loop systems, toward accelerating the rational discovery of next‐generation catalysts. This article is categorized under: Data Science &gt; Artificial Intelligence/Machine Learning Data Science &gt; Chemoinformatics Theoretical and Physical Chemistry &gt; Reaction Dynamics and Kinetics Workflow ; Field (mathematics) ; Heterogeneous catalysis ; Automation ; Interpretation (philosophy) ; Natural language Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Hydrodesulfurization Studies
Authors: Fangning Ren | Xu Chen | Fang Liu
Source: The Journal of Physical Chemistry Letters (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms' approximations. Our model accurately predicted the excitation energies of the trimer and tetramer of perylene and tetracene and estimated S<sub>1</sub> oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different couplings on their size dependency. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies. Excited state ; Exciton ; Materials science ; State (computer science) ; Computer science ; Statistical physics ; Physics ; Atomic physics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Perovskite Materials and Applications
Authors: Izabela Miturska | Katarzyna Antosz
Source: Materials (article, 2025-06-14)
Field: Materials Science — Materials Chemistry

Epoxy Adhesive Materials as Protective Coatings: Strength Property Analysis Using Machine Learning Algorithms This study analyzed the mechanical properties of epoxy adhesive materials used as functional coatings, focusing on how physical modifications impact their microstructure and strength. Compositions based on Epidian 5, 53 and 57 resins were cured using TFF, Z-1, or PAC curing agents and modified with various fillers: mineral (CaCO<sub>3</sub> calcium carbonate), active (activated carbon filler, CWZ-22), and nanostructured (montmorillonite, ZR-2) fillers. The best results were achieved with calcium carbonate (10-20 wt%) in Epidian 5 or 53 resins cured with TFF or Z-1, yielding tensile strength up to 64 MPa, compressive strength up to 145 MPa, and bending strength up to 123 MPa. Activated carbon and nanofillers showed moderate improvements, particularly in more flexible matrices. To support property prediction, machine learning algorithms were applied and successfully modeled the mechanical behavior based on composition data. The most accurate models reached R<sup>2</sup> values of 0.93-0.95 for compression and bending strength. While the models for compression and bending strength demonstrated high accuracy, the tensile strength model yielded lower predictive performance, indicating that further refinement and expanded input features are necessary. Shapley analysis further identified curing agents and fillers as key predictive features. This integrated experimental and data-driven approach offers an effective framework for optimizing epoxy-based coatings in industrial applications. Epoxy ; Adhesive ; Materials science ; Composite material ; Epoxy adhesive ; Property (philosophy) ; Algorithm ; Computer science Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Injection Molding Process and Properties
Authors: Sathish Marimuthu | Andrews Nirmala Grace
Source: ACS Applied Energy Materials (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Operando Evaluation of NIR-Absorbing Squaraine Molecules for Dye-Sensitized Solar Cells: An Informatics-Driven Molecular Machine Learning Framework Near-infrared (NIR) absorbing dyes are crucial for enhancing the performance and transparency of dye-sensitized solar cells (DSSCs), offering a pathway toward more efficient, aesthetically compatible, and versatile photovoltaic systems. Identifying such dyes could represent a quantum leap in the field of photovoltaics, overcoming current limitations in efficiency and spectrum utilization. This study presents an informatics-driven molecular machine learning framework, leveraging operando methodologies to identify optimal NIR dyes. From a data set of 128 squaraine dyes, molecular descriptors were refined from 208 to 81 features using correlation and variance filtering. Among 42 models evaluated, a hybrid framework combining XGBoost and ExtraTrees Regressor achieved high predictive accuracy (R2 = 0.85, RMSE = 18.8, MAE = 12.24), capturing complex structure–property relationships. The framework identified NIR_D19, NIR_D18, and NIR_D15 as top-performing dyes. Molecular similarity analyses using Morgan fingerprints, Taylor-Butina clustering, and Tanimoto metrics revealed structural insights and key functional fragments influencing absorption. Validated against experimental data, this robust and scalable framework accelerates NIR dye discovery, advancing DSSC technologies for transparent and efficient photovoltaics. Dye-sensitized solar cell ; Informatics ; Molecule ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Engineering ; Organic chemistry ; Physical chemistry ; Electrical engineering Machine Learning in Materials Science ; Electrochemical sensors and biosensors ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Guo-Yong Shi | Zhicheng Zhong | Yuehua Su | Ri He | Chao Zhang
Source: Physica Scripta (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Investigating the atomic-level mechanisms of negative thermal expansion in Zn(CN)<sub>2</sub> using machine learning-enabled molecular dynamics Abstract The negative thermal expansion behavior (NTE) of Zn(CN) 2 over a wide range of temperature has received significant attention. Here, we develop a machine learning interatomic potential and perform large-scale molecular dynamics simulations through which we unveil the NTE mechanisms of Zn(CN) 2 . Analysis of the trajectories of atoms reveal that the C and N atoms move on the lateral side of Zn…Zn axis with different distance. The C≡N bond is not exactly parallel to the Zn…Zn axis. The bridging C and N atoms exhibit a unique motion behavior that combines lateral movement with a tilted aspect, which we term the ‘lateral + tilt’ mode. The transition pressure from cubic to orthorhombic phase under ambient temperature is found to be 2.7 GPa. Machine learning-enabled molecular dynamics open a way to better comprehend the physical origins of NTE. Molecular dynamics ; Dynamics (music) ; Thermal ; Materials science ; Chemical physics ; Physics ; Thermodynamics ; Quantum mechanics Thermal Expansion and Ionic Conductivity ; Thermal and Kinetic Analysis ; Thermal properties of materials
Authors: Shichen Zhang | Jun Zhu | Yi Zeng | Huanxiao Mai | Dong Wang | Xiaoyan Zheng
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Interpretable prediction of aggregation-induced emission molecules based on graph neural networks We developed an interpretable graph neural network (96.4% accuracy) for AIEgen identification, revealing 24 characteristic functional groups. Based on these insights, two virtual library strategies (self-fragment and donor-acceptor docking) were proposed and predicted four experimentally confirmed AIEgens successfully, which establishes a rational design framework for AIE materials. Aggregation-induced emission ; Artificial neural network ; Molecule ; Chemistry ; Graph ; Computer science ; Artificial intelligence ; Biological system ; Theoretical computer science ; Organic chemistry ; Physics ; Biology ; Fluorescence Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Advanced Chemical Sensor Technologies
Authors: Minglang Dong | Fangfang Ding | Ya-Nan Jin | Chi Li | Xiang Lin | Shuang Lin
Source: Analytical Chemistry (article, 2025-05-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Facile Synthesis of Ultrasmooth Au Nanospheres into Macroscopic 3D Supercrystals for Machine-Learning-Driven Analysis of Thiram in Soil Self-assembly of nanocrystals into a macroscopic 3D superlattice has emerged as a promising nanostructure due to its collective optical and electrical properties. In this work, a macroscale supercrystal was constructed under constant temperature and humidity with an ultrasmooth Au nanosphere (NS) as a building block synthesized by a facile route. In particular, a reproducible and facile strategy was developed to fabricate highly spherical Au NSs with nanoscale size tenability. Importantly, this method does not require a syringe pump and breaks the limitation of instrumentation for the synthesis of Au NS, significantly contributing to the promotion and popularization of Au NSs. Meanwhile, an operable and cost-effective droplet slow evaporation self-assembly approach was applied to form a homogeneous 3D superlattice array against coffee rings with an enhancement factor as high as 1.37 × 10<sup>8</sup> verified by FDTD simulation results. Furthermore, integrating a convolutional neural network (CNN) model with the ultrasensitive 3D superlattice SERS platform, the precise and wide-range concentration prediction of the pesticide thiram in soil was successfully demonstrated, breaking the limitation of high-concentration saturation in the conventional concentration-intensity fitting curve. Consequently, this innovative Au NS 3D supercrystal facilitates the fabrication of novel nanoassemblies with ideal plasmonic functions for extensive applications in the fields of food safety, environment, medicine, and biology. Chemistry ; Thiram ; Chemical engineering ; Nanotechnology ; Ecology Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications
Authors: Hoang-Giang Nguyen | Sheng‐Joue Young | Thanh-Dung Le | Chi-Ngon Nguyen | Loc Do | Thi Nhu Lan Nguyen | Te‐Hua Fang
Source: Journal of Non-Crystalline Solids (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulation and machine learning to predict mechanical behavior of Cu/Zr multilayer nanofilms under tension-compression Molecular dynamics ; Tension (geology) ; Compression (physics) ; Materials science ; Dynamics (music) ; Mechanical compression ; Composite material ; Biological system ; Computer science ; Chemistry ; Physics ; Engineering ; Biomedical engineering ; Computational chemistry ; Acoustics Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Advanced materials and composites