jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ming Han | Ge Sun | Paul F. Nealey | Juan Pablo
Source: npj Computational Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials. Embedding ; Representation (politics) ; Pipeline (software) ; Context (archaeology) ; Deep learning ; Exploit ; Set (abstract data type) ; Feature learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: Binbin Lin | Luis J. Carrillo | Xiang‐Long Peng | Wanxin Chen | David A. Santosb | Sarbajit Banerjeeb | Bai‐Xiang Xu
Source: ArXiv.org (preprint, 2025-07-24)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning-enabled large-scale analysis of particle geometry-lithiation correlations in battery cathode materials A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical features of lithiated V2O5 nanoparticles as an exemplar of a phase-transforming battery cathode material. First, the deep learning-enabled segmentation model is integrated with the singular value decomposition technique and a spectral database to generate accurate composition and phase maps capturing lithiation heterogeneities as imaged using scanning transmission X-ray microscopy. These phase maps act as the output properties for correlation analysis. Subsequently, the quantitative influences of the geometrical features of nanoparticles such as the particle size (i.e., projected perimeter and area), the aspect ratio, circularity, convexity, and orientation on the lithiation phase maps are revealed. These findings inform strategies to improve lithiation uniformity and reduce stress in phase-transforming lithium battery materials via optimized particle geometry. Battery (electricity) ; Nanoparticle ; Particle (ecology) ; Cathode ; Phase (matter) ; Segmentation ; Lithium (medication) ; Texture (cosmology) ; Deep learning Electron and X-Ray Spectroscopy Techniques ; Advanced Battery Technologies Research
Authors: Jessica Tao (8304264) | Dong Dinh (14760491) | Dominic Caracciolo (11486208) | Zhi-Peng Wu (642828) | Zeqi Li (6371579) | Susan Lu (2504710) | Chuan-Jian Zhong (1275120)
Source: Figshare (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

A Multidimensional Machine Learning-Based Approachto Optimization of Ternary Nanoalloy Catalysts for Oxygen ReductionReaction The arduous process of designing the optimal Pt-based alloy catalyst poses a great barrier to the mass commercialization of hydrogen fuel cells. Given the vast possibilities of metal combinations and their intricate interactions, the trial-and-error experimental search for the optimal catalyst is practically unfeasible. A major challenge lies in understanding the multimetallic alloy catalysts’ dynamic atomic structure and metal composition under oxygen reduction reaction conditions. We report herein a multidimensional machine learning-based approach to the analysis and optimization of ternary nanoalloy catalysts. The effectiveness of this approach is demonstrated through assessing the relationships between chemical composition, lattice structure, and catalytic performance of the alloy catalysts, which provides a data-driven framework and insight for understanding the dynamic nature of the alloy catalysts. Despite the complex, dynamic, and evolving nature of Pt-based alloy catalysts under the reaction conditions, the results provide some important insights into potential pathways to achieving the optimization of such ternary alloy catalysts. Moreover, the proposed data-driven approach is versatile and can readily be extended to discovery and development of other alloy catalysts, including high-entropy alloy catalysts. Alloy ; Ternary operation ; Catalysis ; Ternary alloy ; Oxygen reduction reaction ; Hydrogen ; Metal Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies
Authors: Thomas G. Ciardi | Pawan K. Tripathi | Zhuldyz Ualikhankyzy | Benjamin G. Pierce | T. Yamagata | Sonoko Hamaya | Masayoshi Adachi | Hiroyuki Fukuyama | Roger H. French
Source: Interdisciplinary Information Sciences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computer Vision and Machine Learning Pipelines for Nucleation and Growth Studies of AlN from Metal Alloy Melts Electromagnetic levitation combined with high-speed imaging enables direct in-situ observation of crystal growth dynamics in high-temperature melts, providing crucial insights for materials synthesis. However, high-speed imaging generates massive datasets, often exceeding tens of thousands of frames per experiment, which poses significant challenges for traditional manual characterization methods. We demonstrate the evolution from computer vision methods to machine learning approaches using U-Net through three experimental studies of aluminum nitride (AlN) crystal growth in Ni–Al and Fe–Al systems at 1850–2030 K. Our methodological progression from basic computer-vision image processing to machine learning establishes increasingly robust frameworks for quantifying nucleation events, AlN crystal growth rates, and morphological evolution. When applied to electromagnetic levitation experiments with synchronized dual-camera imaging, these automated techniques revealed quantitative relationships between thermodynamic driving forces and crystal orientation that would be impractical to extract manually. Notably, the deep learning approach in this work achieved 95% IoU in detecting crystal formation area while reducing analysis time to minutes. This deep learning framework is applicable beyond crystal growth studies, offering a template for automated analysis in other in-situ characterization techniques where rapid dynamic processes generate substantial datasets. Nucleation ; Crystal (programming language) ; Characterization (materials science) ; Levitation ; Crystal growth ; Machine vision ; Deep learning ; Nitride Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Machine Learning in Materials Science
Authors: Shingo Takegawa | Haruka Tobita | Yasuhiko Igarashi | Yuya Oaki | Takashi Kato
Source: Nanoscale (article, 2025-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of the phase transition temperatures of functional nanostructured liquid crystals: a machine learning method based on small data for the design of self-assembled materials Here we demonstrate the prediction of the isotropization temperatures of nanostructured ionic liquid crystals (ILCs) by a machine learning method. ILCs, which self-assemble into dynamic and well-ordered nanostructures, have been extensively studied because they can be used as various functional materials including electrolytes, water treatment membranes, and stimuli-responsive materials. In general, machine learning is not easily applied to predict dynamic functions of such self-assembled materials because of the complex structure-function relationship and small data size. Our approach is to use the machine learning method combined with our experimental experiences and chemical insights as researchers. The training data of order-disorder transition (isotropic) temperatures of 116 wedge-shaped ILCs reported by our groups or other groups have been analyzed by this method, leading to the successful construction of the prediction model with a root mean squared error value of 23.7 °C. The model is straightforward, interpretable and reasonable from the viewpoint of chemistry. Moreover, the constructed model predicted the transition temperatures of 45 wedge-shaped ILCs in the test data. The model has sufficient accuracy to predict the transition temperatures of certain kinds of ILCs, which helps the molecular design of ILCs. The transition temperature of isotropization, which is the transition from an ordered liquid-crystalline state to a random isotropic state, is the key factor for ILCs to achieve excellent properties. It is important to predict the transition temperatures before starting experiments, but this has been challenging because complex factors determine the transition temperatures. The model constructed in the present study may accelerate the development of functional ILCs. Phase transition ; Isotropy ; Mean squared error ; Transition temperature ; Ionic bonding ; Function (biology) ; Experimental data Liquid Crystal Research Advancements ; Ionic liquids properties and applications ; Material Dynamics and Properties
Authors: Sujeet Kumar Pandey | G. Jayapriya | V Charunayana | S. Saranya | S Vinodha | Vivek Panyam Muralidharan
Source:  (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning Algorithms for Predictive Modeling in Chemical Synthesis In chemical synthesis, machine learning (ML) has grown a potent tool that facilitates prediction, optimisation, and the discovery of new molecules and processes. Because of their biocompatibility, silver nanoparticles make great candidates for a variety of nonmedical uses. In order to forecast the antibacterial potential of silver nanoparticles, we introduced machine-learning techniques and expanded on the research studies on antifungal. In the first phase of this investigation, 45 data points were collected for the algorithm's training, including ZOI, dose, bacterial/fungal type, core size, and nanoparticle shape. As indicated by the feature significance investigation, the silver nanoparticles' core size is the most vital determinant of their antibacterial potential. Six distinct machine-learning regression techniques were then used to train the data, and four metrics-RMSE, MAE, MSE, and R2 were utilized to evaluate the models' efficiency. Additionally, the significance of the features incorporated into the prediction models has been assessed. The best model for forecasting antifungal and antibacterial activity has been found. Through this approach, researchers may use machine learning to reduce the need for trial and error while addressing the issues of cost and time consumption in laboratory studies. Feature (linguistics) ; Support vector machine ; Predictive modelling ; Core (optical fiber) ; Statistical learning ; Data modeling ; Active learning (machine learning) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanoparticles: synthesis and applications
Authors: Jianwei Deng | Haibao Lu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Deep learning of long short-term memory neural networks in shape memory polymers towards shape memory behaviors Shape-memory polymer ; Artificial neural network ; Term (time) ; Long short term memory ; Long-term memory ; Cognitive science ; Short-term memory ; Artificial intelligence ; Computer science ; Memory formation ; Psychology ; Cognitive psychology ; Neuroscience ; Recurrent neural network ; Shape-memory alloy ; Cognition ; Working memory ; Hippocampus ; Physics Polymer composites and self-healing
Authors: Bin Xiao | Yuchao Tang | Yi Liu
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Integrating Materials Representations Into Feature Engineering in Machine Learning for Crystalline Materials: From Local to Global Chemistry‐Structure Information Coupling ABSTRACT Integrating materials representations into feature engineering by rational design plays a critical role in determining the capability and accuracy of material property prediction via machine learning (ML). There still exists a lack of comprehensive classification and multi‐dimensional evaluation for many existing feature models that could guide model selection in applications and further development. This review systematically classifies feature construction methods for crystalline structures, emphasizing the coupling between chemical and structural information. We systematically discuss the geometric configurations, chemical attributes, and their intricate coupling mechanisms that can be leveraged for feature engineering. Furthermore, a comprehensive comparison is performed across multiple aspects including graph network representation, structural information embedding, chemistry‐structure information coupling, local versus global characteristics, long‐range versus short‐range description, algorithm compatibility with kernel function method or deep neural network, data size requirements, computational complexity, and interpretability mechanisms, thereby highlighting key variations in existing feature models and improving the physical interpretability of predictive models. To illustrate the integration of multi‐dimensional characteristics, the center‐environment (CE) feature model is introduced based on the coupling between local chemical and structural information of physical core‐shell structures. Within the CE model, the pre‐attention mechanism reorients focus from intricate details within complex ML algorithms to explicit feature models that depict physical core‐shell configurations. By minimizing data requirements while enhancing transparency in ML models, the CE feature provides a practical approach for developing efficient and accurate ML‐based predictions tailored for small‐data scenarios in materials science. This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Data Science &gt; Artificial Intelligence/Machine Learning Interpretability ; Computer science ; Feature (linguistics) ; Graph kernel ; Artificial intelligence ; Machine learning ; Feature learning ; Representation (politics) ; Artificial neural network ; Data mining ; Theoretical computer science ; Kernel method ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zhu, Xiangzhou | Rinke, Patrick | Egger, David A.
Source: ArXiv.org (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to investigate the electronic properties of defective semiconductors at finite temperatures efficiently. We develop an active learning approach that integrates two advanced equivariant graph neural networks: MACE for atomic energies and forces and DeepH-E3 for the electronic Hamiltonian. Focusing on representative point defects in GaAs, we demonstrate computational accuracy comparable to density functional theory at a fraction of the computational cost, predicting the temperature-dependent band gap of defective GaAs directly from larger scale molecular dynamics trajectories with an accuracy of few tens of meV. Our results highlight the potential of equivariant neural networks for accurate atomic-scale predictions in complex, dynamically evolving materials. Semiconductor ; Artificial neural network ; Equivariant map ; Thermal ; Band gap ; Density functional theory ; Point (geometry) Machine Learning in Materials Science ; Quantum many-body systems ; Thermal properties of materials
Authors: Zhu, Xiangzhou | Rinke, Patrick | Egger, David A.
Source: arXiv (Cornell University) (preprint, 2025-11-23)
Field: Materials Science — Materials Chemistry

Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to investigate the electronic properties of defective semiconductors at finite temperatures efficiently. We develop an active learning approach that integrates two advanced equivariant graph neural networks: MACE for atomic energies and forces and DeepH-E3 for the electronic Hamiltonian. Focusing on representative point defects in GaAs, we demonstrate computational accuracy comparable to density functional theory at a fraction of the computational cost, predicting the temperature-dependent band gap of defective GaAs directly from larger scale molecular dynamics trajectories with an accuracy of few tens of meV. Our results highlight the potential of equivariant neural networks for accurate atomic-scale predictions in complex, dynamically evolving materials. Semiconductor ; Artificial neural network ; Equivariant map ; Thermal ; Band gap ; Density functional theory ; Point (geometry) Machine Learning in Materials Science ; Quantum many-body systems ; Thermal properties of materials
Authors: Bohong Jia
Source:  (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Research on the Model of Deep Learning in the Performance Prediction of Two-Dimensional Semiconductor Materials This paper proposes an innovative model based on the combination of GNN and MTL (GNN-MTL), which aims to improve the prediction accuracy of multiple performance indicators (such as electron mobility, bandgap width, etc.) at the same time. GNN-MTL is used to represent the atomic structure of two-dimensional semiconductor materials as a graph structure. It uses graph convolutional networks to capture local and global dependencies between atoms. Experimental results show that GNN-MTL outperforms traditional machine learning methods (such as support vector machines, random forests, etc.) in the prediction tasks of electron mobility and bandgap width of multiple two-dimensional semiconductor materials (such as Mos2, Ws2, etc.), especially when multiple tasks are optimized simultaneously. In the prediction of electron mobility, the MAE of GNN-MTL is 0.04eV·s/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, which is 6.2% lower than that of traditional methods. The prediction error of bandgap width is reduced by about 8%. In addition, GNN-MTL also shows good performance in terms of computational efficiency and training convergence speed, and adapts to the needs of large-scale data. This shows that GNN-MTL provides a more accurate and efficient computational method for the performance prediction of two-dimensional semiconductor materials. Graph ; Semiconductor ; Deep learning ; Performance prediction ; Convergence (economics) ; Support vector machine Machine Learning in Materials Science ; Ga2O3 and related materials ; Advanced Data and IoT Technologies
Authors: Junmin Chen | Qian Gao | Yan Lin | Min Huang | Cheng Zheng | Wei Feng | Jianxing Huang | Bo Wang | Kuang Yu
Source: ArXiv.org (preprint, 2025-11-17)
Field: Materials Science — Materials Chemistry

A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of different solvents, additives, and salts, which is difficult to explore experimentally. High-fidelity molecular simulation can accurately predict the bulk properties of electrolytes by employing accurate potential energy surfaces, thus guiding the molecule and formula engineering. At present, the overly simplified classic force fields rely heavily on experimental data for fine-tuning, thus its predictive power on microscopic level is under question. In contrast, the newly emerged machine learning interatomic potential (MLIP) can accurately reproduce the ab initio data, demonstrating excellent fitting ability. However, it is still haunted by problems such as low transferrability, insufficient stability in the prediction of bulk properties, and poor training cost scaling. Therefore, it cannot yet be used as a robust and universal tool for the exploration of electrolyte design space. In this work, we introduce a highly scalable and fully bottom-up force field construction strategy called PhyNEO-Electrolyte. It adopts a hybrid physics-driven and data-driven method that relies only on monomer and dimer EDA (energy deomposition analysis) data. With a careful separation of long/short-range and non-bonding/bonding interactions, we rigorously restore the long-range asymptotic behavior, which is critical in the description of electrolyte systems. Through this approach, we significantly improve the data efficiency of MLIP training, allowing us to achieve much larger chemical space coverage using much less data while retaining reliable quantitative prediction power in bulk phase calculations. PhyNEO-electrolyte thus serves as an important tool for future electrolyte optimization. Electrolyte ; Battery (electricity) ; Stability (learning theory) ; Field (mathematics) ; Ab initio ; Artificial neural network ; Power (physics) ; Force field (fiction) ; Scalability Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Block Copolymer Self-Assembly