jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kalyan Ramakrishnan | Lars L. Schaaf | Chen Lin | Guangrun Wang | Philip H. S. Torr
Source: The Journal of Chemical Physics (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Implicit neural representations for chemical reaction paths We show that neural networks can be optimized to represent minimum energy paths as continuous functions, offering a flexible alternative to discrete path-search methods such as Nudged Elastic Band (NEB). Our approach parameterizes reaction paths with a network trained on a loss function that discards tangential energy gradients and enables instant estimation of the transition state. We first validate the method on two-dimensional potentials and then demonstrate its advantages over NEB on challenging atomistic systems where (i) poor initial guesses yield unphysical paths, (ii) multiple competing paths exist, or (iii) the reaction follows a complex multi-step mechanism. Results highlight the versatility of the method: for instance, a simple adjustment to the sampling strategy during optimization can help escape local-minimum solutions. Finally, in a low-dimensional setting, we demonstrate that a single neural network can learn from existing paths and generalize to unseen systems, showing promise for a universal reaction path representation. Path (computing) ; Representation (politics) ; Artificial neural network ; Simple (philosophy) ; Computer science ; Function (biology) ; Energy (signal processing) ; Deep neural networks ; Algorithm ; Sampling (signal processing) ; State (computer science) ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Computer vision Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Yanju Wang | Qiwen Xu | Xingwu Li | Aixue Sha | Wenfeng Hao
Source: Journal of Materials Engineering and Performance (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Numerical Analysis of the Low-Cycle Fatigue Performance of Ti2AlNb Alloy Components with Perforations Materials science ; Alloy ; Low-cycle fatigue ; Fatigue testing ; Metallurgy ; Composite material High-Velocity Impact and Material Behavior ; Intermetallics and Advanced Alloy Properties ; Aluminum Alloys Composites Properties
Authors: Sushant Sinha | Xiaoping Ma | Kashif Rehman | Narges Armanfard | Stephen Yue
Source: Materials Today Communications (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

An interpretable and reliable framework for alloy discovery in thermomechanical processing In thermomechanical controlled processing, both the alloy composition and the processing strategy shape the mechanical properties of metals. In this study, we present a data-driven approach to discover alloys with optimized strength-ductility trade-off in the thin slab direct rolling process. We evaluate seven different supervised machine learning algorithms to predict two mechanical properties, namely Ultimate Tensile Strength and % Elongation. SHapely Additive exPlanations (SHAP) augments interpretability to the best performing models. NSGA-II, an evolutionary genetic algorithm, is employed with ML models as objective functions to obtain the optimal Pareto Front solutions. Further, we incorporate manifold learning and unsupervised clustering to screen the Pareto Front and to select a few unique solutions which can facilitate added analysis for implementation. Furthermore, we introduce the application of conformal predictions for uncertainty quantification, ensuring reliability of the framework. Overall, the proposed approach enables interpretable and reliable property prediction, thus accelerating alloy design in thermomechanical processing. • Conformal Predictions for Reliability of ML Framework. • Alloy Discovery using Multi-objective Optimization. • Data-driven Post-Pareto Analysis to Choose Unique Alloys. Materials science ; Thermomechanical processing ; Alloy ; Materials processing ; Metallurgy ; Process engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Yingying Li | Hao Xu | Pengyu Hong
Source: ArXiv.org (preprint, 2025-05-16)
Field: Materials Science — Materials Chemistry

2DNMRGym: An Annotated Experimental Dataset for Atom-Level Molecular Representation Learning in 2D NMR via Surrogate Supervision Two-dimensional (2D) Nuclear Magnetic Resonance (NMR) spectroscopy, particularly Heteronuclear Single Quantum Coherence (HSQC) spectroscopy, plays a critical role in elucidating molecular structures, interactions, and electronic properties. However, accurately interpreting 2D NMR data remains labor-intensive and error-prone, requiring highly trained domain experts, especially for complex molecules. Machine Learning (ML) holds significant potential in 2D NMR analysis by learning molecular representations and recognizing complex patterns from data. However, progress has been limited by the lack of large-scale and high-quality annotated datasets. In this work, we introduce 2DNMRGym, the first annotated experimental dataset designed for ML-based molecular representation learning in 2D NMR. It includes over 22,000 HSQC spectra, along with the corresponding molecular graphs and SMILES strings. Uniquely, 2DNMRGym adopts a surrogate supervision setup: models are trained using algorithm-generated annotations derived from a previously validated method and evaluated on a held-out set of human-annotated gold-standard labels. This enables rigorous assessment of a model's ability to generalize from imperfect supervision to expert-level interpretation. We provide benchmark results using a series of 2D and 3D GNN and GNN transformer models, establishing a strong foundation for future work. 2DNMRGym supports scalable model training and introduces a chemically meaningful benchmark for evaluating atom-level molecular representations in NMR-guided structural tasks. Our data and code is open-source and available on Huggingface and Github. Benchmark (surveying) ; Scalability ; Coherence (philosophical gambling strategy) ; Training set ; Representation (politics) ; Set (abstract data type) ; Heteronuclear molecule ; Pattern recognition (psychology) Machine Learning in Materials Science ; Nuclear Physics and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Mohamed Barhoumi
Source: Chemical Engineering Science (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated design of defect-engineered 2D BiOCl: Defect-induced evolution of electronic transitions and direct-gap tuning with supercell size Dopant ; Band gap ; Density functional theory ; Electronic structure ; Doping ; Bismuth ; Semiconductor ; Hybrid functional ; Photocatalysis ; Supercell Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Haibo Xue | Guanjian Cheng | Wan‐Jian Yin
Source: Computational Materials Today (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Elemental augmentation of machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) bridge the gap between the accuracy of ab initio methods and the computational efficiency needed for large-scale simulations. However, custom-trained MLIPs are often limited to specific materials and lack flexibility for incorporating additional elements, while universal potentials (UPots), despite covering a wide range of chemical elements, may sacrifice accuracy for generalization. In this work, we propose an elemental augmentation strategy to efficiently expand MLIPs by incorporating new elements into pre-trained models. Using a Bayesian optimization driven active learning framework, we target the configuration space of new elements where the current MLIPs exhibit high uncertainty and demonstrate the addition of up to 10 elements to a pre-trained UPot. The results demonstrate a high tendency for sampling new structures composed of these elements, minimizing sampling requirements. It reduces computational costs by over an order of magnitude compared to training an MLIP from scratch, while preserving accuracy. This strategy offers a scalable pathway to extend MLIP applicability across diverse chemical spaces. Interatomic potential ; Materials science ; Computer science ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Xinyu Zhong | Yifei Ye | Miaomiao Zheng | Xiubing Huang
Source: Journal of Physics Energy (article, 2025-06-18)
Field: Chemistry — Inorganic Chemistry

High-throughput screening of bimetallic metal–organic frameworks for efficient generation of reactive oxygen species Abstract Currently, the issue of bacterial drug resistance is becoming increasingly severe. Metal–organic frameworks (MOFs) are considered ideal catalysts for reactive oxygen species (ROS) generation and antibacterial applications due to their superior properties, though efficient screening of high-performance MOFs remains challenging. This study employs a dual-track high-throughput screening strategy combining density functional theory (DFT) and machine learning (ML) to predict and evaluate the ROS generation performance of MOFs. Key screening criteria include structural stability, pore size, adsorption capacity, open metal sites, O 2 activation potential, and reaction pathway free energy simulations. Bimetallic MOFs underwent DFT-ML dual-track screening, identifying Cu–Ag-4,4′-bipyridine (Cu–Ag–MOF, Cu:Ag = 1:2) and Cu–Zn-2,5-dihydroxyterephthalic acid (Cu–Zn–MOF, Cu:Zn = 1:3) as optimal candidates. Experimental results demonstrate superior performance of Cu–Ag–MOF, achieving H 2 O 2 yields of 7.79 mmol g −1 under light (60 min) and 3.03 mmol g −1 in darkness. Antibacterial tests showed 99.9% sterilization rate after 30 min illumination and 55.4% efficiency in darkness after 60 min. The enhanced performance originates from bimetallic synergy that improves antibacterial capability and stability, enabling persistent ROS generation and continuous sterilization. This study advances understanding of MOFs catalytic mechanisms and establishes a transferable framework for designing multifunctional biomimetic catalytic materials. Bimetallic strip ; Catalysis ; Metal-organic framework ; Reactive oxygen species ; Adsorption ; Materials science ; Metal ; Sterilization (economics) ; Rational design ; Density functional theory ; Oxygen ; Nanotechnology ; Chemical engineering ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Metallurgy ; Computational chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Philip E. Klunzinger | Thomas Hehre | Bernard Deppmeier | William Ohlinger | Warren J. Hehre
Source: Journal of Computational Chemistry (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Practical Machine Learning Strategies. 2. Accurate Prediction of <scp>ωB97X</scp>‐V/6‐311+G(2df,2p), <scp>ωB97M</scp>‐V/6‐311+G(2df,2p) and <scp>ωB97M</scp>(2)/6‐311+G(2df,2p) Energies From Neural Networks Trained From <scp>ωB97X</scp>‐D/6‐<scp>31G</scp>* Equilibrium Geometries and Energies Starting from ωB97X-D/6-31G* geometries and energies, neural network models have been trained to reproduce energies from three density functionals: ωB97X-V, ωB97M-V, and ωB97M(2), using the 6-311+G(2df,2p) basis set. Training sets on the order of 300 k organic molecules were utilized [≈295,000 molecules for the ωB97X-V functional (up to molecular weight 400 amu); and ≈289,000 molecules for both the ωB97M-V and ωB97M(2) functionals (up to molecular weight 380 amu)]. All training and validation molecules comprise uncharged, closed shell singlets including H, C, N, O, F, S, Cl, and Br (only). The resulting models have been assessed using molecules outside the training sets. Total energies obtained from the neural network models rarely differ from the corresponding density functional values by more than 2-5 kJ/mol (RMS), while differences in conformer energies are typically less than 1 kJ/mol. Molecule ; Chemistry ; Conformational isomerism ; Density functional theory ; Artificial neural network ; Basis set ; Physics ; Computational chemistry ; Artificial intelligence ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Jiahui Yang | Yandong Sun | Ben Xu
Source: Physical review. B./Physical review. B (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Impact of point defects on the thermal conductivity of GaN studied using machine-learned potentials Gallium nitride (GaN) is a crucial material for high power density electronic devices, and its self-heating effects under high power conditions have attracted considerable attention. The study of intrinsic point defects in GaN and their impact on thermal conductivity is a crucial research focus. The influence of these defects on the thermal conductivity is dependent on their type, concentration, and charge state. However, existing experimental and computational methods, such as density functional theory and molecular dynamics, encounter limitations, particularly when dealing with defects of low concentrations, various types, and charged states. To overcome these challenges, we have developed machine-learning potentials for point defects at various concentrations, charge states, and temperatures. Predictive capabilities for the mechanical properties and migration pathways of point defects are demonstrated. We investigate the temperature dependence of thermal conductivity in defect-free structures and analyze how point defect concentration affects thermal conductivity. These studies provide insights for optimizing the performance of GaN materials. Thermal conductivity ; Point (geometry) ; Materials science ; Conductivity ; Thermal ; Condensed matter physics ; Optoelectronics ; Composite material ; Physics ; Thermodynamics ; Mathematics ; Geometry ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Shachar Fite | Zeev Gross
Source: Journal of Chemical Information and Modeling (article, 2025-04-20)
Field: Materials Science — Materials Chemistry

Data-Driven Insights into Porphyrin Geometry: Interpretable AI for Non-Planarity and Aromaticity Analyses Porphyrins are involved in numerous and very different chemical and biological processes, due to the sensitivity of their application-relevant properties to subtle structural changes. Applying modern machine learning methodology is very appealing for discovering structure-activity relationships that can be used for design of tailor-made porphyrins for specific purposes. For achieving this goal, a high-quality set consisting of 425 metal porphyrins was established via curation of 7590 porphyrin structures from the Cambridge crystallographic database. Using data-driven techniques for analyzing nonplanarity and "structural aromaticity" allowed for validation of common knowledge in the field as well as discovery of new relations. Aromaticity was found to be influenced differently by distinct nonplanar distortions. Nonplanarity is more sensitive to macrocycle substitutions than to metal or axial ligand effects, while ruffled distortions are dominated by axial ligand size and metal properties. These findings offer new insights into structure-property relationships in porphyrins, providing a data-driven foundation for targeted synthesis to tune aromaticity and nonplanarity. Despite data set limitations, this work demonstrates the value of machine learning in uncovering complex chemical trends. Planarity testing ; Aromaticity ; Porphyrin ; Artificial intelligence ; Computational chemistry ; Computer science ; Chemistry ; Geometry ; Mathematics ; Crystallography ; Photochemistry ; Organic chemistry ; Molecule Porphyrin and Phthalocyanine Chemistry ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: Harikrishna Sahu | Akhlak Mahmood | Labeeba B. Shafique | Rampi Ramprasad
Source: npj Computational Materials (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models Advances in machine learning have transformed materials discovery, yet challenges remain due to the lack of informatics-ready data and the complexity of numerical descriptors. Scientific knowledge is scattered across publications, making comprehensive data extraction difficult. This study presents a large language model (LLM)-driven framework to accelerate organic solar cell (OSC) materials discovery by extracting structured data from literature and predicting device performance using natural language embeddings. Trained on a curated dataset of 422 OSC devices, the fine-tuned LLM demonstrated strong predictive accuracy across key performance metrics: power conversion efficiency (PCE, R2: 0.87), short-circuit current (JSC, R2: 0.82), open-circuit voltage (VOC, R2: 0.89), and fill factor (FF, R2: 0.59). The models are then used to explore the space of 1.4 million combinations of materials, experimental variables and device architectures. The analysis provides data-driven design guidelines, identifying optimal donor-acceptor combinations and processing conditions that consistently yield higher device performance. Language model ; Key (lock) ; Information extraction ; Question answering ; Natural language ; Knowledge extraction ; Space (punctuation) ; Factor (programming language) ; Data modeling Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Yang Li | Mingjie Zhang | Jianing Chen | Xianhu Liu | Ming Huang | Chuntai Liu | Gang Shao | Hailong Wang | Bingbing Fan | Yuchang Qing
Source: Journal of Advanced Ceramics (article, 2025-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ceramic-based electromagnetic interference shielding materials: Mechanisms, optimization strategies, and pathways to next-generation applications Ceramic-based electromagnetic interference (EMI) shielding materials have emerged as a promising solution due to their tunable dielectric and magnetic properties, excellent chemical stability, and favorable cost-performance ratio. Despite their advantages, enhancing electrical conductivity and optimizing microstructural design remain key technical challenges. This review presents a systematic analysis of the working mechanisms, advanced fabrication techniques, and performance optimization strategies for ceramic-based EMI shielding materials. It provides an in-depth analysis of the key factors influencing shielding efficiency and discusses the shielding mechanisms and performance enhancement strategies for both conventional ceramics (e.g., silicon carbide and ferrites) and advanced ceramics (e.g., MXenes and high-entropy ceramics). Future research directions are identified, including wideband shielding design to meet the requirements of 5G and terahertz communication, the integration of mechanical, thermal, and electromagnetic functionalities, and the development of intelligent, responsive materials. Additionally, this review highlights the potential of machine learning (ML) and artificial intelligence (AI) in accelerating material design and performance optimization. By critically analyzing the interrelationships among material properties, fabrication processes, and shielding mechanisms, this work offers a comprehensive perspective on the innovative application of advanced ceramics in EMI shielding, with the aim of bridging the gap between fundamental research and industrial implementation. Electromagnetic shielding ; EMI ; Electromagnetic interference ; Silicon carbide ; Fabrication ; MXenes ; Ceramic ; Conducted electromagnetic interference Electromagnetic wave absorption materials ; Aluminum Alloys Composites Properties ; Material Properties and Applications
Authors: Wei Li | Zhiliang Cai | Song Li | Weixiong Wu
Source: Separation and Purification Technology (article, 2025-07-28)
Field: Chemistry — Inorganic Chemistry

Multitask transfer learning framework for predicting hydrogen storage performance of metal/covalent-organic frameworks across diverse operating conditions Metal-organic framework ; Covalent bond ; Hydrogen storage ; Covalent organic framework ; Transfer of learning ; Process engineering ; Chemistry ; Hydrogen ; Computer science ; Engineering ; Artificial intelligence ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Ohumi, Kazunori
Source: ArXiv.org (preprint, 2025-12-03)
Field: Materials Science — Materials Chemistry

Beyond Converging Representations A Philosophical Response on the Interpretation Risks of Scientific Foundation Models Machine learning models of vastly different modalities and architectures are being trained to predict the behavior of molecules, materials, and proteins. However, it remains unclear whether they learn similar internal representations of matter. Understanding their latent structure is essential for building scientific foundation models that generalize reliably beyond their training domains. Although representational convergence has been observed in language and vision, its counterpart in the sciences has not been systematically explored. Here, we show that representations learned by nearly sixty scientific models, spanning string-, graph-, 3D atomistic, and protein-based modalities, are highly aligned across a wide range of chemical systems. Models trained on different datasets have highly similar representations of small molecules, and machine learning interatomic potentials converge in representation space as they improve in performance, suggesting that foundation models learn a common underlying representation of physical reality. We then show two distinct regimes of scientific models: on inputs similar to those seen during training, high-performing models align closely and weak models diverge into local sub-optima in representation space; on vastly different structures from those seen during training, nearly all models collapse onto a low-information representation, indicating that today's models remain limited by training data and inductive bias and do not yet encode truly universal structure. Our findings establish representational alignment as a quantitative benchmark for foundation-level generality in scientific models. More broadly, our work can track the emergence of universal representations of matter as models scale, and for selecting and distilling models whose learned representations transfer best across modalities, domains of matter, and scientific tasks. Generality ; Representation (politics) ; Interpretation (philosophy) ; Scientific modelling ; Foundation (evidence) ; Convergence (economics) ; Modalities ; Range (aeronautics) ; Feature learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods