jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michele Tomasini | Michal Szostak | Albert Poater
Source: Asian Journal of Organic Chemistry (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Machine Learning in Predicting Activation Barrier Energy of C=N Bond Rotation in Amides Abstract This study applies machine learning (ML) to predict activation energy barriers for cis‐trans isomerization in twisted amides, focusing on the C=N bond. Amides are increasingly used in synthetic chemistry, particularly in cross‐coupling reactions, due to their versatility. However, the C=N bond's high activation energy presents a challenge. Using Density Functional Theory (DFT) calculations, the study evaluates key structural parameters and energy barriers for different amides. ML models, including support vector regression and neural networks, are developed to predict these activation barriers based on molecular descriptors. The results show that twisted amides, particularly N ‐Ts and N ‐Boc types, exhibit unique reactivities influenced by steric and electronic factors. The neural network model outperformed other methods with R 2 values of around or over 0.9 for ΔG ≠ and ΔG trans , though overfitting remains a concern. These findings contribute to a deeper understanding of amide reactivity, facilitating the design of more efficient catalytic systems. Chemistry ; Steric effects ; Overfitting ; Isomerization ; Reactivity (psychology) ; Peptide bond ; Computational chemistry ; Density functional theory ; Artificial neural network ; Amide ; Electronic effect ; Catalysis ; Stereochemistry ; Organic chemistry ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Ammonia Synthesis and Nitrogen Reduction
Authors: Seyed Amin Khatami | Pejman Rezaei | Pouria Zamzam | Soheil Hadipour | Bhargav Appasani
Source: Diamond and Related Materials (article, 2025-10-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comprehensive sensing framework for sugar molecules based on graphene absorber: Coupled mode theory, transmission line analysis, and deep learning integration Terahertz radiation ; Metamaterial ; Metamaterial absorber ; Deep learning ; Polarization (electrochemistry) ; Transmission line ; Absorption (acoustics) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Energy Harvesting in Wireless Networks
Authors: R. J. Debski | W. Gąsior | Wojciech Gierlotka | A. Dębski
Source: Symmetry (article, 2025-08-09)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Prediction of Mixing Enthalpy in Binary Alloys with Symmetry-Augmented Embeddings The modeling of the enthalpy of mixing in binary alloys is essential to thermodynamic assessments and computational alloy design, particularly in data-scarce systems where experimental measurements are limited or incomplete. In this work, we propose a machine learning framework for the prediction of mixing enthalpy in binary alloys under conditions of limited data availability. The method integrates symmetry-augmented embeddings, which enforce physical invariances such as element permutation and compositional mirroring, ensuring consistency across chemically equivalent representations and capturing chemically meaningful similarities between elements, thereby supporting knowledge transfer across alloy systems. To account for data uncertainty and improve trust in predictions, we incorporate Bayesian neural networks, enabling the estimation of predictive confidence, especially in composition ranges lacking experimental data. The model is trained jointly across multiple binary alloy systems, allowing it to share structural insights and improve prediction quality in data-limited concentration intervals. The method achieves a reduction in mean absolute error by more than a factor of eight compared with the classical Miedema model (0.53 kJ·mol−1 vs. 4.27 kJ·mol−1) while maintaining consistent accuracy even when trained on only 25% of the experimental measurements, confirming its robustness thanks to cross-alloy knowledge transfer and symmetry-based data augmentation. We evaluate the method on a benchmark dataset containing both fully and partially characterized binary alloy systems and demonstrate its effectiveness in interpolating and extrapolating enthalpy values while providing reliable uncertainty estimates. The results highlight the value of incorporating domain-specific symmetries and uncertainty-aware learning in data-driven material modeling and suggest that this approach can support predictive thermodynamic assessments even in under-sampled systems. Binary number ; Computer science ; Enthalpy ; Benchmark (surveying) ; Binary data ; Thermodynamics ; Algorithm ; Statistical physics ; Machine learning ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Jisu Kim | Jiho Lee | Sangmin Oh | Yutack Park | Seungwoo Hwang | Seungwu Han | Sungwoo Kang | Youngho Kang
Source: npj Computational Materials (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods. Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient. However, this approach often induces catastrophic forgetting, undermining the generalizability that is a key advantage of pretrained MLIPs. Herein, we propose reEWC, an advanced fine-tuning strategy that integrates Experience Replay and Elastic Weight Consolidation (EWC) to effectively balance forgetting prevention with fine-tuning efficiency. Using Li6PS5Cl (LPSC), a sulfide-based Li solid-state electrolyte, as a fine-tuning target, we show that reEWC significantly improves the accuracy of a pretrained MLIP, resolving well-known issues of potential energy surface softening and overestimated Li diffusivities. Moreover, reEWC preserves the generalizability of the pretrained MLIP and enables knowledge transfer to chemically distinct systems, including other sulfide, oxide, nitride, and halide electrolytes. Compared to Experience Replay and EWC used individually, reEWC delivers clear synergistic benefits, mitigating their respective limitations while maintaining computational efficiency. These results establish reEWC as a robust and effective solution for continual learning in MLIPs, enabling universal models that can advance materials research through large-scale, high-throughput simulations across diverse chemistries. Generalizability theory ; Forgetting ; Key (lock) ; On the fly ; Generalization Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Source: Chinese Journal of Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Study of OH-Induced Disintegration of Cu/ZnO Catalysts Based on Machine Learning Potentials Catalysis ; Molecular dynamics ; Dynamics (music) ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Computer science ; Chemical physics ; Computational chemistry ; Physics ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning and ELM
Authors: Jiamin Ren | Xing Yang | Zhibin Wen | Xiaoguang Gao | Fangjun Jin | Yingqian Wang
Source: Biosensors and Bioelectronics (article, 2025-11-05)
Field: Chemistry — Inorganic Chemistry

Machine learning-assisted dual-amplified visual platform based on fluorescent metal-organic framework and photonic crystals enables ATP detection for pathogen monitoring Aptamer ; Fluorescence ; Adenosine triphosphate ; Sensitivity (control systems) ; SIGNAL (programming language) ; Linear range ; Nanoclusters ; Visualization Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications
Authors: Hoje Chun | Minjoon Hong | Seung Hyo Noh | Byungchan Han
Source: Journal of Chemical Theory and Computation (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Learning Pairwise Interaction for Extrapolative and Interpretable Machine Learning Interatomic Potentials with Physics-Informed Neural Network Achieving both robust extrapolation and physical interpretability in machine learning interatomic potentials (ML-IPs) for atomistic simulation remains a significant challenge, particularly in data-scarce areas such as chemical reactions or complex, multicomponent materials at extreme conditions. Here, we present a pairwise-decomposed physics-informed neural network (P2Net) that parametrizes an analytical bond-order potential (BOP) layer to decouple the energy contributions of atomic pairs. By leveraging fundamental physical principles, P2Net demonstrates excellence at extrapolating beyond its training regime and accurately capturing molecular geometries far from equilibrium. The pairwise energy decomposition further empowers the bond analyses for deprotonation and S<sub>N</sub>2 reactions, which is not easy with most ML-IPs. The atomic pair energy offers how to elucidate the evolution of interatomic interactions as a reaction proceeds. Our methodology highlights enhanced data efficiency in building ML-IPs and facilitates more informative postsimulation analysis, thereby broadening the applicability of ML-IPs to complex and reactive systems. Pairwise comparison ; Artificial neural network ; Artificial intelligence ; Machine learning ; Computer science ; Data science Machine Learning in Materials Science ; Topic Modeling ; Neural Networks and Applications
Authors: Saliha Saher | Tu C. Le | Tamar L. Greaves | Jennifer M. Pringle | Douglas R. MacFarlane | Karolina Matuszek
Source: Materials Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-based predictions of melting temperature and enthalpy of fusion for protic organic salt phase change materials Linear and non-linear machine learning models predicted T m and Δ H f of 182 protic salts, achieving R 2 as high as 0.82. Experimental cross-validation demonstrated the acceptable predictive ability of linear models for both T m and Δ H f . Enthalpy of fusion ; Enthalpy ; Salt (chemistry) ; Fusion ; Thermodynamics ; Melting temperature ; Phase change ; Phase (matter) ; Melting point ; Chemistry ; Materials science ; Physical chemistry ; Organic chemistry ; Physics ; Philosophy Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices
Authors: Jingjing Si | Jiaheng Jiang | Shihao Su | Yuming Zhang | Mengxiang Du | JingChao Xu | Huimin Jia | Zhi Zheng | Weiwei He
Source: Journal of Chemical Education (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Synthesis and Peroxidase-like Activity of 2D FeB Nanozyme for Antioxidant Detection: An Undergraduate Open Platform Practice Course Nanozymes have garnered widespread concern due to their broad application prospects in molecular detection, disease diagnosis, and treatment. To cultivate the practical innovation skills of undergraduates, an undergraduate open platform practical course related to the synthesis, characterization, and peroxidase-like activity of novel 2D FeB nanozymes and their applications in detecting antioxidants was thoughtfully designed. A liquid molten salt method was first developed for the synthesis of high-purity single-phase 2D FeB nanozymes, utilizing thermodynamic principles (Gibbs free energy and chemical potential) taught in physical chemistry courses to overcome challenges in material synthesis. The raw materials used are easily accessible, and the synthesis procedure is straightforward and safe. Students characterized the nanozymes using standard undergraduate-level techniques such as transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, electron paramagnetic resonance, and ultraviolet–visible spectroscopy. Additionally, machine learning methods were integrated to analyze sample detection results, significantly enhancing detection efficiency and accuracy. By bridging cutting-edge nanomaterials with daily life relevance, this course strengthens students’ technical expertise, interdisciplinary problem-solving skills, and collaborative innovation. Peroxidase ; Antioxidant ; Chemistry ; Course (navigation) ; Mathematics education ; Environmental chemistry ; Biochemistry ; Psychology ; Engineering ; Enzyme Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Juhi Jannat Mim | Abdullah Al Mamun | Mahtab Hossain Nayem | Saifuddin Mahmud | Antu Nath | S M Maksudur Rahman | Shekh Asraful Fidal | Nayem Hossain
Source: Materials Today Communications (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review Process optimization ; Process (computing) ; Process design ; Engineering design process ; Materials processing ; Material Design ; Design of experiments Machine Learning in Materials Science ; Nanoporous metals and alloys ; Laser-Ablation Synthesis of Nanoparticles
Authors: Şener Özönder | Hatice Kübra KÜÇÜKKARTAL
Source: The Journal of Physical Chemistry A (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel Grid searching a large and high-dimensional chemical space with density functional theory (DFT) to discover new materials with desired properties is prohibitive due to the high computational cost. We propose an approach utilizing Bayesian optimization (BO) with an artificial neural network kernel to enable an efficient and low-cost guided search on the chemical space, avoiding costly brute-force grid search. This method leverages the BO algorithm, where the kernel neural network trained on a limited number of DFT results determines the most promising regions of the chemical space to explore in subsequent iterations. This approach aims to discover new materials with target properties while minimizing the number of DFT calculations required. To demonstrate the effectiveness of this method, we investigated 63 doped graphene quantum dots (GQDs) with sizes ranging from 1 to 2 nm to find the structure with the highest light absorption. Using time-dependent DFT (TDDFT) only 12 times, we achieved a significant reduction in computational cost, approximately 20% of what would be required for a full grid search. Considering that TDDFT calculations for a single GQD require about half a day of wall time on high-performance computing nodes, this reduction is substantial. Our approach can be generalized to the discovery of new drugs, chemicals, crystals, and alloys in high-dimensional and large chemical spaces, offering a scalable solution enabled by the neural network kernel. Kernel (algebra) ; Graphene ; Artificial neural network ; Bayesian optimization ; Bayesian probability ; Absorption (acoustics) ; Computer science ; Materials science ; Biological system ; Pattern recognition (psychology) ; Machine learning ; Artificial intelligence ; Nanotechnology ; Mathematics ; Discrete mathematics ; Biology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Jian Zhang | Zhuo Zhao | Miao Jiang | Yuan Cheng | Gang Zhang
Source: Physical Review Materials (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Thermal properties of hexagonal diamond: Machine learning potential and molecular dynamics study Molecular dynamics ; Hexagonal crystal system ; Thermal ; Dynamics (music) Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Thermal properties of materials
Authors: Ethan P. Shapera | Cheng‐Wei Lee
Source: Physical review. A/Physical review, A (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Neural networks for the prediction of electronic excitation dynamics We demonstrate a machine-learning-based approach that can learn the time-dependent electronic excitation dynamics of small molecules subjected to ion irradiation. Ensembles of neural networks are trained on data generated by time-dependent density-functional theory to relate atomic positions to occupations of molecular orbitals. New data are incrementally and efficiently added to the training data using an active learning process, thereby improving model accuracy. Predicted changes in orbital occupations made by the neural network ensemble are found to have errors and one-standard-deviation uncertainties which are two orders of magnitude smaller than the typical values of the orbital occupation numbers. The trained neural network ensembles demonstrate a limited ability to generalize to molecules not used to train the models. In such cases, the models are able to identify key qualitative features, but struggle to match the quantitative values. The machine-learning procedure developed here is potentially broadly applicable and has the potential to enable study of broad ranges of both materials and dynamical processes by drastically lowering the computational cost and providing surrogate model for multiscale simulations. Dynamics (music) ; Excitation ; Artificial neural network ; Computer science ; Neuroscience ; Statistical physics ; Artificial intelligence ; Physics ; Psychology ; Acoustics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Shuyi Jia | Panchapakesan Ganesh | Victor S.C. Fung
Source: ArXiv.org (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

Electronic Structure Guided Inverse Design Using Generative Models The electronic structure of a material fundamentally determines its underlying physical, and by extension, its functional properties. Consequently, the ability to identify or generate materials with desired electronic properties would enable the design of tailored functional materials. Traditional approaches relying on human intuition or exhaustive computational screening of known materials remain inefficient and resource-prohibitive for this task. Here, we introduce DOSMatGen, the first instance of a machine learning method which generates crystal structures that match a given desired electronic density of states. DOSMatGen is an E(3)-equivariant joint diffusion framework, and utilizes classifier-free guidance to accurately condition the generated materials on the density of states. Our experiments find this approach can successfully yield materials which are both stable and match closely with the desired density of states. Furthermore, this method is highly flexible and allows for finely controlled generation which can target specific templates or even individual sites within a material. This method enables a more physics-driven approach to designing new materials for applications including catalysts, photovoltaics, and superconductors. Intuition ; Generative grammar ; Template ; Inverse ; Electronic structure ; Electronic systems ; Joint (building) Machine Learning in Materials Science ; Topological Materials and Phenomena ; Electrocatalysts for Energy Conversion
Authors: Qian Chen | Siwen Wang | Chen Ling
Source: Journal of Energy Chemistry (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Redefining atomistic simulations of all-solid-state batteries through machine learning interatomic potentials Interatomic potential ; Materials science ; Solid-state ; Molecular dynamics ; Statistical physics ; Chemical physics ; Engineering physics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Trofim Polikovskiy | Victoria E. Gontcharenko | Vladislav M. Korshunov | Mikhail T. Metlin | Daria A. Metlina | Sergey V Samsonov | Yu. А. Belousov | Carlos D. S. Brites | Luís Carlos | Ilya V. Taydakov
Source: Materials Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Combining materials design and deep learning: AI-enhanced luminescence thermometry with a novel Eu <sup>3+</sup> /Tb <sup>3+</sup> polymeric coordination compound The study explores the effectiveness of various machine learning algorithms for luminescence thermometry. We demonstrate that the Convolutional Neural Network outperforms other machine learning algorithms and the ratiometric classical approach. Luminescence ; Convolutional neural network ; Coordination complex ; Deep learning Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Machine Learning in Materials Science
Authors: Remah A.S. Radi | Mohamed Mostafa Hassan | Fatima Jabbar Hassoun | Hussein Ali Kadhim Kyhoiesh | Hassan E. Abd Elsalam | Islam H. El Azab
Source: Journal of Sulfur Chemistry (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Multiscale machine learning strategies for designing high-performance indaceno-bithiophene-based polymers Chemistry ; Polymer ; Nanotechnology ; Organic chemistry ; Materials science Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Sandeep Kumar Singh | Parth Joshi | Abhishek Sharma | Arti Kashyap
Source: Journal of Magnetism and Magnetic Materials (article, 2025-09-27)
Field: Materials Science — Materials Chemistry

A simple feature enriched CGCNN for predicting magnetization in transition metal compounds Ferrimagnetism ; Magnetization ; Magnetism ; Convolutional neural network ; Ferromagnetism ; Graph ; Artificial neural network ; Spin (aerodynamics) ; Feature (linguistics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Y. Shiraki | Hiromasa Kaneko
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-03-11)
Field: Chemistry — Organic Chemistry

Correlations between the constituent molecules, crystal structures, and dielectric constants in organic crystals Organic crystals are crystals composed of molecules of organic compounds. Materials made from organic crystals are used in devices, such as capacitors and ferroelectric memories. It is desirable to develop new materials with improved physical properties such as the dielectric constant (DC). However, the relationship between the constituent molecule (CM), crystal structure (CS), and DC of the organic crystals is not clearly understood. In this study, we investigated the relationship between CM, CS, and DC with existing data and machine learning. Using regression analysis, we could construct machine learning models between CM and DC, between CS and DC, and between CM and CS, and could predict DC from CM, DC from CS, and CS from CM using the constructed models. • Organic crystals are crystals composed of molecules of organic compounds. • The relationship between molecules, crystal structure, and dielectric constant of organic crystals was modeled. • Prediction models between each of the physical properties were constructed. • The information on crystal structures could be predicted from molecular information. • Contributions to material design considering molecular, crystal structure and dielectric constant are expected. Dielectric ; Crystal (programming language) ; Molecule ; Materials science ; Organic molecules ; Crystallography ; Chemistry ; Chemical physics ; Computational chemistry ; Organic chemistry ; Computer science ; Optoelectronics Chemical Thermodynamics and Molecular Structure ; Solid-state spectroscopy and crystallography ; Advanced Chemical Sensor Technologies