jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saadiallakh Normatov | Pavel V. Nesterov | Timur A. Aliev | Alexandra A. Timralieva | Alexander S. Novikov | Ekaterina V. Skorb
Source: ACS Omega (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms This work proposes several machine learning models that predict B3LYP-D4/def-TZVP outputs from HF-3c outputs for supramolecular structures. The data set consists of 1031 entries of dimer, trimer, and tetramer cyclic structures, containing both molecules with heteroatoms in the ring and without. Six quantum chemistry descriptors and features are calculated by using both computational methods: Gibbs energy, electronic energy, entropy, enthalpy, dipole moment, and band gap. Statistical analysis shows a good correlation between energy properties and bad correlation only for the dipole moment. Machine learning models are separated into three groups: linear, tree-based, and neural networks. The best models for the prediction of density functional theory features are LASSO for linear, XGBoost for tree-based, and single-layer perceptron for neural networks with energy-related features having the best prediction values and dipole moment having the worst. Hartree–Fock method ; Artificial neural network ; Density functional theory ; Algorithm ; Artificial intelligence ; Computer science ; Physics ; Statistical physics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Chemical Physics Studies
Authors: Roshan Kumar Jha | R. Kumar | R. Shri Varsheni | Sumantra Mandal
Source: Journal of Materials Science (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

A machine learning-based framework for predicting the solute segregation energy in bicrystal and nanocrystalline grain boundaries Nanocrystalline material ; Grain boundary ; Voronoi diagram ; Energy (signal processing) ; Alloy ; Range (aeronautics) Microstructure and mechanical properties ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Junchi Liu | Ying Tang | Sergei Tretiak | Wenhui Duan | Liujiang Zhou
Source: npj Computational Materials (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior. Interpretability ; Bridging (networking) ; Predictive power ; Graph ; Property (philosophy) ; Implementation ; Predictive modelling ; Artificial neural network ; Expansive Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Yiwen Zhang | Zihao Ye | Dejun Hu | Shutao Qi | Zuobang Sun | Junfeng Yang | Yan Ma | Wayne Zhang | Junliang Zhang | Zhiming Li
Source: ACS Applied Polymer Materials (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

QC-Augmented NN/GNN for Few-Shot Prediction of Amorphous Polymer Properties Via Multi-Scale Microstructures Accurate prediction of polymer properties is essential for accelerating materials design. However, deep learning techniques, as well as traditional density functional theory (DFT) and molecular dynamics (MD) methods, often face limitations due to the scarcity of experimental data and insufficient understanding of amorphous polymer microstructures, particularly in few-shot learning scenarios. To address this challenge, we introduce a paradigm based on “local clusters,” structural motifs whose properties can be efficiently computed using low-cost quantum chemical (QC) methods. These clusters, simulated across multiple scales, serve as key descriptors that capture essential microstructural features of amorphous polymers. By integrating QC-derived descriptors with graph convolutional networks (GNN) or neural networks (NN), we developed Locluster, a multiscale, microstructure-informed predictive framework tailored for few-shot learning scenarios in amorphous polymer research. Notably, Locluster eliminates the need for full-scale QC simulations of entire polymers and requires only 2–5 descriptors and as few as two dozen training samples to accurately predict critical properties such as density, refractive index, dielectric constant, and glass transition temperature. The model achieves predictive accuracy comparable to large-data approaches and can generate predictions for 100–200 polymer candidates within 24 h on a single 128-core server, making it well-suited for rapid iteration and design updates in early- to midstage polymer development. This work offers an efficient and practical strategy for the rational design and accelerated discovery of amorphous polymeric materials. Amorphous solid ; Microstructure ; Scale (ratio) ; Shot (pellet) ; Materials science ; Polymer ; Computer science ; Composite material ; Chemistry ; Physics ; Crystallography ; Metallurgy Diamond and Carbon-based Materials Research ; Thermal properties of materials ; Metal and Thin Film Mechanics
Authors: Liyuan Fan | Xuetao Li | Xixi Luo | Bo Zhu | Wei Guan
Source: Chemistry - A European Journal (article, 2025-04-22)
Field: Chemistry — Organic Chemistry

Data‐Driven Prediction of Reactivity and Additive Selection for C(sp <sup>2</sup> )–(Hetero)Atom Bond Couplings in an Adaptive Dynamic Homogeneous Catalysis Under visible light-driven redox conditions, employing transition-metal catalysis provides a powerful platform for constructing C(sp<sup>2</sup>)-(hetero)atom bonds. Although these reactions are highly significant, they require precise optimization of reaction parameters. König, Ghosh, and colleagues introduced an adaptive dynamic homogeneous catalysis (AD-HoC) platform that furnishes robust, high-yield conditions for photocatalyzed cross-coupling reactions. The AD-HoC system eliminates the need to optimize catalysts, ligands, and bases, instead, it achieves C(sp<sup>2</sup>)-(hetero)atom bond coupling by merely altering additives and substrate molecules. Leveraging the predictability of reaction conditions within the AD-HoC system, machine learning offers a method to evaluate the reactivity of substrate combinations and the categories of additives. Our research integrates high-throughput quantum mechanical calculations with cheminformatics approaches to explore the reactivity of substrate combinations and the selection of additives within the AD-HoC system. Further data-driven analysis reveals that the electronic characteristics of electrophiles and the geometric characteristics of nucleophiles are key factors regulating reactivity within the AD-HoC system. Herein, we present an end-to-end tool for prediction starting from the SMILES (Simplified Molecular-Input Line-Entry System) representation. This work demonstrates the collaborative use of computational statistics and machine learning to predict the reactivity and reaction conditions of substrate combinations, thereby enhancing the precision and efficiency of synthetic processes. Reactivity (psychology) ; Substrate (aquarium) ; Chemistry ; Catalysis ; Electrophile ; Nucleophile ; Computer science ; Computational chemistry ; Combinatorial chemistry ; Organic chemistry Radical Photochemical Reactions ; Advanced Photocatalysis Techniques ; E-Government and Public Services
Authors: Fabian P. Krüger | Johan Östman | Lewis Mervin | Igor V. Tetko | Ola Engkvist
Source: Journal of Cheminformatics (article, 2025-03-26)
Field: Materials Science — Materials Chemistry

Publishing neural networks in drug discovery might compromise training data privacy This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. Our results reveal significant privacy risks across all evaluated datasets and neural network architectures. Combining multiple attacks increases these risks. Molecules from minority classes, often the most valuable in drug discovery, are particularly vulnerable. We also found that representing molecules as graphs and using message-passing neural networks may mitigate these risks. We provide a framework to assess privacy risks of classification models and molecular representations, available at https://github.com/FabianKruger/molprivacy . Our findings highlight the need for careful consideration when sharing neural networks trained on proprietary chemical structures, informing organisations and researchers about the trade-offs between data confidentiality and model openness. Computer science ; Confidentiality ; Artificial neural network ; Context (archaeology) ; Inference ; Openness to experience ; Machine learning ; Data science ; Information privacy ; Artificial intelligence ; Data mining ; Computer security Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Adversarial Robustness in Machine Learning
Authors: Xinyu Ni | Yoshifumi Amamoto | Jun Kikuchi
Source: Sustainable materials and technologies (article, 2025-11-20)
Field: Materials Science — Polymers and Plastics

Simultaneous multimodal and multitask strategies for diverse biodegradable polymers powered by NMR data science Biodegradable polymer ; Polymer ; Polyester ; Relaxometry ; Rigidity (electromagnetism) ; Dynamic mechanical analysis Polymer composites and self-healing ; Polymer crystallization and properties ; biodegradable polymer synthesis and properties
Authors: Yukun Teng | Haoyi Tan | Wei Huang | Guangcun Shan
Source: Communications Physics (article, 2025-11-11)
Field: Chemistry — Inorganic Chemistry

Atomic-level interpretable multimodal graph neural network for predicting carbon dioxide adsorption in metal-organic frameworks Metal–organic frameworks (MOFs) are porous crystalline materials with strong potential for CO₂ capture because their structures and chemistries are highly tunable. However, their vast compositional and structural search space hampers efficient prediction of adsorption performance. Here we introduce MMAGNN, a multimodal MOF adsorption graph neural network that integrates graph representations and three-dimensional atomic coordinates. MMAGNN employs graph neural networks (GNNs) with attention mechanisms to extract and fuse molecular and spatial features, and incorporates a Transformer-based interpreter to quantify the contribution of individual atoms to adsorption. We find that the multimodal design reduces redundant information and feature interference, improving prediction accuracy and stability. Validation against adsorption density distributions confirms that MMAGNN learns interatomic interactions and pore-structure information and accurately predicts CO₂ adsorption sites. This interpretable framework provides high-throughput screening and rational design of high-performance MOFs for carbon-capture applications. Metal–organic frameworks (MOFs) are porous crystalline materials with tunable chemistry and pores, making them promising for CO₂ capture. This work introduces the Multimodal MOF Adsorption Graph Neural Network, fusing atomic graphs and 3D point clouds to predict CO₂ adsorption and produce atom-level attention maps highlighting adsorption sites. Adsorption ; Graph ; Artificial neural network ; Fuse (electrical) ; Point cloud ; Feature (linguistics) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Kunal Meshram | Milena Petković | Martin Holeňa | Natasha Dropka
Source: Journal of Crystal Growth (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Machine learning framework for derivation and optimization of Cz-YAG crystal growth recipe Recipe ; Crystal growth ; Crystal (programming language) ; Optimization algorithm Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Luminescence Properties of Advanced Materials
Authors: Jinrong Huo | Pengfei Liu | Yuxin Tang | Kai Zhang
Source: The Journal of Physical Chemistry C (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

Prediction of New Crystal of Titanium Oxides and Property Calculations Using Structure Prediction Software and First-Principles Theory The crystal structure prediction software CBD-GM, based on the Bayesian optimization algorithm combined with deep learning, was used to predict the crystal structure of titanium oxide. One known bulk structure and three new structures were predicted. The first-principles of density functional theory (DFT) have been used to calculate and analyze the properties associated with three new material structures. The results show that these structures have outstanding thermodynamic and kinetic stability. The elastic constants, bulk modulus, and Young’s modulus indicate that the structure materials have excellent mechanical properties. Calculation of the energy band structure using hybrid density flooding shows that the prediction structures are all broadband semiconductors and also have a high dielectric constant and high optical response in the UV region, which has great scope for development in semiconductor devices and deep exotic optoelectronics, providing a completely new research direction for the development and experimental synthesis of new structures. Property (philosophy) ; Crystal structure prediction ; Materials science ; Titanium ; Crystal structure ; Density functional theory ; Statistical physics ; Computational chemistry ; Crystallography ; Chemistry ; Physics ; Metallurgy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Yaroslav I. Isaev | Dmitriy M. Makarov | Ilya A. Khodov
Source: Journal of Molecular Liquids (article, 2025-08-28)
Field: Chemistry — Spectroscopy

Machine learning prediction of NMR shifts for rare and transition metal complexes (45Sc, 49Ti, 89Y, 91Zr, 139La) Transition metal ; Chemistry ; Materials science ; Organic chemistry ; Catalysis Advanced NMR Techniques and Applications ; Medical Imaging Techniques and Applications ; Machine Learning in Materials Science
Authors: Yu Kumagai
Source: Materia Japan (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures Crystal structure prediction ; Materials science ; Artificial intelligence ; Computer science ; Chemical physics ; Crystallography ; Crystal structure ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Welding Techniques and Residual Stresses
Authors: Koichi Gocho | Masato Hamaie | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama | Masayuki Karasuyama | Ryo Kobayashi
Source: The Journal of Physical Chemistry C (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Causal Analysis of Factors for Li Ionic Conductivity in Olivine-Type LiMXO <sub>4</sub> Materials Using LiNGAM All-solid-state batteries are increasingly examined to improve the safety of lithium-ion batteries. Considerable efforts are being made to develop solid electrolyte materials with high ionic conductivity to realize such batteries. Recently, materials informatics (MI), which uses machine learning to quantify the relationships between descriptors that represent the composition, structure, and properties of materials, has been increasingly utilized. However, MI has primarily focused on evaluating the correlations between descriptors, with few examples analyzing hierarchical cause-and-effect relationships. This study analyzes the causal relationships among the descriptors of lithium-ion conductivity in olivine-type LiMXO4 materials using linear non-Gaussian acyclic model (LiNGAM), a method of causal discovery. We previously compiled a data set for these materials using first-principles calculations and performed machine learning regression analyses. A key aspect of this research is the systematization of knowledge regarding the composition, structure, and ionic conductivity of materials. Clarifying the causal relationships can lead to the discovery of factors that truly control ionic conductivity. Therefore, we qualitatively interpret the causal relationship diagram and provide specific guidelines for controlling the lithium-ion conductivity by comparing the results of previous machine learning regression analyses with those of LiNGAM. Ionic conductivity ; Conductivity ; Lithium (medication) ; Computer science ; Olivine ; Ionic bonding ; Machine learning ; Ion ; Data mining ; Artificial intelligence ; Electrolyte ; Chemistry ; Mineralogy ; Physical chemistry ; Psychology Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Han-Zhi Luo | Liang Qiming | Zi-Xing Guo | X. H. Xie | Jin-Peng Tang | Tong Guan | Yefei Li | Si-Cong Ma | Yingchen Xu | Zhen-Xiong Wang | Cheng Shang | Liu Zhi-pan
Source: Acta Physico-Chimica Sinica (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

LASPAI: AI-powered platform for the future atomic simulation Bridging (networking) ; Interface (matter) ; Generative grammar ; Artificial neural network ; Range (aeronautics) ; Graphical user interface ; Ranging Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Vahe Gharakhanyan | Luis Barroso-Luque | Yi Yang | Muhammed Shuaibi | Kyle Michel | Daniel S. Levine | Misko Dzamba | Xiang Fu | Meng Gao | Xingyu Liu | Haoran Ni | Keian Noori | Brandon M. Wood | Matt Uyttendaele | Arman Boromand | C. Lawrence Zitnick | Noa Marom | Zachary W. Ulissi | Anuroop Sriram
Source: ArXiv.org (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

Open Molecular Crystals 2025 (OMC25) Dataset and Models The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly available datasets of structures with property labels. To address this challenge, we introduce the Open Molecular Crystals 2025 (OMC25) dataset, a collection of over 27 million molecular crystal structures containing 12 elements and up to 300 atoms in the unit cell. The dataset was generated from dispersion-inclusive density functional theory (DFT) relaxation trajectories of over 230,000 randomly generated molecular crystal structures of around 50,000 organic molecules. OMC25 comprises diverse chemical compounds capable of forming different intermolecular interactions and a wide range of crystal packing motifs. We provide detailed information on the dataset's construction, composition, structure, and properties. To demonstrate the quality and use cases of OMC25, we further trained and evaluated state-of-the-art open-source machine learning interatomic potentials. By making this dataset publicly available, we aim to accelerate the development of more accurate and efficient machine learning models for molecular crystals. Crystal (programming language) ; Intermolecular force ; Crystal structure prediction ; Property (philosophy) ; Relaxation (psychology) ; Range (aeronautics) ; Density functional theory ; Quality (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: F. Lan | Jian-hua Liu | Yang He | Jian Shao | Min Xiao | Lin Zhang | Shuai Zhang | Feng Xu | Xinfeng Duan
Source: Journal of Intelligent Manufacturing (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine-learning-driven synergistic optimization of multi-objective mechanical properties in steel materials Multi-objective optimization ; Boosting (machine learning) ; TOPSIS ; Reliability (semiconductor) ; Stability (learning theory) ; Property (philosophy) ; Manufacturing cost ; Work (physics) ; Feature (linguistics) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Zhihao Li | Xuehai Huang | Yansong Jiang | Wei Zeng | Ding Zou | Xue Dong | LiuLin YANG | Xiaoyu Cao | ZW Tian | Yu Wang
Source: Journal of the American Chemical Society (article, 2025-12-23)
Field: Chemistry — Organic Chemistry

Catassembly Triad: A Catalytic Framework for Enantioselective Chiral Molecular Assembly Chiral molecular assembly represents a frontier in creating life-inspired functional materials; yet, a predictive framework for its enantioselective catalysis remains elusive. Here, we introduce the Catassembly Triad - a mechanistic paradigm for catalytic enantioselective assembly, demonstrated using chiral supramolecular cages as a model system. Combining in situ characterization, <i>ab initio</i> calculations, and machine learning, we resolve how catalysts must harmonize attachment, chiral control, and detachment to direct assembly pathways. Kinetic and structural analyses reveal that chiral diamine catalysts (e.g., cyclohexanediamine) enforce enantioselectivity by templating transient intermediates with deformation-induced affinity changes driving timely catalyst release. We establish a predictive framework based on quantitative triad metrics (Δ<i>E</i><sub>att</sub>, Δ<i>E</i><sub>ctl</sub>, and Δ<i>E</i><sub>det</sub>) from calculations, validated by Bayesian clustering and experimental testing. The framework achieves a perfect concordance between predicted and observed catalyst efficacy across diverse molecular classes. This framework bridges enzymatic cooperativity and supramolecular assembly, enabling programmable chirality transfer and unlocking catalyst design for adaptive chiral materials. Enantioselective synthesis ; Supramolecular chemistry ; Cooperativity ; Catalysis ; Chirality (physics) ; Enantiopure drug ; Supramolecular catalysis ; Catalytic triad Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Synthesis and Properties of Aromatic Compounds