jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tomoya Gake | Daisuke Hirai | S. Hirose
Source: Physical Review Materials (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Computational exploration of novel polar oxides: From composition selection to crystal structure prediction Materials have various degrees of freedom in their composition and structure, resulting in a large chemical space characterized by these combinations. To accelerate the discovery of promising, unreported materials, methods for identifying favorable compositions and their stable structures efficiently are required. In this study, we propose a crystal structure prediction method that combines low computational cost with high accuracy. This approach integrates symmetry-constrained random structure generation, a universal machine-learning interatomic potential, and first-principles calculations. Reproducibility tests conducted on the crystal structures of eight reported compositions demonstrate the efficacy of our method. To identify promising compositions from the vast chemical space strategically, we developed a machine learning model that estimates the existence probability of unreported compositions using information from reported compositions. Validation involved treating the reported compositions as unreported, achieving an 88% reproducibility rate. As an application of our approach, we explored novel ternary oxides having polar structures by incorporating an additional machine learning model to predict their polarity based on compositional information. Crystal structure prediction was carried out for 18 unreported compositions, each predicted by machine learning to have an existence probability exceeding 0.9 and polar structures. Our exploration yielded three candidates for novel polar oxides that are thermodynamically and dynamically stable. Further, each candidate has an unreported structure, underscoring the potential of our approach to expand chemical space. Polar ; Ternary operation ; Crystal structure ; Crystal structure prediction ; Polarity (international relations) ; Chemical space ; Crystal (programming language) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Marco Gibaldi | Jun Luo | Andrew J. P. White | Rebeca Mayo | Cécile Pereira | Tom K. Woo
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Generalizable classification of crystal structure error types using graph attention networks Graph neural network models classifying errors ( e.g. , omitted hydrogens, charge imbalance, disorder) in crystal structure databases are developed which attain high accuracy and generalizable performance across various domains of chemistry. Computer science ; Artificial intelligence ; Graph ; Pattern recognition (psychology) ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yuan Zhou | Jian Li | Lan‐Xin Ye | Xin‐Long Lin | Jiawei Huang | Yu‐Xi Tian | Zi‐Tao Jiang | Yan Li | Miao Yang | Hong‐Xia Liu | Zhi‐Chuan J. Xu | Qi‐Xun Shi | Chuan Wang
Source: Rare Metals (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

High Performance Catalyst Design by Artificial Intelligence for Acetylene Hydrochlorination ABSTRACT The rapid development of artificial intelligence (AI) has brought transformative contributions to society, revolutionizing various fields through its advanced computational capabilities and data‐driven approaches. In the field of materials science, AI has become a powerful tool that significantly accelerates the discovery of new materials by providing predictive insights, optimizing workflows, and guiding experimental design. These advancements have paved the way for more efficient and sustainable innovation processes. Herein, we developed a model called AI‐driven ligand screening model based on a deep neural network to guide the catalyst design. With an accuracy of 81% on the validation dataset, the model screened 8033 ligands and identified 31 promising candidates for catalyst modification. By utilizing this AI‐driven approach, we successfully developed two mercury‐free acetylene hydrochlorination catalysts with excellent catalytic performance (95% vinyl chloride yield). This work highlights the potential of AI in simplifying catalyst development and enhancing material performance. The AI‐driven ligand screening model demonstrates its capability to accelerate the discovery process, offering a high‐efficiency framework for optimizing catalysts and advancing sustainable materials research. Catalysis ; Acetylene ; Work (physics) ; Artificial neural network ; Field (mathematics) ; Transformative learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Xianglin Liu | Kai Yang | Yongxiang Liu | Fei Zhou | Dengdong Fan | Zongrui Pei | Pengxiang Xu | Yonghong Tian
Source: arXiv (Cornell University) (preprint, 2025-03-16)
Field: Materials Science — Materials Chemistry

Revealing Nanostructures in High-Entropy Alloys via Machine-Learning Accelerated Scalable Monte Carlo Simulation The computational cost of traditional first-principles method quickly becomes prohibitively expensive as the number of atoms increases. This challenge is further amplified by the need to evaluate finite-temperature properties with Monte Carlo (MC) simulations, which is inherently challenging to parallelize due to sequential Markov chain updates. Here, we introduce Scalable Monte Carlo (SMC), an efficient MC simulation method that overcomes the parallelization bottlenecks in conventional MC simulation, reducing the computational complexity of a MC sweep from quadratic to linear. We present a GPU implementation of the SMC method, SMC-GPU, which simultaneously harnesses the thousands of processing cores on a GPU to accelerate the computation. By adopting a data-driven workflow that surrogates the computationally expensive density functional theory (DFT) with ML models, we demonstrate that SMC-GPU is capable of simulating systems of more than one-billion atoms, while maintaining the accuracy of first-principles methods. Using this unprecedented capability, we performed billion-atom MC simulations to investigate the nanostructure evolution of two important high-entropy alloys (HEAs), FeCoNiAlTi and MoNbTaW, in which the nanostructures are believed to be responsible for their superb mechanical properties. Our results reveal a rich diversity of nanostructures, including nanoparticles (NP), 3D-connected NP, and disorder protected nanophases. We quantitatively analyze the size, composition, and morphology of the nanostructures, as well as directly simulate the atom-probe-tomography (APT) needle. The results align well with available experimental observations. This work underscores the promising potential of leveraging large-scale MC simulation to explore the largely uncharted territory of nanostructure evolution in HEAs. Monte Carlo method ; Scalability ; High entropy alloys ; Statistical physics ; Nanostructure ; Computer science ; Kinetic Monte Carlo ; Entropy (arrow of time) ; Materials science ; Nanotechnology ; Physics ; Thermodynamics ; Mathematics ; Alloy ; Metallurgy ; Statistics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Joseph Chakar | Yvan Bonnassieux | Stéfania Cacovich | J.P. Connolly | Jean‐Paul Kleider | Jean‐François Guillemoles | Jean-Baptiste Puel | Philip Schulz
Source: SPIRE - Sciences Po Institutional REpository (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Fast-Tracking Solar Cell Materials Discovery Using Automation, Physics Modeling, and Bayesian Machine Learning International audience Bayesian probability ; Bayesian inference ; Bayesian optimization ; Bayesian statistics ; Statistical learning Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Silicon and Solar Cell Technologies
Authors: Seunghun Jang | Gyoung S. Na
Source: Journal of Cheminformatics (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

A quantum chemical dataset of interacting molecular pairs for chemical reaction studies Understanding molecular interactions beyond single-molecule properties is critical for studying real-world chemical systems. Quantum chemical calculations of molecule-molecule interactions are computationally demanding, making large, publicly available datasets scarce. Here, we present an efficient framework for generating initial configurations of molecular interaction systems and construct a molecular interaction dataset, containing 49,620 individual molecules and 247,741 molecular pairs spanning chromophore-solvent, solute-solvent, and drug-drug interactions, each associated with experimentally characterized equilibrium structures. Our dataset can be used for theoretical studies and machine learning applications in chemical sciences, particularly for modeling intermolecular interactions and structure-based prediction of experimental properties. In future work, we plan to expand the dataset to include non-equilibrium structures and atomic forces, thereby broadening its applicability to reaction modeling and force field development. Quantum chemical ; Intermolecular force ; Construct (python library) ; Force field (fiction) ; Molecule ; Field (mathematics) ; Molecular dynamics ; Chemical reaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Shinde Vinita
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Anisotropic Growth Prediction: A Data-Driven Framework for Controlled Crystal Morphology Anisotropy ; Crystal (programming language) ; Morphology (biology) ; Work (physics) ; Crystal plasticity Machine Learning in Materials Science ; Enzyme Structure and Function ; Crystallization and Solubility Studies
Authors: Gian‐Marco Rignanese
Source:  (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Response properties of inorganic materials from high-throughput density-functional perturbation theory and machine-learning Perturbation theory (quantum mechanics) ; Perturbation (astronomy) ; Work (physics) ; Density functional theory Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: M. Nigar Fathima | Prasanna Daasi | V Saili | Sirisha T
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence, Machine Learning, and Generative AI in Synthetic Planning and Drug Discovery Artificial Intelligence (AI) and Machine Learning (ML) have come up as transformative technologies in the pharmaceutical sciences, particularly in synthetic route planning and early-stage drug discovery.ML models can predict retrosynthetic pathways, optimize reaction conditions, and reduce trial-and-error in laboratory synthesis. Beyond their role in synthesis, AI-driven tools are now widely used to analyze complex biological and chemical data, helping researchers identify viable drug candidates at much earlier stages. Techniques such as virtual screening, predictive modeling, and structure–activity analysis allow scientists to make informed decisions before entering costly experimental phases. These methods also support the evaluation of safety, efficacy, and pharmacokinetic behavior, improving the overall quality of candidate selection. At the same time, Generative AI (GAI) is revolutionizing drug discovery by designing novel molecular structures with desired biological properties using deep learning algorithms such as generative adversarial networks (GANs) and transformer-based models. These intelligent systems not only accelerate the drug development timeline but also enhance decision-making in lead optimization and target validation. This integrated AI-driven approach significantly reduces cost, time and resources, enabling the way for faster and more efficient drug innovation. Drug discovery ; Generative grammar ; Timeline ; Transformative learning ; Drug candidate ; Generative adversarial network ; Deep learning ; Virtual screening Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Chuanguo Zhang | Fan Cheng | Qirong Zheng | Zhi Zeng | Yonggang Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Primary Radiation Displacements and Damage in Uranium Mononitride: A Deep Neural Network Molecular Dynamics Study Uranium ; Primary (astronomy) ; Molecular dynamics ; Dynamics (music) ; Radiation ; Artificial neural network ; Radiation damage ; Materials science ; Chemistry ; Computer science ; Physics ; Artificial intelligence ; Nuclear physics ; Metallurgy ; Computational chemistry ; Acoustics ; Astrophysics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Tang, Siwen | Li, Jiajia | Cao, Yun | Zhou, Guobing
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Understanding the thermal conductivity of LiF and Li2O components in solid electrolyte interphase using machine learning-based interatomic potential Interphase ; Thermal conductivity ; Electrolyte ; Interatomic potential ; Conductivity ; Thermal Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: 学 伊原 | Manabu Ihara | Sergei Manzhos
Source: Institutional Repositories DataBase (IRDB) (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Effects of nonlinearity and inter-feature coupling in machine learning studies of Nb alloys with center-environment features Coupling (piping) ; Nonlinear system ; Stability (learning theory) ; Feature (linguistics) ; Artificial neural network Machine Learning in Materials Science ; Metallurgy and Material Forming ; Titanium Alloys Microstructure and Properties
Authors: Zahran, Hussein
Source: Ghent University Academic Bibliography (Ghent University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of fatigue behaviour in fusion and generation IV reactor materials : numerical, experimental, and machine learning study Fusion ; Expert system ; Sensor fusion ; Support vector machine ; Artificial neural network Fusion materials and technologies ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: 山村 貴史 | Takashi Yamamura | Kyoshiro Kanamori | Tetsu Nagata | Tadashi Hongou | 住谷 明 | Akira Sumitani | 齊藤 卓志 | Takushi Saito
Source: Tokyo Tech Research Repository (Tokyo Institute of Technology) (article, 2025-10-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Temperature measurement using thermochromic liquid crystals with scattered light intensity measurements and machine learning Temperature measurement ; Thermochromism ; Intensity (physics) ; Light intensity ; Liquid crystal ; Light scattering Liquid Crystal Research Advancements ; Phase-change materials and chalcogenides ; Surface Roughness and Optical Measurements
Authors: Bo Zhao | Dong Wang | Shurui Shi | Zhengwei Ding | Zesen Wei
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Atomic-Level Insights into Ticn Via Machine Learning Force Field Molecular Dynamics Simulations Molecular dynamics ; Force field (fiction) ; Dynamics (music) ; Field (mathematics) ; Atomic force microscopy ; Computer science ; Nanotechnology ; Materials science ; Chemical physics ; Physics ; Artificial intelligence ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced materials and composites
Authors: Yuanming Song | Min Dai
Source: ChemRxiv (preprint, 2025-11-28)
Field: Materials Science — Biomaterials

Redox-responsive Peptide Screening via Deep Learning, Molecular Dynamics, and Network Hamiltonian Models Peptide ; Molecular biophysics ; Ideal (ethics) ; Biological network ; Molecular dynamics ; Protocol (science) ; Experimental data Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions
Authors: A. A. Afshari
Source: ChemRxiv (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Beyond Prediction: Mechanistic Elucidation of Molecular Drivers for Aqueous Solubility Using Interpretable Machine Learning Interpretability ; Solubility ; Polar surface area ; Molecular descriptor ; Solvation ; Outlier ; COSMO-RS ; Support vector machine ; Quantitative structure–activity relationship ; Partition coefficient Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shang Wang | Guang Chen | Lidan Lu | Lianqing Zhu
Source: Optics Express (article, 2025-11-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design and spectral reconstruction of computational multispectral metasurfaces using deep learning and genetic algorithms Currently, the key challenge in designing computational multi-spectral metasurfaces in the long-wave infrared (LWIR) region mainly lies in identifying structures with minimally correlated transmission spectra. Traditionally, predefined materials and geometries have been relied on to generate spectral responses. Despite recent advancements in inverse design methodologies, the applications of hybrid deep learning combined with genetic algorithms in gallium antimonide (GaSb)-based photonic crystal structures have not been sufficiently investigated. GaSb possesses low optical loss and a high refractive index in the long-wave infrared region, and is compatible with III-V semiconductor fabrication techniques, enabling integration with existing optoelectronic components. Therefore, we propose an end-to-end inverse design framework using deep neural networks to optimize the photonic crystal design, which encodes the geometric parameters of the photonic crystal to identify metasurfaces using the desired structural features. Low-correlation spectra are selected via a genetic algorithm and fed into the inverse model to predict the corresponding geometries. The spectral response is then reconstructed, achieving a mean square error (MSE) on the order of 10<sup>-3</sup>. This rapid prototyping solution, especially in resource-constrained scenarios, can mark a significant step forward in the development of the compact and efficient computational multispectral metasurface system. Multispectral image ; Photonic crystal ; Deep learning ; Genetic algorithm ; Inverse problem ; Photonics ; Inverse ; Computational complexity theory ; Artificial neural network Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Thermal Radiation and Cooling Technologies
Authors: Haruhisa Kitano | Yuuji Kimura | Akinobu Shibata
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Metals and Alloys

Hydrogen Embrittlement Factors in Low-Alloyed High-Strength Steels: A Machine Learning and Symbolic Regression Approach Hydrogen embrittlement ; Embrittlement ; Materials science ; Metallurgy ; Symbolic regression ; Hydrogen ; Artificial intelligence ; Computer science ; Chemistry ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Nuclear Materials and Properties
Authors: L. Y. Wu | Junjie Wang | Zhifeng Gao | Xiaohong Ji | Rong Zhu | Xinyu Li | Linfeng Zhang | Guolin Ke | E Weinan
Source: ArXiv.org (preprint, 2025-07-30)
Field: Materials Science — Materials Chemistry

Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling Organic reaction, the foundation of modern chemical industry, is crucial for new material development and drug discovery. However, deciphering reaction mechanisms and modeling multi-molecular relationships remain formidable challenges due to the complexity of molecular dynamics. While several state-of-the-art models like Uni-Mol2 have revolutionized single-molecular representation learning, their extension to multi-molecular systems, where chemical reactions inherently occur, has been underexplored. This paper introduces Uni-Mol3, a novel deep learning framework that employs a hierarchical pipeline for multi-molecular reaction modeling. At its core, Uni-Mol3 adopts a multi-scale molecular tokenizer (Mol-Tokenizer) that encodes 3D structures of molecules and other features into discrete tokens, creating a 3D-aware molecular language. The framework innovatively combines two pre-training stages: molecular pre-training to learn the molecular grammars and reaction pre-training to capture fundamental reaction principles, forming a progressive learning paradigm from single- to multi-molecular systems. With prompt-aware downstream fine-tuning, Uni-Mol3 demonstrates exceptional performance in diverse organic reaction tasks and supports multi-task prediction with strong generalizability. Experimental results across 10 datasets spanning 4 downstream tasks show that Uni-Mol3 outperforms existing methods, validating its effectiveness in modeling complex organic reactions. This work not only ushers in an alternative paradigm for multi-molecular computational modeling but also charts a course for intelligent organic reaction by bridging molecular representation with reaction mechanism understanding. Organic molecules ; Bridging (networking) ; Representation (politics) ; Pipeline (software) ; Foundation (evidence) ; Molecular model ; Deep learning ; Downstream (manufacturing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Zewen Zhang | Yanbin Li | Weijiang Zhou | Gong‐Her Wu | Jane Lee | Jiayu Wan | Hao Chen | William Huang | Yusheng Ye | Michael F. Schmid | Wah Chiu | Yi Cui
Source: ChemRxiv (preprint, 2025-01-03)
Field: Materials Science — Surfaces, Coatings and Films

Resolving Three-Dimensional Nanoscale Heterogeneities in Lithium Metal Batteries with Cryogenic Electron Tomography Current direct observation of sensitive battery materials and interfaces primarily relies on two-dimensional (2D) imaging, leaving out their three-dimensional (3D) relationship. Here, we used cryogenic electron tomography (cryo-ET) to visualize the lithium metal anode in 3D at nanometer resolution and cryogenic electron microscopy (cryo-EM) to reveal atomic details in local regions. We imaged both freshly prepared and calendar aged Li metal anodes to reveal the development of LiH in Li dendrites and the Li-LiH interface, as well as the development of the solid-electrolyte interphase (SEI). Using a convolutional neural network-based technique, the 3D arrangement of Li metal, along with nanoscale LiH and Cu heterogeneities in dendrites were visualized and annotated. In longer-term calendar aging, we observed more substantial LiH growth accompanied by extended SEI growth. Our results show that the growth of LiH and of the extended SEI during battery calendar aging are temporally and spatially separate processes Nanoscopic scale ; Lithium (medication) ; Electron tomography ; Materials science ; Electron ; Tomography ; Nanotechnology ; Metal ; Lithium metal ; Nuclear engineering ; Engineering physics ; Aerospace engineering ; Physics ; Nuclear physics ; Engineering ; Metallurgy ; Optics ; Power (physics) ; Psychology ; Transmission electron microscopy ; Battery (electricity) ; Scanning transmission electron microscopy ; Thermodynamics Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials ; Surface and Thin Film Phenomena