jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dylan
D. Fortney (21986185) | Brett M. Savoie (1293810)
Source: Figshare (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Data-Driven Coarse-Graining with Simple FunctionalForms Deep neural networks have become popular model architectures for fitting coarse-grained molecular dynamics potentials (CGMD) owing to their ability to describe complex features and ease of training against large data sets. However, such architectures are much more complicated than traditional functional forms. This raises the question of whether a similar data-driven approach that used a simpler functional form would have any advantages. Here, we develop a genetic algorithm that optimizes Lennard-Jones potentials for coarse-grained models of various crystal and liquid crystal forming materials based on both structural and thermodynamic information. A detailed description of the genetic algorithm, its loss function, and hyperparameters is presented. The models developed by the algorithm reproduce a much broader range of physical properties than achieved by simpler parametrization schemes used in more general methods. The models also show surprising transferability in reproducing properties that were not directly trained against. Simulations of larger systems with these models demonstrate stabilization of the reference crystal structure on long time scales, preserve melting-point trends, and reproduce liquid crystalline phase transitions, despite this information being absent from training. Despite the rush to adopt neural network potentials, these case-studies show that simpler functional forms retain untapped potential for CGMD when coupled with data-driven training algorithms. Artificial neural network ; Simple (philosophy) ; Parametrization (atmospheric modeling) ; Transferability ; Genetic algorithm ; Range (aeronautics) ; Phase (matter) ; Crystal (programming language) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Yang Wang
Source: ChemRxiv (preprint, 2025-08-21)
Field: Chemistry — Organic Chemistry

Autodesmotic Reactions: Toward a General Approach for Strain Energy Evaluation in Polycyclic Aromatic Nanocarbons Accurate quantification of strain energy (SE) is essential for rational design and synthesis of aromatic nanocarbons, as intrinsic strain strongly affects their structure, stability, reactivity, and properties. Existing approaches often suffer from arbitrariness in reference selection, limited applicability, or high computational cost. In particular, the balance of π-energy between the target molecule and its strain-free references has long been overlooked, which is critical for π-conjugated systems. Here, we introduce “autodesmotic reactions” as a general and efficient strategy for reliable SE evaluation in polycyclic aromatic nanocarbons. An autodesmotic reaction transforms a strained molecule into a uniquely defined single-molecule reference. This reference resides within a virtual strain-free chemical space derived from physically motivated models trained on planar benzenoid polycyclic aromatic hydrocarbons (PAHs). Crucially, preservation of molecular topology ensures both unique reference choice and proper π-energy balance. Benchmarking against asymptotic and StrainViz models across diverse carbon nanobelts (CNBs) validates the accuracy and robustness of the approach. It is broadly applicable to both macrocyclic and nonmacrocyclic systems while requiring only a single quantum chemical calculation per molecule. Applications to more challenging systems, including helicene-containing and saddle-shaped CNBs, helicenes, bowl-shaped PAHs, carbon nanotubes, and fullerenes, demonstrate high computational efficiency and provide valuable insights into strain–structure–property relationships. Overall, autodesmotic reactions establish a general, conceptually transparent, and efficient framework for SE analysis in π-conjugated nanocarbons. The accuracy and generalizability of the current method can be further enhanced through rapidly advancing machine-learning techniques. Strain (injury) ; Strain energy ; Chemistry ; Environmental chemistry ; Environmental science ; Nanotechnology ; Materials science ; Physics ; Thermodynamics ; Biology Synthesis and Properties of Aromatic Compounds ; Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites
Authors: Ayokanmi Joseph Aremu | Phiphob Naweephattana | Ismail Dwi Putra | Borwornlak Toopradab | Phornphimon Maitarad | Thanyada Rungrotmongkol
Source: Journal of Computational Chemistry (article, 2025-02-03)
Field: Chemistry — Organic Chemistry

Rational Design for Antioxidant Diphenylamine Derivatives Using Quantitative Structure–Activity Relationships and Quantum Mechanics Calculations Diphenylamine (DPA) derivatives, used as antioxidants in rubber-based products, inhibit autoxidation by donating hydrogen atoms to peroxyl radicals. Octanol-water partition coefficient (LogK<sub>ow</sub>), an antioxidant index, helps predict their distribution in hydrophobic polymer matrices. Therefore, this study aimed to investigate the relationship between the structure of DPA derivatives and their antioxidant activities, using machine learning with quantitative structure-activity relationships (QSAR) and quantum mechanics (QM). The structure of DPA derivatives was optimized using Density Functional Theory and analyzed for molecular properties. The QSAR models were trained using important descriptors identified through permutation importance. Among the models developed, the Gradient Boosting Regressor (GBR) showed the best performance, with R<sup>2</sup> of 0.983 and root mean square error (RMSE) of 0.642 for the test set. SHAP analysis revealed that molecular weight and electronic properties significantly influenced LogK<sub>ow</sub> predictions. For instance, a higher molecular weight was associated with increased LogK<sub>ow</sub>, and a higher positive charge of C2 correlated with higher LogK<sub>ow</sub> predictions. Consequently, the two potent compounds (D1 and D2) were designed based on QSAR model guidance. The GBR model predicted LogK<sub>ow</sub> values of 9.789 and 7.102 for D1 and D2, respectively, which are higher than the training compounds in the model. To gain molecular insight, the quantum chemical calculations with M062X/6-311++G(d,p)//M062X/6-31G(d,p) were performed to investigate the bond dissociation enthalpy (BDE). The results showed that D1 (79.50 kcal/mol) and D2 (72.43 kcal/mol) exhibited lower BDEs than the reference compounds, suggesting that the designed compounds have the potential for enhanced antioxidant activity. In addition, the antioxidant reaction mechanism was studied by using M062X/6-311++G(d,p)//M062X/6-31G(d,p) which found that the hydrogen atom transfer is the key step, where D1 and D2 showed activation energy barriers of 10.38 and 6.29 kcal/mol, respectively, compared to reference compounds of R3 (10.39 kcal/mol), R1 (10.40 kcal/mol), and R2 (18.26 kcal/mol). Therefore, our findings demonstrate that integrating QSAR with quantum chemical calculations can effectively guide the design of DPA derivatives with improved antioxidant properties. Quantitative structure–activity relationship ; Chemistry ; Partition coefficient ; Computational chemistry ; Autoxidation ; Hydrogen bond ; Molecule ; Physical chemistry ; Organic chemistry ; Stereochemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Samuel Kerschbaumer | Martin Ondráček | Sebastien E. Hadjadj | Oleksandr Stetsovych | Andrés Pinar Solé | Adriana E. Candia | Paula Angulo‐Portugal | Andrea Aguirre‐Baños | Martina Corso | David Serrate | Jorge Lobo‐Checa | Pavel Jelı́nek | Maxim Ilyn | Pablo M. Piaggi | Celia Rogero
Source: Advanced Science (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Coverage–Dependent Structural Evolution of CoBr <sub>2</sub> at the Au(111) Interface Unraveling the growth mechanism of van der Waals materials is crucial for their device implementation, as this improves the overall film quality, allowing precise control of their electronic and magnetic properties in nanoscale applications. The initial structure formed on the substrate during growth is often assumed to be bulk-like, thereby neglecting the role of the surface in the assembly. Here, the coverage-dependent growth of CoBr<sub>2</sub> on Au(111) from a stoichiometric molecular powder is studied using a combination of experimental techniques, machine-learning-driven molecular dynamics simulations and density functional theory calculations. It is found that CoBr<sub>2</sub> molecules initially form a molecular precursor phase characterized by three-molecule clusters arranged in a surface-stabilized structure with long-range order and a periodic coincidence with Au(111). As the surface coverage is increased, this phase subsequently undergoes a transition to form the equilibrium van der Waals crystal layered structure observed for the bulk material. These findings challenge conventional views of direct van der Waals layer formation and provide new insight into the role of the substrate during the growth process. van der Waals force ; Molecular dynamics ; Substrate (aquarium) ; Phase (matter) ; Nanoscopic scale ; Stoichiometry ; Density functional theory ; Crystal (programming language) 2D Materials and Applications ; Surface Chemistry and Catalysis ; Graphene research and applications
Authors: Arefeh Naghizadeh | Ahmadreza Jafari-Sirizi | Behnam Amiri-Ramsheh | Saeid Atashrouz | Dragutin Nedeljković | Ahmad Mohaddespour | Abdolhossein Hemmati‐Sarapardeh
Source: Scientific Reports (article, 2025-08-14)
Field: Chemistry — Inorganic Chemistry

Advanced intelligent techniques for modeling oxygen storage in zeolite-based porous materials Oxygen and nitrogen are essential gases with extensive applications in industrial and medical fields, making their separation from air a critical yet complex challenge. Zeolites, renowned for their highly porous structures, have garnered significant attention as promising materials for enhancing gas storage and separation processes. In this paper, advanced machine learning (ML) methods, including Generalized Regression Neural Network (GRNN), Cascade Forward Neural Network, and Multilayer Perceptron were utilized to forecast the O<sub>2</sub> uptake capacity in zeolites. A comprehensive database of 750 experimental O<sub>2</sub> uptake values was constructed, incorporating pressure, pore volume, temperature, and surface area as input features to develop robust predictive models. The findings demonstrated the superior performance of the GRNN model, achieving an exceptional root mean square error of 0.03 and a coefficient of determination (R<sup>2</sup>) of 0.9991, outperforming the other techniques. Additionally, further analyses confirmed the reliability of the presented models in accurately capturing physical trends of O<sub>2</sub> uptake under varying pressure conditions at different constant temperatures. Sensitivity analysis further revealed that pressure positively influences O<sub>2</sub> storage, while temperature exerts the most significant effect, with relevancy factors of 0.341 and - 0.848, respectively. These results underscore the effectiveness of ML techniques in precisely forecasting and enhancing gas storage processes in zeolites, offering meaningful insights to drive advancements in separation technologies. Zeolite ; Porosity ; Computer science ; Chemical engineering ; Materials science ; Chemistry ; Engineering ; Composite material ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Advanced Data Processing Techniques
Authors: Arsenios Gkourras | Dimitrios Iliopoulos | Leonidas N. Gergidis | Denis G. Samsonenko⧫ | Vladimir P. Fedin
Source: Advanced Theory and Simulations (article, 2025-10-25)
Field: Chemistry — Inorganic Chemistry

Adsorption of Methane, Ethane and Their Equimolar Mixture in NIIC‐20‐Bu Metal‐Organic Framework from Grand Canonical Monte Carlo Simulations and Artificial Neural Networks Abstract The adsorption of methane, ethane, and their equimolar mixture in the mesoporous NIIC‐20‐Bu metal–organic framework (MOF) is investigated utilizing molecular simulations and artificial neural networks. To the best of knowledge, this is the first computational study of small alkanes sorbed in this particular MOF. Grand Canonical Monte Carlo simulations provided the adsorption isotherms of the aforementioned alkanes in NIIC‐20‐Bu at different temperatures. The simulation findings are compared with existing experimental sorption measurements showing reasonable quantitative and qualitative agreement. Predictive models based on artificial neural networks are developed incorporating simulation data and available experimental measurements in the training phase to predict the sorption isotherms of methane, ethane, and their equimolar mixture in NIIC‐20‐Bu mesoporous material with the minimum computational cost. 3D density profiles of sorbed methane and ethane are computed based on their positions in the simulation box of NIIC‐20‐Bu, as obtained from GCMC simulations. Moreover, the analysis of the aforementioned profiles highlighted preferred localization domains, siting motifs and interesting segregation phenomena of the sorbed methane, ethane molecules as pure components or in their equimolar mixture within the mesoporous crystal. The present findings highlight the potential applications of NIIC‐20‐Bu as an efficient adsorbent material. Adsorption ; Mesoporous material ; Monte Carlo method ; Sorption ; Artificial neural network ; Combining rules ; Grand canonical ensemble ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Xianghong He | Yuhan Wang | Rentao Wu | Jidong Gao | Rongpei Zhang
Source: Axioms (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

An Energy Minimization-Based Deep Learning Approach with Enhanced Stability for the Allen-Cahn Equation The Allen-Cahn equation is a fundamental model in materials science for describing phase separation phenomena. This paper introduces an Energy-Stabilized Scaled Deep Neural Network (ES-ScaDNN) framework to solve the Allen-Cahn equation by energy minimization. Unlike traditional numerical methods, our approach directly approximates the solution of steady-state solution the Allen-Cahn equation by minimizing the associated energy functional using a deep neural network. ES-ScaDNN incorporates two key innovations. The first is a scaling layer designed to map the network output to the physical range of the Allen-Cahn phase variable. The second is a variance-based regularization term designed to promote clear phase separation. We demonstrate the accuracy and efficiency of ES-ScaDNN through comprehensive numerical experiments in both one and two dimensions. Our results show that ReLU activation functions are particularly well-suited for one-dimensional cases, while tanh functions are more suitable for two-dimensional problems due to their superior ability to maintain solution smoothness. Furthermore, we investigate how training epochs and the interface parameter ε influence the behavior of the solution. ES-ScaDNN provides a novel, accurate, and efficient deep learning framework for solving the Allen-Cahn equation, paving the way for tackling more complex phase-field problems. Deep learning ; Stability (learning theory) ; Regularization (linguistics) ; Artificial neural network ; Range (aeronautics) ; Energy (signal processing) ; Term (time) ; Scaling Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Aoyuan Peng | Wenqian Wang | Jingtao Xia | Hu Jian
Source:  (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Hybrid Feature Learning for Crystal System and Space Group Classification from Standard XRD Patterns The ability to identify crystal structures correctly is essential for predicting material performance and the creation of novel materials. As the internal order of the materials, XRD (x-ray diffraction) patterns serve as a crucial tool for structure determination. On the other hand, conventional XRD analysis approaches largely depend on professional experience and manual peak matching, which leads to low efficiency and susceptibility to subjective errors. Machine learning approaches have recently shown enormous promise for structural analysis, although studies that currently exist are frequently restricted to simple classification tasks, which still have difficulties with fine-grained tasks like space group recognition and strong performance when there is noise. In this paper, we present a hybrid feature learning method that uses a LightGBM classifier to identify crystal systems and space groups based on large-scale simulated XRD patterns created from the International Crystal Structure Database (ICSD) to combine handcrafted physical features with deep convolutional features in order to identify crystal systems. Extensive research shows that the suggested approach outperforms conventional machine learning models by achieving 97.8 % accuracy in the classification of the crystal system and 87.2 % accuracy in space group categorization. Robustness tests under noise and peak shifts validate its stability. These results suggest that integrating domain knowledge and data-driven features enhances intelligent XRD analysis, offering a promising strategy for high-throughput material discovery. Group (periodic table) ; Space (punctuation) ; Feature (linguistics) ; Crystal (programming language) ; Computer science ; Crystal system ; Feature vector ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Crystallography ; Crystal structure ; Physics ; Chemistry X-ray Diffraction in Crystallography
Authors: Ru Geng | Yixian Gao | Jian Zu | Hong-Kun Zhang
Source: Physica D Nonlinear Phenomena (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

IADNet: A neural network for discovering particle interactions from trajectory data Interpretability ; Network topology ; Complex system ; Graph ; Artificial neural network ; Lattice (music) ; Biological network ; Trajectory ; Complex network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Protein Structure and Dynamics
Authors: Zhibo Wang | Alexander Davis | Rajarshi Dutta | Kanishk Yadav | Ihor Neporozhnii | Oleksandr Voznyy
Source: ChemRxiv (preprint, 2025-07-21)
Field: Chemistry — Spectroscopy

How the Discreteness of the Periodic Table Undermines Machine Learning Predictions The discovery of novel optoelectronic materials relies on accurate bandgap characterization, enabling efficient exploration of uncharted materials space. Machine learning (ML) is a promising approach to accelerate materials discovery, yet accurate bandgap prediction remains a persistent challenge. This study finds that poor ML performance is not solely attributable to insufficient data or model robustness but rather to the intrinsic complexity and discontinuous nature of chemical space. The lack of a continuous trend in the periodic table fundamentally limits ML models, preventing them from leveraging systematic patterns. While smooth trends exist for properties like total and formation energies, bandgaps exhibit inherent fragmentation, leading models to memorize localized correlations rather than learn universal physical laws. Consequently, increasing dataset size fails to improve predictions. Thus, the core challenge of bandgap prediction lies not in model scale or complexity, but in bridging the discretized foundations of the periodic table itself. Table (database) ; Computer science ; Artificial intelligence ; Periodic table ; Machine learning ; Statistical physics ; Cognitive science ; Psychology ; Physics ; Data mining ; Quantum mechanics Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Statistical and Computational Modeling
Authors: Marie Romedenne | Praneeth Bachu | James L. Smialek | Govindarajan Muralidharan | Rishi Pillai
Source:  (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Unsupervised Clustering and Supervised Regression Learning to Select High Temperature Oxidation-Resistant Materials Abstract High temperature oxidation and corrosion degradation mechanisms dictate the lifetime of materials critical to energy production. The combination of modeling and experimental approaches such as machine learning (ML) and data analytics, with sufficient experimental data, can enable a cost-effective acceleration of the development of new materials. In the present work, ML will be applied to two high temperature oxidation data libraries (Oak Ridge National Laboratory and National Air and Space Administration) that comprised of about 5000 mass change sample datasheets for a variety of materials and temperatures in dry air and air + 10 % H2O. A python code was developed to prepare the data for machine learning by collecting and formatting oxidation rate constants, alloy compositions and environment of exposure into a single data frame. Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset distribution on the performance of the developed ML models was evaluated. Potential strategies to improve the predictions and enhance extrapolative capability of the previously trained model were investigated. Computer science ; Cluster analysis ; Unsupervised learning ; Machine learning ; Artificial intelligence ; Regression ; Regression analysis ; Supervised learning ; Pattern recognition (psychology) ; Statistics ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Iron and Steelmaking Processes ; Nuclear Materials and Properties
Authors: Seungho Lee | C. Lee | Y. Ro | Sung Hyuk Park | Jun Jo | Chong Soo Lee | Taekyung Lee
Source: Journal of Materials Research and Technology (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Advancing constitutive analysis of the hot deformation behavior of Ti–3Al–8V–6Cr–4Mo–4Zr via physics-aided machine learning Near- β Ti-3Al-8V-6Cr-4Mo-4Zr alloy necessitates an optimized thermomechanical process to ensure excellent hot workability. The traditional constitutive approach requires a large amount of hot deformation data and has the issue of insubstantially fixed parameters that limit its predictive performance and range of applications. On the other hand, a purely data-driven approach utilizing machine learning (ML) lacks generalizability and physical interpretability, particularly under extrapolated conditions. To address these limitations, this study proposes a physics-aided ML framework that combines the Zener–Hollomon equation with a hyperparameter-tuned neural network to dynamically determine material-dependent parameters with respect to the deformation temperature, strain rate, and plastic strain. The proposed framework successfully reduces the predictive error by 44.2% compared with the conventional Zener–Hollomon constitutive model in the most difficult predictive scenarios, including the lowest temperature and highest strain rate. Moreover, the proposed framework improves not only the predictive accuracy but also the generalizability, both for interpolation and extrapolation, while retaining physical transparency. To validate the predictive applicability, the proposed framework is employed to correct an incomplete processing map that reasonably explains the microstructural evolution under the given processing conditions. Artificial neural network ; Deformation (meteorology) ; Constitutive equation ; Interpolation (computer graphics) ; Generalizability theory ; Range (aeronautics) Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Machine Learning in Materials Science