jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amr Ismail | Santosh Biswas
Source: Journal of Nano Molecular Intelligence and Virtual Health Systems (article, 2025-09-14)
Field: Materials Science — Materials Chemistry

Hybrid Molecular Nano Frameworks for Disease Modeling Using Neuro Symbolic Machine Learning Disease modelling has advanced significantly when computational methods and molecular data are used. For prediction accuracy and interpretability, this initiative proposes to create a hybrid molecular nano framework for disease modelling employing neuro-symbolic machine learning, with Graph Attention Networks (GAT) as the central method. The GAT depicts molecules as graphs, and the technique interactively assigns focus to atom-bond interactions to extract structural and relational properties. These traits' anticipated disease-relevant relationships and biological objectives are paired with symbolic reasoning, which uses molecular similarity metrics (such as Tanimoto coefficients) and 3D structure data. One of the main discoveries is the successful use of GAT to capture necessary chemical substructures and achieve better prediction performance than conventional models. The hybrid framework showed excellent interpretability, effectively connecting biological targets like CYP2D6 and HERG to molecular patterns and offering insights into disease processes. Finally, combining GAT with symbolic reasoning reveals a viable strategy for molecular-based illness modelling by striking a balance between interpretability and prediction accuracy. Interpretability ; Similarity (geometry) ; Focus (optics) ; Task (project management) ; Computational model ; Graph ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Linus Lind | Hilda Sandström | Patrick Rinke
Source: ArXiv.org (preprint, 2025-10-23)
Field: Materials Science — Materials Chemistry

An interpretable molecular descriptor for machine learning predictions in atmospheric science The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are particularly affected because they often rely on dictionary-based descriptors tied to specific molecular substructures, which currently fail to capture the full range of organic atmospheric compounds, including large, highly oxidized molecules common in the atmosphere. We introduce ATMOMACCS, an interpretable descriptor combining the 166 binary keys of the MACCS fingerprint with motifs inspired by the SIMPOL method for estimating saturation vapor pressures. We show that ATMOMACCS based models improve predictions of saturation vapor pressures (7-8 % error reduction), equilibrium partition coefficients (5 % and 9 % error reduction), glass transition temperatures (22 % error reduction), and enthalpy of vaporization (61 % error reduction) on four datasets with atmospheric compounds. Feature analysis shows that saturation vapor pressure and partition coefficients are governed by carbon number and oxygen-related features, whereas other phase-transition properties (e.g., enthalpy of vaporization, glass transition temperature) depend on carbon-hydrogen bond types and the presence of heteroatoms other than oxygen. This highlights the generalizability of ATMOMACCS across different datasets and properties as an interpretable molecular descriptor. Generalizability theory ; Molecular descriptor ; Vapor pressure ; Enthalpy ; Enthalpy of vaporization ; Binary number ; Random forest ; Boiling point ; Atmospheric pressure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Wei Sun | Xinzhe Chen | Dongping Chen
Source: Proceedings of the Combustion Institute (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Thermal decomposition kinetics of plastic mixtures (PE/PVC and PE/PP) based on chemical reaction neural networks Thermal decomposition ; Thermogravimetric analysis ; Polyvinyl chloride ; Decomposition ; Kinetics ; Polypropylene ; Kinetic energy ; Polyethylene Polymer Science and PVC ; Thermal and Kinetic Analysis ; Polymer crystallization and properties
Authors: Shijie Sun | Yumeng Li
Source:  (article, 2025-08-17)
Field: Materials Science — Materials Chemistry

Thermal Properties of Defective Graphene: Insights From Molecular Dynamics With Machine Learning Potential Abstract Graphene’s exceptional thermal conductivity has attracted significant research attention for its potential in nanoscale electronic devices, yet its thermal performance is significantly impacted by structural defects. To systematically investigate the thermal properties of defective graphene, this study employs molecular dynamics (MD) simulations integrated with machine learning potentials (MLPs). Specifically, an artificial neural network (ANN) potential based on the Behler-Parrinello approach is developed and trained using density functional theory (DFT)-derived datasets. The ANN potential accurately reproduces DFT-calculated potential energies and phonon dispersion relations, confirming its reliability for simulating thermal transport phenomena. Using nonequilibrium molecular dynamics (NEMD), the study calculates the thermal conductivity of graphene as a function of temperature and defect concentration. Our results indicate a substantial reduction in thermal conductivity even at low defect concentrations, with a pronounced decrease of over 90% observed at just 1% vacancy concentration. Additionally, the phonon mean free path (MFP) analysis reveals enhanced phonon scattering due to defects, providing deeper insights into the mechanisms governing thermal transport in defective graphene. This work demonstrates the effectiveness of MLPs in accurately predicting complex thermal behaviors and highlights their potential for guiding the design of graphene-based materials with optimized thermal management capabilities for advanced electronic applications. Additionally, the methodology can be extended to a broad range of defect landscapes, making it a promising tool for future materials design. Thermal conductivity ; Phonon ; Molecular dynamics ; Thermal ; Graphene ; Mean free path ; Phonon scattering ; Work (physics) ; Nanoscopic scale Machine Learning in Materials Science ; Graphene research and applications ; Thermal properties of materials
Authors: Kazuhiro TAKEDA | Naoya Ohtsuka | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Synthesis of Prospective Organic Molecular Materials: An Algorithm with Latent Variables for Understanding and Predicting Experimentally Unobservable Reactions Unobservable ; Heuristic ; Property (philosophy) ; Latent variable ; Substitution (logic) ; Synthetic data ; Focus (optics) Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Bohan Zhang | Biyuan Liu | Penghua Ying | Zherui Chen | Yanzhou Wang | Yonglin Zhang | Haikuan Dong | Jinglei Yang | Zheyong Fan
Source: arXiv (Cornell University) (preprint, 2025-12-25)
Field: Materials Science — Materials Chemistry

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry to heat transport remains challenging. In this work, we develop a machine-learned neuroevolution potential (NEP) trained on an existing density functional theory dataset (\textit{Angew.\ Chem.\ Int.\ Ed.}, \textbf{63} , e202410088 (2024)), achieving reasonable accuracy at a computational cost much lower than the existing machine-learned and empirical potentials. Leveraging this potential, we perform large-scale molecular dynamics (MD) simulations to model the thermal reduction of GO across realistic structural domains. Using the homogeneous nonequilibrium MD method with a proper quantum-statistical correction scheme, we find that reduced GO exhibits strongly suppressed thermal conductivities, ranging from a few to tens of Wm$^{-1}$K$^{-1}$, substantially lower than pristine GO without defects and far below graphene. Moreover, the thermal conductivity of reduced GO increases moderately with increasing OH/O ratio, except at the highest oxidation level (O/C=0.5) where this trend inverts, while decreasing significantly with increasing O/C ratio, a trend strongly correlated with the fraction of recovered graphene-like structures. Our work provides a computationally tractable and predictive atomistic machine learning framework for exploring how chemical structure governs heat transport in heterogeneous carbon materials. Graphene ; Thermal conductivity ; Oxide ; Molecular dynamics ; Reduction (mathematics) ; Work (physics) ; Non-equilibrium thermodynamics ; Thermal Machine Learning in Materials Science ; Thermal properties of materials ; Model Reduction and Neural Networks
Authors: Ashutosh Tiwari | Widodo Widodo | Dyah Ika Krisnawati | Tsung‐Rong Kuo
Source: Advanced Theory and Simulations (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Physics‐Informed Machine Learning for Accurate and Physically Valid Prediction of Semiconductor Defect Levels Abstract Accurately predicting semiconductor defect energy levels with strong physical validity remains a major bottleneck in accelerating materials discovery. Routine density function theory (DFT) calculations based on generalized gradient approximations such as Perdeco Burke Ernzerhof (PBE) are computationally cheap yet systematically underestimate defect levels, while hybrid functionals such as Heyd Scuseria Ernzerhof (HSE) improve accuracy but require prohibitive computational cost. A physics informed machine learning model is presented for predicting HSE calculated defect concentrations from PBE defect results and host material descriptors. A multilayer perceptron with one hidden layer achieved strong numerical performance with test R2 equal to 0.9615 and mean absolute error equal to 0.1635 eV, yet produced large physical violations with 73.33 percent of predictions falling outside the host bandgap. Adding a physics informed loss reduced violation rates to 59.44 percent with R2 above 0.94. A sigmoid scaled output multilayer perceptron achieved zero percent violations but with reduced flexibility and R2 equal to 0.5169. SHAP analysis confirmed PBE defect levels and host bandgaps as dominant features. Incorporating physical constraints greatly enhanced scientific validity and supports practical scalable high throughput semiconductor defect sorting. Bottleneck ; Sigmoid function ; Perceptron ; Semiconductor ; Scalability ; Artificial neural network ; Hybrid functional ; Multilayer perceptron Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Big Data and Digital Economy
Authors: Chenbo Zhang | Xian Chen
Source: ArXiv.org (preprint, 2025-06-12)
Field: Materials Science — Materials Chemistry

FerroAI: A Deep Learning Model for Predicting Phase Diagrams of Ferroelectric Materials Composition-temperature phase diagrams are crucial for designing ferroelectric materials, however predicting them accurately remains challenging due to limited phase transformation data and the constraints of conventional methods. Here, we utilize natural language processing (NLP) to text-mine 41,597 research articles, compiling a dataset of 2,838 phase transformations across 846 ferroelectric materials. Leveraging this dataset, we develop FerroAI, a deep learning model for phase diagram prediction. FerroAI successfully predicts phase boundaries and transformations among different crystal symmetries in Ce/Zr co-doped BaTiO$3$ (BT)-$x$Ba${0.7}$Ca$_{0.3}$TiO$_3$ (BCT). It also identifies a morphotropic phase boundary in Zr/Hf co-doped BT-$x$BCT at $x = 0.3$, guiding the discovery of a new ferroelectric material with an experimentally measured dielectric constant of 9535. These results establish FerroAI as a powerful tool for phase diagram construction, guiding the design of high-performance ferroelectric materials. Phase diagram ; Ferroelectricity ; Phase boundary ; Phase (matter) ; Dielectric ; Boundary (topology) Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Ferroelectric and Piezoelectric Materials
Authors: Amin Ahmadisharaf | Adri C. T. van Duin | Bin Liu | Dylan Evans | Sadra Sabouri | Jeffrey Comer
Source: The Journal of Physical Chemistry C (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

ReaxFF Parameter Set for Boron Clusters and Icosahedral Boron Crystals: Comparison with Density Functional Theory and Machine-Learning Potentials Icosahedral boron materials, which include regular icosahedra of 12 boron atoms have gained increasing attention due to their potential applications as superhard materials, semiconductors, and energy storage media. However, the synthesis of high quality crystals of these materials has been a major barrier to the development of these applications. To enable computational prediction of synthesis conditions yielding high-quality icosahedral boron crystals, herein we tested and refined a set of ReaxFF parameters for the nucleation and growth of such crystals. We focused on matching the relative energies of small boron clusters obtained by density functional theory since such small clusters and similar motifs are likely present in crystal nuclei and at the interface of growing crystals. Using a training set of B<sub>80</sub> clusters, including a low-energy core-shell structure containing a B<sub>12</sub> icosahedron core and a high-energy single-shell structure produced in preliminary ReaxFF simulations, the ReaxFF parameter set was refined to better reproduce energies calculated by density functional theory (DFT). Among existing ReaxFF parameter sets and the machine-learning interatomic potentials MACE-MP-0, MACE-MP-0b3, MACE-MPA-0, PFP v7.0.0, and SevenNet-MF-ompa, only our new parameter set and PFP v7.0.0 correctly ranked these B<sub>80</sub> clusters. This refinement led to improved agreement with DFT for a test set of 58 clusters consisting of 8-103 boron atoms. Furthermore, our refined parameter set yielded greater local icosahedral structure than the previously existing ReaxFF parameter set for larger scale simulations of crystallization from supercooled liquid boron. Additionally, simulations of solid boron in contact with molten nickel using our refined ReaxFF parameters yielded a boron solubility value that agrees moderately well with experimental expectations, while the previous boron parameters gave a value that was much too low. ReaxFF ; Icosahedral symmetry ; Density functional theory ; Boron ; Molecular dynamics ; Nucleation Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Sarah Chattha | Simant R. Upreti | Philip K. Chan
Source: Micromachines (article, 2025-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting Liquid Crystal Behavior with Artificial Neural Networks Liquid crystals (LCs) with fluid-like flow and solid-like molecular orientation find important applications in optical display and sensor technologies. Predicting the mean steady-state polar angle and refractive index is crucial for optimizing LC performance. While conventional predictive models such as those based on continuum theories require complex and computationally intensive numerical simulations, this study employs artificial neural networks (ANNs). In particular, they are developed to predict the mean steady state polar angle and refractive index from surface viscosity and anchoring energy. Using the train, validation, test method, ANN_A4 (R<sup>2</sup> = 0.9995) and ANN_B2 (R<sup>2</sup> = 0.9969) are found to have the highest predictive accuracy. On the other hand, using the K-Fold cross-validation, the results significantly differ, with the best performance shown in ANN_A5* (R<sup>2</sup> = 0.40767) and ANN_B4* (R<sup>2</sup> = 0.93799). Coupled with the low latency of ANNs, these results indicate that ANNs have significant potential in LC modeling, especially for use in the computationally intensive optimization of LC-based technologies. Artificial neural network ; Refractive index ; Liquid crystal ; Polar ; Anchoring ; Orientation (vector space) ; Viscosity Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Material Dynamics and Properties
Authors: Hongfei Cheng | Hui Wang | Zhiyong Wang | Zhijian Wang
Source: Advances in Engineering Innovation (article, 2025-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of neural network algorithms in the design of electromagnetic parameters for dielectric loss microwave absorbing materials Dielectric loss microwave absorbing materials have been widely used to mitigate electromagnetic interference and achieve radar stealth. Their design and optimization involve multiple electromagnetic parameters, such as dielectric constant, magnetic permeability, and loss factors. Traditional optimization methods typically require extensive computation and experimentation, making them inefficient and prone to local optima. In recent years, neural network algorithms, as effective nonlinear modeling and optimization tools, have found growing applications in optimizing the performance of absorbing materials. This paper explores the application of neural networks in optimizing electromagnetic parameters of dielectric loss microwave absorbing materials. Using neural networks, we predict the reflectivity values under different electromagnetic parameters and examine the patterns of variation with frequency and thickness. The results show that optimal reflectivity for various frequencies corresponds to different sets of electromagnetic parameters and matching thicknesses. As the frequency increases, the optimal dielectric constant values (both real and imaginary parts) concentrate in a lower range, especially when the material thickness is small. Finally, the consistency between predicted and experimentally measured reflectivity values confirms the reliability of the neural network-based predictions. Microwave ; Artificial neural network ; Dielectric ; Dielectric loss ; Materials science ; Electronic engineering ; Computer science ; Algorithm ; Acoustics ; Engineering ; Optoelectronics ; Physics ; Telecommunications ; Artificial intelligence Electromagnetic wave absorption materials ; Microwave and Dielectric Measurement Techniques ; Advanced Antenna and Metasurface Technologies
Authors: Lars Doorenbos | Lucas Patty | Raphael Sznitman | Pablo Márquez-Neila
Source:  (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Inferring geometry and material properties from Mueller matrices with machine learning Mueller matrices (MMs) encode information on geometry and material properties, but recovering both simultaneously is an ill-posed problem. We explore whether MMs contain sufficient information to infer surface geometry and material properties with machine learning. We use a dataset of spheres of various isotropic materials, with MMs captured over the full angular domain at five visible wavelengths (450-650 nm). We train machine learning models to predict material properties and surface normals using only these MMs as input. We demonstrate that, even when the material type is unknown, surface normals can be predicted and object geometry reconstructed. Moreover, MMs allow models to identify material types correctly. Further analyses show that diagonal elements are key for material characterization, and off-diagonal elements are decisive for normal estimation. Isotropy ; Surface (topology) ; Material properties ; Domain (mathematical analysis) ; Spectral properties ; Diagonal ; ENCODE X-ray Diffraction in Crystallography ; Polymer Nanocomposites and Properties ; Surface Roughness and Optical Measurements
Authors: Enda Xiao | Terumasa Tadano
Source: ArXiv.org (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline anisotropy energy ($E_{\mathrm{aniso}}$). Structure optimization and evaluation of formation energy and distance to hull convex were performed using the eSEN-30M-OAM interatomic potential, while local magnetic moments, phonon stability, magnetic stability, and $E_{\mathrm{aniso}}$ were predicted by eSEM models trained on our DxMag Heusler database. A frozen transfer learning strategy was employed to improve accuracy. Candidate compounds identified by the ML-HTP workflow were validated with density functional theory, confirming high predictive precision. We also benchmark the performance of different uMLIPs, discuss the fidelity of local magnetic moment prediction, and demonstrate generalization to unseen elements via transfer learning from a universal interatomic potential. Workflow ; Benchmark (surveying) ; Magnetocrystalline anisotropy ; Magnetic moment ; Density functional theory ; Generalization ; Alloy ; Austenite ; Characterization (materials science) Machine Learning in Materials Science
Authors: Jie Zhang (64655) | Zhi Yang (124104) | Xiaoxiao Zhang (803510) | Haochuan Yang (16892200) | Xiaofeng Nie (6059708) | Qiong Wu (84327) | Hongguo Zhang (5631635) | Ming Yue (115776)
Source: Figshare (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Machine Learning-EnabledPrediction of Synthesis–PropertyRelationships in Cobalt Nanowires as Rare-Earth-Free Permanent Magnets The development of rare-earth-free permanent magnetic materials has become increasingly important due to concerns about supply chain vulnerabilities and sustainability associated with rare-earth elements. Cobalt nanowires represent promising candidates for high-performance permanent magnets, yet understanding the complex relationships between the synthesis parameters and magnetic properties remains challenging. This study establishes quantitative synthesis–property relationships for cobalt nanowires through machine learning analysis and experimental validation. A comprehensive database containing complete synthesis–property records was constructed from the peer-reviewed literature focusing on polyol reduction methods. Among the 18 evaluated machine learning algorithms, gradient-boosting decision trees demonstrated superior predictive performance for coercivity prediction. Shapley Additive exPlanations analysis identified reducing agents as the most critical parameter influencing coercivity, followed by nucleating agents and process parameters. To validate these machine learning insights, systematic experimental investigations were conducted using four different reducing agents, while maintaining identical synthesis conditions. First-order reversal curve analysis further validated the critical role of reducing agents in determining the magnetic uniformity and switching behavior. This work demonstrates the effectiveness of machine learning in elucidating synthesis–property relationships and provides experimental validation of model predictions, establishing a foundation for the rational design of high-performance cobalt nanowires. Cobalt ; Coercivity ; Nanowire ; Magnet ; Polyol ; Machine design Machine Learning in Materials Science ; Magnetic Properties of Alloys ; Magnetic properties of thin films