jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tatsuya Joutsuka | Yoshiteru Itagaki
Source: Journal of Computational Chemistry (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Computational Evaluation of Molecular Binding on Metal Surfaces in Temperature Programmed Desorption: Accuracy of <scp>DFT</scp> Functionals and Neural Network Potentials Understanding molecular adsorption and desorption on metal surfaces is crucial for heterogeneous catalysis and surface science. In this work, we systematically evaluate the performance of various density functional theory (DFT) exchange-correlation functionals-PBE, PBE-D3, revPBE-D3, optB88-vdW, BEEF-vdW, and SCAN+rVV10-and a neural network potential for modeling molecular binding relevant to temperature programmed desorption (TPD). Using well-characterized systems, we compare computed bulk lattice constants, surface energies, and binding energies of representative adsorbates (CO, CO<sub>2</sub>, methanol, and benzene) on transition metal surfaces (Ni, Cu, Ru, Rh, Pd, Ag, Pt, Au) against experimental TPD data. Our results reveal that dispersion-corrected functionals like PBE-D3 and SCAN+rVV10 yield accurate bulk lattice constants, while BEEF-vdW tends to overestimate them. optB88-vdW and SCAN+rVV10 accurately reproduce surface energies, while PBE-D3 and revPBE-D3 often overestimate them. On the other hand, BEEF-vdW provides better agreement for binding energies but at the cost of less accurate bulk properties. We further demonstrate how machine-learned potentials can efficiently reproduce DFT-level energetics and enable molecular dynamics simulations to extract more realistic kinetic parameters, including pre-exponential factors and potential of mean force (PMF) profiles. This study demonstrates the accuracy of modeling molecular adsorption using DFT and neural network potentials, elucidating the trade-offs associated with functional selection in surface science and offering practical guidance for choosing appropriate computational methods for accurately simulating desorption processes. Desorption ; Molecular dynamics ; Density functional theory ; Adsorption ; Molecular binding ; Binding energy ; Yield (engineering) ; Lattice (music) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Pengmei, Zihan | Guo, Spencer C. | Lorpaiboon, Chatipat | Dinner, Aaron R.
Source: ArXiv.org (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Hierarchical geometric deep learning enables scalable analysis of molecular dynamics Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Graph neural networks (GNNs) in which messages are passed between nodes that represent atoms that are spatial neighbors promise to obviate manual feature engineering, but the use of GNNs with biomolecular systems of more than a few hundred residues has been limited in the context of analyzing dynamics by both difficulties in capturing the details of long-range interactions with message passing and the memory and runtime requirements associated with large graphs. Here, we show how local information can be aggregated to reduce memory and runtime requirements without sacrificing atomic detail. We demonstrate that this approach opens the door to analyzing simulations of protein-nucleic acid complexes with thousands of residues on single GPUs within minutes. For systems with hundreds of residues, for which there are sufficient data to make quantitative comparisons, we show that the approach improves performance and interpretability. Scalability ; Context (archaeology) ; Deep learning ; Feature (linguistics) ; Graph ; Artificial neural network ; Deep neural networks Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Graph Neural Networks
Authors: Sergei Manzhos | 恵佑 亀田 | Keisuke Kameda | 学 伊原 | Manabu Ihara
Source: Institutional Repositories DataBase (IRDB) (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Neural network - Gaussian process regression hybrid for insightful machine learning: from materials informatics to DFT functional development Artificial neural network ; Process (computing) ; Gaussian process ; Development (topology) ; Regression ; Noisy data ; Informatics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Hirofumi Sumi | Mizuki Momai | Yuki Yamaguchi
Source: Journal of Alloys and Metallurgical Systems (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

An attempt toward machine learning-driven optimization of manufacturing processes for metal-supported solid oxide fuel cells using nickel and iron oxides as raw support materials A novel fabrication method for metal-supported solid oxide fuel cells (SOFCs) is proposed using nickel and iron oxides as raw support materials. This approach enables the co-sintering of all components in their oxide states via conventional ceramic processing. To minimize cell warpage, which is a critical issue in mechanical integrity, machine learning models, including random forest and linear regression, are employed to optimize the manufacturing process parameters. The random forest regression model demonstrated superior predictive accuracy (RMSE = 0.01454 and R 2 = 0.7310) than the linear regression model (RMSE = 0.02253 and R 2 = 0.4642), effectively capturing the non-linear relationships between the manufacturing process parameters and cell warpage. The electrolyte thickness, presintering temperature, and Ni/(Fe+Ni) atomic ratio are identified as key factors influencing cell warpage. Material characterizations using X-ray diffraction and coefficient of thermal expansion measurements validate these predictions. Although the open circuit voltage remains below the theoretical electromotive force owing to residual microstructural defects in the electrolyte, the cell warpage successfully reduced from 0.07578 to 0.03825 by increasing the presintering temperature from 1150 to 1250 °C. These results demonstrate the potential of machine learning for guiding manufacturing process optimization of mechanically robust and high-performance metal-supported SOFCs. Raw material ; Oxide ; Electromotive force ; Thermal expansion ; Solid oxide fuel cell ; Ceramic ; Process (computing) ; Nickel ; Process optimization Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrophoretic Deposition in Materials Science
Authors: Yanyi Yang | Long Xia | Jianhong Dai | Xiaochi Liu | Dongyan Zheng | Juexian Cao | Yong Hu
Source: The Journal of Physical Chemistry Letters (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Interpretable Identification of Single-Molecule Charge Transport via Fusion Attention-Based Deep Learning Interpretability is fundamental in the precise identification of single-molecule charge transport, and its absence in deep learning models is currently the major barrier to the usage of such powerful algorithms in the field. Here, we have pioneered a novel identification method employing fusion attention-based deep learning technologies. Central to our approach is the innovative neural network architecture, SingleFACNN, which integrates convolutional neural networks with a fusion of multihead self-attention and spatial attention mechanisms. Our findings demonstrate that SingleFACNN accurately classifies the three-type and four-type STM-BJ data sets, leveraging the convolutional layers' robust feature extraction and the attention layers' capacity to capture long-range interactions. Through comprehensive gradient-weighted class activation mapping and ablation studies, we identified and analyzed the critical features impacting classification outcomes with remarkable accuracy, thus enhancing the interpretability of our deep learning model. Furthermore, SingleFACNN's application was extended to mixed samples with varying proportions, achieving commendable prediction performance at low computational cost. Our study underscores the potential of SingleFACNN in advancing the interpretability and credibility of deep learning applications in single-molecule charge transport, opening new avenues for single-molecule detection in complex systems. Fusion ; Identification (biology) ; Charge (physics) ; Deep learning ; Artificial intelligence ; Computer science ; Physics ; Philosophy ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Weihuan Li | Chenchen Xiong | Yang Zhou | Yangzezhi Zheng | Jiarui Xing | Yanji Jin | Yuling Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deciphering the Initial Hydration Reaction of Tricalcium Aluminate Based on Ab Initio-Accurate Machine Learning Force Field Ab initio ; Aluminate ; Field (mathematics) ; Force field (fiction) ; Materials science ; Artificial intelligence ; Chemistry ; Computer science ; Mathematics ; Metallurgy ; Cement ; Organic chemistry Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Mineral Processing and Grinding
Authors: Tingyang Xing (22217422) | Panyi Mao (22217425) | Shixian Wang (6405995) | Hao Ye (519545) | Shoucheng Yang (22217428) | Weikang Jiang (12899964) | Xiaozhong Qiu (308209) | Yanling Shi (5280629) | Lijun Wu (152858)
Source: Figshare (article, 2025-09-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

GSH and HalidesDirected Controllable Synthesis ofChiral Gold Nanostars for Sensitive Diagnosis of Nephritis Types UsingSERS and Transformer Neural Network Surface-enhanced Raman spectroscopy (SERS) has shown potential for early disease diagnosis via urinary metabolomics, but still faces challenges in achieving stable hot spots and processing complex clinical data. In this study, the preparation of chiral gold nanostars with precisely controllable branch size, number, and sharpness was realized by investigating the effects of l-GSH and CTA<i>X</i> (<i>X</i> indicates halides) on site occupancy, reduction rate, and selective adsorption on crystal facets. Raman spectroscopic characterization using rhodamine 6G (R6G) as a reporter molecule revealed that nanoparticles with fewer branches, larger branch bases, and smoother surfaces exhibited excellent SERS activity, with an analytical enhancement factor (AEF) of 5.49 × 10<sup>6</sup> in the nonresonance region, which was also supported by finite-difference time-domain (FDTD) calculations. The hierarchical self-assembly of gold nanostructures was achieved by introducing a hydrophobic template to control the substrate size, significantly improving the reproducibility of SERS substrates. Combined with a Transformer neural network, this study achieved 99.94% diagnostic accuracy in distinguishing urine samples from healthy individuals and patients with acute interstitial nephritis and nonacute interstitial nephritis, demonstrating the potential of SERS and deep learning in clinical diagnostics. Rhodamine 6G ; Raman spectroscopy ; Colloidal gold ; Molecule ; Clinical diagnosis ; Nanostructure ; Nanoparticle ; Surface-enhanced Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: I. S. Galtsov | R. V. Muratov | G. V. Vyskvarko | Semen Murzov | S. A. Dyachkov | P. R. Levashov
Source: ArXiv.org (preprint, 2025-11-28)
Field: Materials Science — Materials Chemistry

MDcraft -- a modern molecular dynamics simulation package with machine learning potentials support Molecular dynamics is widely used to study various phenomena, such as diffusion, shock wave propagation, and plasma dynamics. A wide range of software packages supports the expanding scope of molecular dynamics applications. However, the quality of simulations depends on force field approximations, ranging from simple models to direct quantum solutions. Recently, machine learning approaches for constructing accurate interatomic potentials have received significant attention. In MDcraft, we integrate these advances into a scalable, physically accurate framework. MDcraft is a comprehensive, modern molecular dynamics platform. It offers a high-level Python API with a user-friendly, script-based interface. The core simulation algorithms are implemented in C++ to ensure robustness and computational efficiency. MDcraft is built for high-performance computing on modern clusters and supports dynamic domain decomposition and load balancing via the Message Passing Interface (MPI) for scalable parallelization. Additionally, MDcraft leverages multithreading within nodes through standard C++ parallelism, enabling efficient use of heterogeneous architectures. We demonstrate the code's capabilities through several examples, including the shock response in aluminum, the shock Hugoniot in argon, and the cold curve of copper. Scalability ; Multithreading ; Python (programming language) ; Robustness (evolution) ; Molecular dynamics ; Software ; Ranging Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; High-pressure geophysics and materials
Authors: A. F. Schmidt | A. A. Kurokhtina | E. V. Larina | N. A. Lagoda
Source: Russian Chemical Bulletin (article, 2025-11-01)
Field: Chemistry — Organic Chemistry

New applications of kinetic data. The development of machine learning kinetic models for the Suzuki—Miyaura reaction Kinetic energy ; Work (physics) ; Range (aeronautics) ; Process (computing) ; Product (mathematics) ; Training set ; Set (abstract data type) ; Catalysis Catalytic Cross-Coupling Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation ; Machine Learning in Materials Science
Authors: Liangzhi Qiao | Aijun Tong | Yilan Liao | Shan‐Jing Yao | Dong‐Qiang Lin
Source: Chemical Engineering Science (article, 2025-07-16)
Field: Chemistry — Spectroscopy

Quantitative relationships between structure characteristics and mass transfer for chromatographic resin by combining digital material techniques and machine learning algorithms Mass transfer ; Algorithm ; Chromatography ; Computer science ; Process engineering ; Chemistry ; Materials science ; Engineering Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications
Authors: Soham Mandal | Ashutosh Srivastava | Tanmoy Das | Abhishek K. Singh | Prabal K. Maiti
Source: Small (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Probing Lattice Anharmonicity and Thermal Transport in Ultralow‐κ Materials Using Machine Learning Interatomic Potentials Crystalline solids with ultralow lattice thermal conductivity (κ) are highly sought after for thermoelectric energy conversion and thermal barrier coating applications. However, a comprehensive theoretical understanding of heat transport in strongly anharmonic materials remains limited, as conventional perturbative frameworks such as the Boltzmann transport equation (BTE) break down when anharmonicity is too strong to be treated as a "small" perturbation. Herein, machine learning interatomic potentials (MLIP) are developed to investigate thermal transport in TlAgSe, a metal chalcogenide, and Cs<sub>2</sub>PbI<sub>2</sub>Cl<sub>2</sub>, an all-inorganic layered Ruddlesden-Popper perovskite. The anharmonic lattice dynamics, structural properties, and finite-temperature distortions are examined using MLIP-driven molecular dynamics (MD) simulations, revealing local symmetry breaking typical of ultralow-κ (<1 Wm<sup>-1</sup>K<sup>-1</sup>) materials. The linear response theory-based Green-Kubo (GK) framework, implemented via equilibrium MD simulations, is employed to calculate the κ. The non-perturbative GK framework captures all anharmonic effects of underlying interatomic potentials and yields κ closely matching experimental values. Phonon scattering rates exceeding the Ioffe-Regel limit and the degree of anharmonicity σ<sup>A</sup> > 0.5 confirm the strongly anharmonic nature of both materials. This MLIP-integrated theoretical and numerical framework enhances the physical understanding of heat transport and guides the design of ultralow-κ materials. Anharmonicity ; Phonon ; Thermal conductivity ; Interatomic potential ; Boltzmann equation ; Scattering ; Thermal Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Perovskite Materials and Applications
Authors: Mohamed‐Ouejdi Belarbi | Abdelhak Khechai | A. Bouhdjar | Phạm Văn Vĩnh | Aman Garg | Lee Li | Akhil Ranjan Garg
Source: Neurocomputing (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Assessment of free vibration frequencies of nano-scale functionally graded materials using hybrid machine learning approaches Hyperparameter ; Artificial neural network ; Support vector machine ; Vibration ; Mean squared error ; Variance (accounting) Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Composite Structure Analysis and Optimization
Authors: Lion Frangoulis (21805489) | Zahra Khatibi (21805492) | Lorenzo A. Mariano (20870567) | Alessandro Lunghi (2866130)
Source: Figshare (article, 2025-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generating New CoordinationCompounds via MultireferenceSimulations, Genetic Algorithms, and Machine Learning: The Case ofCo(II) and Dy(III) Molecular Magnets The design of coordination compounds with target properties often requires years of continuous feedback loop between theory, simulations, and experiments. In the case of magnetic molecules, this conventional strategy has indeed led to the breakthrough of single-molecule magnets with working temperatures above liquid nitrogen’s boiling point, but at significant costs in terms of resources and time. Here, we propose a computational strategy capable of accelerating the discovery of new coordination compounds with the desired electronic and magnetic properties. Our approach is based on a combination of high-throughput multireference ab initio methods, genetic algorithms, and machine learning. While genetic algorithms allow for an intelligent sampling of the vast chemical space available, machine learning reduces the computational cost by prescreening molecular properties in advance of their accurate and automated multireference ab initio characterization. Importantly, the presented framework is able to generate novel organic ligands and explore chemical motifs beyond those available in pre-existing structural databases. We showcase the power of this approach by automatically generating new Co(II) and Dy(III) mononuclear coordination compounds with record magnetic properties in a fraction of the time required by either experiments or brute-force ab initio approaches. In the case of Dy compounds, simulations uncover new nontrivial chemical strategies toward pentagonal bipyramidal complexes with record-breaking values of magnetic anisotropy. Ab initio ; Coordination complex ; Magnet ; Chemical space ; Ab initio quantum chemistry methods ; Genetic algorithm Magnetism in coordination complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Luke H. Elder | Alexey V. Onufriev
Source: ChemRxiv (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution The accuracy of computational models of water is key to atomistic simulations of biomolecules. Here we explore a decoupled framework that combines classical physics- based models with deep neural networks (DNNs) to correct residual error in hydration free energy (HFE) prediction. Our main goal is to evaluate this framework on out-of- distribution data (molecules that differ significantly from those used in training), where DNNs are known to struggle. Several common physics-based solvation models are used in the evaluation. Graph neural network architectures are tested for their ability to generalize using multiple dataset splits, including out-of-distribution HFEs and unseen molecular scaffolds. Our most important finding is that for out-of-distribution data, where DNNs alone often struggle, the physics + DNN models consistently improve physics model predictions. For in-distribution data, the DNN corrections significantly improve the accuracy of physics-based models, with a final RMSE below 1 kcal/mol and a relative improvement between 40% and 65% in most cases. The accuracy of physics + DNN models tends to improve when the 6% of molecules with the highest experimental uncertainty are removed. This study provides insights into the potential and limitations of combining physics and machine learning for molecular modeling, offering a practical and generalizable strategy. Distribution (mathematics) ; Set (abstract data type) ; Artificial neural network ; Training (meteorology) ; Training set ; Graph ; Statistical physics ; Computer science ; Deep neural networks ; Energy (signal processing) ; Energy distribution ; Physics ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Quantum mechanics ; Atomic physics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Daniel Zanetti de Florio | Arlete Santos | J. Almeida | Fabiane de Jesus Trindade | Marissol Rodrigues Felez
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Prediction and Synthesis of New High-Entropy Flourite Oxide Artificial intelligence ; Oxide ; Machine learning ; Entropy (arrow of time) ; Computer science ; Materials science ; Thermodynamics ; Physics ; Metallurgy Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; X-ray Diffraction in Crystallography
Authors: Dongfu Xie | Zhenghong Yu | Juin‐Yih Lai | Yufeng Luo | Xiong Li | Xudong Sun | Yanjun Wei | W Zeng
Source: Scientific Reports (article, 2025-12-18)
Field: Chemistry — Analytical Chemistry

Research on the detection of foreign materials in tobacco shreds based on hyperspectral reflection imaging technology combined with machine learning Plastic and paper foreign materials in tobacco shreds mainly originate from tobacco processing and packaging. These materials are highly similar to tobacco shreds in color and size, making them difficult for traditional machine vision systems to detect, thus posing a significant risk to product quality and safety. To address this issue, this study proposes a method combining hyperspectral imaging (HSI) and machine learning. After segmenting and denoising hyperspectral images to extract pixel-level spectral data, principal component analysis (PCA) is used to construct classification models of support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF). The PCA dimensionality reduction results show that SVM accuracy decreased by 0.08%, LDA by 2.67%, while RF maintained a 100% accuracy rate. The LDA model trained with full spectral bands achieved 100% accuracy, demonstrating better robustness and generalization ability. The final classification results are mapped back to the original hyperspectral images for visual classification and detection of foreign materials. This method can reliably detect partially obscured foreign materials. This study overcomes the limitations of detection based on color and size, providing a solution for real-time quality control in the food and pharmaceutical industries. Hyperspectral imaging ; Support vector machine ; Dimensionality reduction ; Discriminative model ; Linear discriminant analysis ; Random forest ; Robustness (evolution) ; Principal component analysis Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Remote-Sensing Image Classification