jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel S. King | Daniel Grzenda | Ruijie Zhu | Nathaniel Hudson | Ian Foster | Bingqing Cheng | Laura Gagliardi
Source: ChemRxiv (preprint, 2025-04-28)
Field: Chemistry — Physical and Theoretical Chemistry

Cartesian Equivariant Representations for Learning and Understanding Molecular Orbitals Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies and characterizations, given the molecular coefficients from Hartree-Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network CEONet, shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine learned potentials. We find that CEONet performs well at predicting difficult quantitative labels such as the orbital energy. Furthermore, we find that the CEONet representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONet to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. Equivariant map ; Molecular orbital ; Atomic orbital ; Physics ; Computer science ; Theoretical physics ; Pure mathematics ; Mathematics ; Quantum mechanics ; Molecule History and advancements in chemistry ; Various Chemistry Research Topics ; Chemistry and Chemical Engineering
Authors: Brandon K. Phan | Kuan-Hsuan Shen | Rishi Gurnani | Tran Doan Huan | Ryan P. Lively | Rampi Ramprasad
Source: npj Computational Materials (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Author Correction: Gas permeability, diffusivity, and solubility in polymers: simulation-experiment data fusion and multi-task machine learning Thermal diffusivity ; Solubility ; Fusion ; Permeability (electromagnetism) ; Task (project management) ; Polymer ; Materials science ; Artificial intelligence ; Thermodynamics ; Computer science ; Chemistry ; Composite material ; Engineering ; Physics ; Physical chemistry ; Philosophy ; Systems engineering Machine Learning in Materials Science
Authors: Yi Zhao | Lei Zhang | Libo Zhang | Mengyuan Gong | Haoran Yuan
Source: Computers & Chemical Engineering (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Multi-scale modeling and optimization of CO₂ hydrogenation to light olefins via deep learning and optimization algorithms Scale (ratio) ; Optimization algorithm ; Computer science ; Deep learning ; Algorithm ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Physics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysts for Methane Reforming
Authors: Dishant Beniwal | Pratik K. Ray
Source: Computational Materials Science (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

MAPAL: A python library for mapping features and properties of alloys over compositional spaces Python (programming language) ; Workflow ; Alloy ; Ternary operation ; High entropy alloys ; Macro ; Material properties Machine Learning in Materials Science ; High Entropy Alloys Studies ; Catalysis and Oxidation Reactions
Authors: Han Xu | Taoyong Cui | Chenyu Tang | Jianfeng Ma | Dongzhan Zhou | Yuqiang Li | Xiang Gao | Xingao Gong | Wanli Ouyang | Shufei Zhang | Mao Su
Source: Nature Communications (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Evidential deep learning for interatomic potentials Machine learning interatomic potentials have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. To ensure the reliability of the simulation, the training dataset is iteratively expanded through active learning, where uncertainty serves as a critical indicator for identifying and collecting out-of-distribution data. However, existing uncertainty quantification methods tend to involve either expensive computations or compromise prediction accuracy. Here we show an evidential deep learning framework for interatomic potentials with a physics-inspired design. Our method provides uncertainty quantification without significant computational overhead or decreased prediction accuracy, consistently outperforming other methods across a variety of datasets. Furthermore, we demonstrate applications in exploring diverse atomic configurations, using examples including water and universal potentials. These results highlight the potential of our method as a robust and efficient alternative for uncertainty quantification in molecular simulations. Uncertainty quantification ; Reliability (semiconductor) ; Deep learning ; Computation ; Variety (cybernetics) ; Molecular dynamics ; Interatomic potential Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Florian M. Schenk | Maksym Yarema
Source: Chemistry of Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Solution-Phase Design of Emerging Nanomaterials Emerging materials such as liquid metals, intermetallics, phase-change materials, and glassy chalcogenides provide unconventional properties to drive innovations in catalysis, biomedicine, photonics, and data processing. However, the same unique characteristics that enable these functionalities, such as dynamic surfaces, complex bonding, or amorphous structures, simultaneously present significant challenges in controlling their synthesis and tailoring them for specific applications. Solution-phase assembly of these materials from nanoscale building blocks offers a powerful avenue to overcome these barriers. This approach provides unprecedented nanoscale control and high flexibility to adjust composition, geometry, and surface, while also enabling advanced patterning technologies and providing compatibility with various substrates. This perspective highlights the transformative potential of solution-phase synthesis for the development of next-generation functional materials. More broadly, we showcase the avenue toward precise design of materials as well as accelerated materials discovery and optimization, particularly when integrated with modern machine learning-based algorithms. This has far-reaching implications, complementing and even replacing conventional fabrication methods as well as high-throughput materials screening and optimization. Flexibility (engineering) ; Transformative learning ; Nanomaterials ; Emerging technologies ; Compatibility (geochemistry) ; Nanoscopic scale Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Nanoporous metals and alloys
Authors: Bengü Temizel | Elif Neyran Soylu
Source: Environment Development and Sustainability (article, 2025-05-07)
Field: Materials Science — Biomaterials

Modeling of diatom indices (Bdı, Tdı and Gdı) based on the physico-chemical structure of the river ecosystem with machine learning and artificial intelligence methods; a comparative example Abstract Diatom indices are used to assess the quality of aquatic plants in sustainable river ecosystems. The traditional assessment of diatom indices involves complicated and lengthy process steps. Today, artificial intelligence-based modelling plays a key role in overcoming this complexity. The aim of this work is to model selected diatom indices Biological Diatom Index (BDI), Trophic Diatom Index (TDI) and General Diatom Index (GDI) based on the physicochemical structure of river ecosystems using artificial intelligence and machine learning methods. The application part of the study used surface water variables from rivers monitored by 5 different stations for 24 months as a data set. Traditional analyses were compared with artificial intelligence and machine learning methods using the MATLAB programme. Different algorithms were considered, including Neural Network/Multilayer Perceptron (MLP), Support Vector Machine (SVM), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree and Levenberg-Marquardt (LM) approach. To evaluate the quality of the models, the coefficient of determination (R 2 ), root mean square error squared (RMSE) and mean absolute percentage error (MAPE) were compared. The R 2 values of the Levenberg-Marquardt model, which gave the best prediction results for BDI, TDI and GDI, were found to be Validation; 0.7691, Training; 0.9620 Testing; 0.8613, Validation 0.9273, Training; 0.9303, Testing; 0.9199, Validation; 0.9273, Training; 0.9303, Testing; 0.9199, respectively. Levenberg Marquardt efficiently predicted Diatom index results accurately with high precision. Our results show that artificial intelligence and machine learning methods are highly efficient tools for the prediction of diatom indices. A time-efficient and labour-saving application in sustainable ecosystem management was successfully demonstrated. Diatom ; Artificial intelligence ; Sustainable development ; Ecosystem ; River ecosystem ; Ecology ; Environmental science ; Computer science ; Biology Diatoms and Algae Research ; Water Quality Monitoring Technologies ; Water Quality Monitoring and Analysis
Authors: Najmeh Zahabi | Ioannis Petsagkourakis | Nicolas Rolland | Ali Beikmohammadi | Xianjie Liu | Mats Fahlman | Eleni Pavlopoulou | Igor Zozoulenko
Source: Physical Review Materials (article, 2025-10-17)
Field: Materials Science — Polymers and Plastics

Deciphering conductivity in PEDOT guided by machine learning: From solvent baths to charge paths PEDOT:Tos is a promising conducting polymer for electronic and bioelectronic applications, yet its charge transport is affected by various factors and remains challenging to optimize. This study investigates the impact of solvent posttreatment on PEDOT:Tos thin films, exploring its influence on morphology and electrical conductivity. A combined experimental-theoretical approach is employed, integrating molecular dynamics, density functional theory, and transport calculations on one hand and conductivity, GIWAXS and XPS measurements on the other hand. Moreover, we developed a machine learning (ML) framework based on convolutional neural networks with the Coulomb matrix as the predictor, and transfer integrals for multiscale transport calculations as targets. Our results reveal that solvent-induced morphological changes strongly affect charge transport, with the ML model effectively reproducing observed conductivity. The developed ML model dramatically boosts the speed of mobility calculations, enabling the analysis of large-scale polymer films that were previously beyond computational reach. Charge (physics) ; Coulomb ; Density functional theory ; Solvent ; PEDOT:PSS ; Polymer ; X-ray photoelectron spectroscopy ; Conductivity Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical Analysis and Applications
Authors: Ball, Akash | Oh, Changhwan | Dovranova, Gozel | Kulik, Heather
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-07)
Field: Chemistry — Inorganic Chemistry

Combining Chemical, Geometric, and Novel Topological Features to Develop Generalizable Machine Learning Models for Predicting Mechanically Stable MOFs Stability (learning theory) ; Topological data analysis ; Topology (electrical circuits) ; Artificial neural network ; Support vector machine ; Sequence (biology) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications
Authors: Changhong Zhang | Jie Liang | Yi Li | Rongmei Chen | Yuanhao Miao | Zhenyu Wang | Lei Zhang | Haoyuan Li | Hanshen Xin
Source: Diamond and Related Materials (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Application and acceleration of machine learning potential construction using farthest point sampling: A case study of carbon nanotubes Carbon nanotube ; Acceleration ; Sampling (signal processing) ; Carbon fibers ; Point (geometry) ; Computer science ; Nanotechnology ; Materials science ; Mathematics ; Physics ; Algorithm ; Computer vision ; Geometry ; Classical mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Thermal properties of materials
Authors: Andrew S. Paluch | Jeffrey G. Ethier | Vikas Varshney
Source: ChemRxiv (preprint, 2025-05-26)
Field: Materials Science — Materials Chemistry

Leveraging Molecular Mechanics with the uESE Continuum Solvation Model for Efficient Solvation Free Energy Prediction: Impact of Conformation and Extensive Validation The solvation free energy is a fundamental property of a solute directly related to solubility, which in turn is critical for processes ranging from pharmaceutical to materials manufacturing. We seek to develop efficient strategies to predict the solvation free energy knowing only molecular structure using electronic structure calculations with the uESE continuum solvation model for the potential use as accurate descriptors in physics-informed machine learning models to predict solubility. Benchmarking on the Minnesota Solvation Database, single conformations generated using the molecular mechanics force field MMFF94 yielded predictive accuracy comparable to reference gas phase optimized geometries obtained with electronic structure calculations. Surprisingly, exploring multiple conformations did not consistently improve predictions, suggesting uESE performs well with a single representative input. Evaluation on the independent dGsolvDB1 dataset demonstrated reasonable predictive ability with single molecular mechanics-generated conformations and some generalizability to novel chemical space. Our findings indicate that combining fast molecular mechanics-based structure generation with uESE offers a promising approach for efficient and reasonably accurate solvation free energy predictions, supporting its utility in high-throughput screening and machine learning for solubility prediction. Solvation ; Implicit solvation ; Molecular mechanics ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Molecular dynamics ; Molecule ; Quantum mechanics Machine Learning in Materials Science
Authors: Erik Jansson | Magnus Röding
Source: Physical review. E (article, 2025-04-25)
Field: Chemistry — Electrochemistry

Interpretability versus performance of analytical and neural-network-based permeability prediction models: Exploring separability, monotonicity, and dimensional consistency Effective mass transport properties of porous materials, such as permeability, are heavily influenced by their three-dimensional microstructure. There are numerous models developed for the prediction of permeability from microstructural characteristics, ranging from straightforward analytical relationships to high-performing machine learning models based on neural networks. There is an inherent tradeoff between predictive performance and interpretability; analytical models do not provide the best predictive performance but are relatively simple to understand. Neural networks, on the other hand, provide better predictive performance but are harder to interpret. In this paper, we investigate a multitude of models on the performance-versus-interpretability spectrum. Specifically, we use a dataset of 90000 microstructures developed elsewhere and consider the prediction of permeability using the microstructural descriptors porosity, specific surface area, and geodesic tortuosity. At the respective ends of the spectrum, we study analytical, power-law-type models and fully connected neural networks. In between, we study neural networks that are either separable, monotonic, or both separable and monotonic. Establishing monotonic relationships is particularly interesting considering the potential for solving the inverse microstructure design problem using gradient-based methods. In addition, we study versions of these models that are consistent and inconsistent in terms of physical dimension. Interpretability ; Monotonic function ; Consistency (knowledge bases) ; Artificial neural network ; Applied mathematics ; Mathematics ; Permeability (electromagnetism) ; Artificial intelligence ; Computer science ; Econometrics ; Mathematical optimization ; Chemistry ; Mathematical analysis Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Non-Destructive Testing Techniques
Authors: Patrick T. Gemperline | R. Comès
Source: ArXiv.org (preprint, 2025-01-15)
Field: Materials Science — Surfaces, Coatings and Films

Improvement of Data Analytics Techniques in Reflection High Energy Electron Diffraction to Enable Machine Learning Perovskite oxides such as LaFeO$_3$ are a well-studied family of materials that possess a wide range of useful and novel properties. Successfully synthesizing perovskite oxide samples usually requires a significant number of growth attempts and a detailed film characterization on each sample to find the optimal growth window of a material. The most common real-time \textit{in situ} diagnostic technique available during molecular beam epitaxy (MBE) synthesis is reflection high-energy electron diffraction (RHEED). Conventional use of RHEED allows a highly experienced operator to determine growth rate by monitoring intensity osciallations and make some qualitative observations during growth, such as recognizing the sample has become amorphous or recognizing that large islands have formed on the surface. However, due to a lack of theoretical understanding of the diffraction patterns, finer, more precise levels of observations are challenging. To address these limitations, we implement new data analytics techniques in the growth of three LaFeO$_3$ samples on Nb-doped SrTiO$_3$ by MBE. These techniques improve our ability to perform unsupervised machine learning using principal component analysis (PCA) and k-means clustering by using drift correction to overcome sample or stage motion during growth and intensity transformations that highlight more subtle features in the images such as Kikuchi bands. With this approach, we enable the first demonstration of PCA and k-means across multiple samples, allowing for quantitative comparison of RHEED videos for two LaFeO$_3$ film samples. These capabilities set the stage for real-time processing of RHEED data during growth to enable machine learning-accelerated film synthesis. Reflection (computer programming) ; Analytics ; Computer science ; Electron diffraction ; Data analysis ; Energy (signal processing) ; Diffraction ; Data science ; Electron ; Physics ; Data mining ; Optics ; Nuclear physics Electron and X-Ray Spectroscopy Techniques
Authors: Christoforos Rekatsinas | Panagiotis Krokidas | Vasileios Vavourakis | Clara L. Essmann | George Giannakopoulos
Source: Advanced Intelligent Discovery (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics Predicting material properties, such as Young's modulus, in complex multilayered‐multimaterial structures is a challenging task that requires the integration of data‐driven methods with physical principles. In this work, we present a Physics‐guided Neural Network framework for estimating multiple Young's moduli in multimaterial cylindrical structures, with a specific application to Caenor habditis elegans worms. Our approach leverages contact mechanics to model the force‐indentation behavior of multilayered ring‐cylinder systems, providing a physics‐based foundation for the neural network. To ensure physically consistent predictions, we introduce a custom activation function that enforces bounds on the estimated Young's modulus using hyperbolic tangent transformations and scaling. The neural network architecture is designed to handle the complexity of multilayered systems, and the training process incorporates progressive tightening of bounds to balance exploration and exploitation. We validate our framework using both numerical data from high‐fidelity finite element models and experimental force‐indentation measurements from C. elegans worms , demonstrating the framework's ability to accurately predict Young's modulus for each layer of the cylindrical structure. This work highlights the potential of combining physics‐guided machine learning with domain‐specific knowledge to solve complex inverse problems in material science and biomechanics, offering a versatile and efficient alternative to state‐of‐the‐art global optimization techniques like the Bayesian optimization algorithm. Artificial neural network ; Pipeline (software) ; Finite element method ; Hyperelastic material ; Process (computing) ; Contact mechanics ; Inverse problem ; Function (biology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Z. M. Qiu | Luozhijie Jin | Zijian Du | Hongyu Chen | Guangzhao Mao | Yan Cen | Siqi Sun | Yongfeng Mei | Hao Zhang
Source: npj Computational Materials (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Massive discovery of crystal structures across dimensionalities by leveraging vector quantization Discovering new functional crystalline materials through computational methods remains a challenge in materials science. We introduce VQCrystal, a deep learning framework leveraging discrete latent representations to overcome key limitations to crystal generation and inverse design. VQCrystal employs a hierarchical VQ-VAE architecture to encode global and atom-level crystal features, coupled with an inter-atomic potential model and a genetic algorithm to realize property-targeted inverse design. Benchmark evaluations on diverse datasets demonstrate VQCrystal’s capabilities in representation learning and crystal discovery. We further apply VQCrystal for both 3D and 2D material design. For 3D materials, the density-functional theory validation confirmed that 62.22% of bandgaps and 99% of formation energies of the 56 filtered materials matched the target range. 437 generated materials were validated as existing entries in the full MP-20 database outside the training set. For 2D materials, 73.91% of 23 filtered structures exhibited high stability with formation energies below -1 eV/atom. Vector quantization ; Computer science ; Quantization (signal processing) ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mingzi Sun | Bolong Huang
Source: Advanced Energy Materials (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Exploring Challenging C<sub>2+</sub> Products During CO<sub>2</sub> Reduction via Machine Learning Acceleration (Adv. Energy Mater. 16/2025) CO2 Reduction In article number 2500177, Mingzi Sun and Bolong Huang have applied the first-principles machine learning method to unravel the reaction mechanisms of challenging C2+ products during the CO2 reduction reaction on graphdiyne-supported atomic catalysts, which supply insights into improving the selectivity of designed catalysts. Materials science ; Acceleration ; Energy (signal processing) ; Reduction (mathematics) ; Nanotechnology ; Engineering physics ; Chemical engineering ; Physics ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science