jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Renlei Jiang | Di Fan | Chuanhou Gao | Denis Dochain
Source: IFAC-PapersOnLine (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Molecular Computation of Convolutional Neural Networks via Chemical Reaction Networks This paper focuses on implementing molecular computation of convolutional neural networks (CNNs) via chemical reaction networks (CRNs). A powerful CRN capable of processing image information, which is called biochemical convolutional neural network (BCNN), is built via programming every module of CNN with CRNs and assembling them by chemical oscillator. Necessary theoretical analyses are presented to ensure convergence. We also evaluate the performance of our BCNN on a standard image recognition task. Our results have the potential to impact the developments of synthetic biology, molecular computation and artificial intelligence. Convolutional neural network ; Computation ; Artificial neural network ; Image (mathematics) ; Pattern recognition (psychology) ; Deep learning ; Image processing Machine Learning in Materials Science ; DNA and Biological Computing ; Quantum-Dot Cellular Automata
Authors: Emmanuella Obasi | Olufunso Abosode
Source: Nile Journal of Engineering and Applied Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning Algorithms for Enhanced Prediction of Product Yields and Purity in Chemical Reactions Accurate prediction of chemical product yield is crucial for optimizing reaction conditions and improving efficiency in both research and industrial settings. This study presents a robust, data-driven pipeline for yield prediction using advanced machine learning techniques, specifically CatBoost and XGBoost regressors. The methodology integrates comprehensive data preprocessing—including outlier removal, feature engineering, normalization, dimensionality reduction with PCA, and feature selection—to enhance model performance and interpretability. Experimental data were split into training and testing sets, with hyperparameter tuning performed via grid search to identify optimal model configurations. Model evaluation revealed that CatBoost outperformed XGBoost, achieving a mean squared error (MSE) of 68.12, root mean squared error (RMSE) of 8.25, mean absolute error (MAE) of 5.67, and an R² score of 0.92, indicating high predictive accuracy and the ability to explain 92% of the variance in yield. XGBoost also performed strongly, with an R² of 0.90. Explainable AI techniques using SHAP analysis identified the most influential features driving model predictions and provided transparency into feature interactions and their impact on yield outcomes. Visualizations of actual versus predicted yields and feature importance further validated the models&amp;apos; effectiveness. The proposed pipeline demonstrates a systematic and reproducible approach to chemical yield prediction, offering valuable insights for process optimization and experimental planning in chemical research and industry. Hyperparameter optimization ; Feature (linguistics) ; Mean squared error ; Pipeline (software) ; Outlier ; Hyperparameter ; Dimensionality reduction ; Feature selection ; Variance (accounting) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Yudian Lin | Baoyu Xu | Mengmeng Dai | Jiangcan Pang | Chenyang Huang | Huanyu Xu | Simin Zhao | Shengzhou Li | Dongbo Dai | Huiran Zhang
Source:  (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

AS-MPNN: An Adaptive Substructure-Aware Message Passing Neural Network for Enhanced Polymer Property Prediction Polymer property prediction is essential in the discovery and design of polymers. Graph Neural Networks (GNNs) are widely used for this task due to their ability to process molecular graphs. However, existing GNN-based models often represent graphs at the atomic level, overlooking critical substructural or functional group information, which limits their ability to capture the multiscale characteristics of polymers. Recent studies suggest integrating substructural information to capture more advanced chemical features, but current substructure fragmentation methods rely on predefined rules, lacking flexibility. To address this, we propose the Adaptive Substructure-aware Message Passing Neural Network (AS-MPNN), which enhances polymer property prediction by combining atom-level and substructure-level representations. By integrating Morgan fingerprints with a Random Forest (RF) model, AS-MPNN flexibly captures key substructures, ensuring optimal fragmentation. Benchmarking on multiple datasets demonstrates the model's exceptional performance and strong generalization across tasks. Property (philosophy) ; Artificial neural network ; Generalization ; Process (computing) ; Key (lock) ; Benchmarking ; Substructure Machine Learning in Materials Science
Authors: Tiago de Sousa Araújo Cassiano | Marcelo Lopes Pereira | Pedro Henrique de Oliveira Neto | Luiz Antônio Ribeiro
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Determining charge transport regimes in organic molecular crystals: a machine learning framework Charge transport performance in organic molecular crystals (OMCs) is crucial for advancements in nanotechnology. Experiments have shown metallic-like and semiconducting charge transport regimes in OMCs, mediated by free electrons with Bloch-like oscillations (BOs) and polaronic states. In metallic-like regimes, the charge propagates as a wave, while in semiconducting regimes, it travels as a quasi-particle coupling charge with a cloud of lattice phonons. While the conditions for polaronic states in OMCs are well-established, those enabling BOs still need to be understood. In this study, we identify the electronic and structural properties of OMCs that favor the formation of polarons or BOs by analyzing their linear and wave transport properties. We employ semiempirical non-adiabatic dynamical simulations at the picosecond scale and machine learning methods to map the parameter spaces where the charge transport occurs <i>via</i> polarons or BOs. The dynamical simulations are based on a general model Hamiltonian developed to address OMCs. Our results reveal that increasing the electronic transfer rate between molecules, the crystal's speed of sound, and its metallicity favors the formation of BOs. BOs in OMCs can exhibit frequencies around 2 THz and current amplitudes up to 3000 |<i>e</i>| ps<sup>-1</sup>, opening up possibilities for high-frequency applications. Conversely, large polarons are predominantly formed based on the interplay between intra- and intermolecular electron-lattice interactions. Charge (physics) ; Electron transport chain ; Chemical physics ; Electron ; Nanotechnology ; Materials science ; Transport phenomena ; Metal ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Inorganic and Organometallic Chemistry
Authors: Cibrán López | Joshua Ojih | Minglie Hu | J. Ll. Tamarit | Edgardo Saucedo | Claudio Cazorla
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal management technologies. However, their prediction is computationally intensive due to the need to account for finite-temperature effects. Here, we present an uncertainty-aware machine-learning-guided framework for high-throughput prediction of temperature-induced polymorphic phase transitions in inorganic crystals. By combining density functional theory calculations with graph-based neural networks trained to estimate vibrational free energies, we screened a curated dataset of approximately 50,000 inorganic compounds and identified over 2,000 potential solid-solid transitions within the technologically relevant temperature interval 300-600 K. Among our key findings, we uncover numerous phase transitions exhibiting large entropy changes (&gt; 300 J K$^{-1}$ kg$^{-1}$), many of which occur near room temperature hence offering strong potential for solid-state cooling applications. We also identify $21$ compounds that exhibit substantial relative changes in lattice thermal conductivity (20-70%) across a phase transition, highlighting them as promising thermal switching materials. Validation against experimental observations and first-principles calculations supports the robustness and predictive power of our approach. Overall, this work establishes a scalable route to discover functional phase-change materials under realistic thermal conditions, and lays the foundation for future high-throughput studies leveraging generative models and expanding open-access materials databases. Phase transition ; Thermal conductivity ; Density functional theory ; Thermal ; Entropy (arrow of time) ; Robustness (evolution) ; Scalability ; Experimental data Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ashley Phala | David Tshwane | Charles Siyasiya | Sisa Pityana | Regina Maphanga
Source: MATEC Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of the optimal hydrogen storage in TiVCrMnAlFe alloys using machine learning models The quest for efficient hydrogen storage materials is vital for the advancement of clean energy technologies. Among various candidates, high entropy alloys (HEAs) such as TiVCrFeAl have received significant attention due to their tunable structures and favorable thermodynamic properties. This study explores the application of machine learning (ML) techniques to predict the hydrogen storage capacity of the TiVCrFeAl alloy system. Using a dataset compiled HEAPS and calculated properties, multiple regression models, such as Random Forest, Gradient Boosting, Decision Tree, XGBoost, Linear Regression and Support Vector Regression, were trained to capture complex relationships between alloy composition, processing parameters, and hydrogen weight percent (wt%). Data preprocessing steps included feature selection, imputation of missing values, and standardization to ensure model robustness. The performance of the models was evaluated using cross-validation and test set metrics such as R² and mean squared error. Results show that ensemble-based models, particularly Random Forest and XGBoost, achieved high predictive accuracy, demonstrating the effectiveness of ML in modeling nonlinear property trends in HEAs. This approach offers a powerful tool for screening and optimizing hydrogen storage materials, accelerating the discovery process through computational insight. Random forest ; Support vector machine ; Data pre-processing ; Linear regression ; Hydrogen storage ; Standardization ; Nonlinear system ; Mean squared error ; Preprocessor Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Li Wang (15202) | Jingbai Li (2868986)
Source: Figshare (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Modeling the NonradiativeDecay of Cyclooctatetrathiophenein Solution and Crystal Phases Using Machine Learning Modeling excited-state dynamics of molecular aggregates via multiscale calculations remains challenging due to the resource-intensive excited-state electronic structure calculations. This study presents a direct combination of machine learning (ML)-accelerated excited-state calculations and semiempirical-level molecular mechanics methods, specifically GFN-FF, within the ONIOM scheme. The ML-photodynamics simulations reveal the critical role of neighboring π<sub>CC</sub> and σ<sub>CC</sub> bond torsion in controlling the nonradiative decay of the well-known aggregation-induced emission (AIE) molecule cyclooctatetrathiophene (COTh) in THF solution and COTh crystal. The predicted fluorescence quantum yield enhancement factors, ranging from 3- to 22-fold in the solution to the crystal, are in good agreement with the experimental results. The trajectory analysis revealed increasing restrictions on the π<sub>CC</sub> bond torsion of COTh from the THF solution to the COTh crystal, which blocked the nonradiative decay pathways. Our approach provides a quantitative understanding of the AIE mechanisms of COTh and is expected to be applied in the rational design of AIE materials in the future. Torsion (gastropod) ; ONIOM ; Molecular dynamics ; Crystal (programming language) ; Yield (engineering) ; Quantum yield ; Molecule ; Crystal structure prediction Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics ; Nonlinear Optical Materials Studies
Authors: Wan‐Lu Li | Xujian Wang | Haocheng Tang | Xiongwu Wu | Bernard R. Brooks | Junmei Wang
Source: Research Square (preprint, 2025-11-25)
Field: Materials Science — Materials Chemistry

Redefining Computational Enzymology with Multiscale Machine Learning/Molecular Mechanics: Catalytic Mechanism and Stereoselectivity in Diels–Alderases Mechanism (biology) ; Electrostatics ; Rational design ; Molecular dynamics ; Work (physics) ; Catalysis ; Molecular machine ; Static electricity ; Enzyme catalysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Ethan Berger | Mohammad Bagheri | Hannu‐Pekka Komsa
Source: ArXiv.org (preprint, 2025-04-09)
Field: Materials Science — Materials Chemistry

Screening of material defects using universal machine-learning interatomic potentials Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has been dramatically accelerated by the invent of universal machine-learning interatomic potentials that offer first principles accuracy at orders of magnitude lower computational cost. Their application to the screening of defects with desired properties or to finding new stable compounds with high density of defects, however, has not been explored. Here, we show that the universal machine-learning interatomic potentials have reached sufficient accuracy to enable large-scale screening of defective materials. We carried out vacancy calculations for 86 259 materials in the Materials Project database and analyzed the formation energies in terms of oxidation numbers. We further demonstrate the application of these models for finding new materials at or below the convex hull of known materials and for simulated etching of low-dimensional materials. Interatomic potential ; Convex hull ; Vacancy defect ; Set (abstract data type) ; Density functional theory ; Regular polygon Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electrocatalysts for Energy Conversion
Authors: Kazuumi Fujioka | Ryan M. Richard | Jonathan M. Waldrop | Yuheng Luo | Theresa L. Windus | Rui Sun
Source: Journal of Chemical Theory and Computation (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

VENUSpy: A Chemical Dynamics Simulation Program in the Era of Machine Learning and Exascale Computing The field of chemical reaction dynamics has evolved considerably since its inception, driven by advances in computational power and theoretical methodologies. While <i>ab initio</i> molecular dynamics (AIMD) simulations offer high accuracy by computing forces directly from electronic structure theory, its high computational cost limits its applicability to small systems and short time scales. Machine learning (ML) potentials and exascale computing offer new ways of developing potential energy surfaces (PESs) for chemical reaction dynamics simulations of longer times scales and larger systems. In this work, we introduce VENUSpy, a Python-based reimplementation and extension of the classical VENUS code, designed to interface with existing ML-based potentials and the exascale-ready quantum chemistry package NWChemEx. While NWChemEx is in development, VENUSpy demonstrates interfacing with its top-level classes and objects for use in reaction dynamics simulations. VENUSpy preserves the large library of initial sampling and classical trajectory propagation of the original, Fortran-based VENUS, while expanding its versatility through integration with modern Python tools. This modular framework enables ML/<i>ab initio</i> hybrid dynamics simulations, which offers distinct advantages to AIMD simulations and MLMD simulations, as well as fosters rapid development of new methodologies for studying complex reactive systems. Interfacing ; Exascale computing ; Modular design ; Chemical Dynamics ; Field (mathematics) ; Python (programming language) ; Molecular dynamics ; Interface (matter) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Choy Boy | Edoardo Altamura | Dilhan Manawadu | Ivano Tavernelli | Stefano Mensa | David J Wales
Source: Machine Learning Science and Technology (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Encoding molecular structures in quantum machine learning Abstract Quantum machine learning (QML) has great potential for the analysis of chemical datasets. However, conventional quantum data-encoding schemes, such as fingerprint encoding, are generally unfeasible for the accurate representation of chemical moieties in such datasets. In this contribution, we introduce the quantum molecular structure encoding (QMSE) scheme, which encodes the molecular bond orders and interatomic couplings expressed as a hybrid Coulomb-adjacency matrix, directly as one- and two-qubit rotations within parametrised circuits. We show that this strategy provides an efficient and interpretable method in improving state separability between encoded molecules compared to other fingerprint encoding methods, which is especially crucial for the success in preparing feature maps in QML workflows. To benchmark our method, we train a parametrised ansatz on molecular datasets to perform classification of state phases and regression on boiling points, demonstrating the competitive trainability and generalisation capabilities of QMSE. We further prove a fidelity-preserving chain-contraction theorem that reuses common substructures to cut qubit counts, with an application to long-chain fatty acids. We expect this scalable and interpretable encoding framework to greatly pave the way for practical QML applications of molecular datasets. Encoding (memory) ; Scalability ; Qubit ; Feature (linguistics) ; Benchmark (surveying) ; Representation (politics) ; Quantum ; State (computer science) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Jayashrita Debnath | Gerhard Hummer
Source: ArXiv.org (preprint, 2025-09-07)
Field: Materials Science — Materials Chemistry

Random functions as data compressors for machine learning of molecular processes Machine learning (ML) is rapidly transforming the way molecular dynamics simulations are performed and analyzed, from materials modeling to studies of protein folding and function. ML algorithms are often employed to learn low-dimensional representations of conformational landscapes and to cluster trajectories into relevant metastable states. Most of these algorithms require selecting a small number of features that describe the problem of interest. Although deep neural networks can tackle large numbers of input features, the training costs increase with input size, which makes the selection of a subset of features mandatory for most problems of practical interest. Here, we show that random nonlinear projections can be used to compress large feature spaces and make computations faster without substantial loss of information. We describe an efficient way to produce random projections and then exemplify the general procedure for protein folding. For our test cases NTL9 and the double-norleucin variant of the villin headpiece, we find that random compression retains the core static and dynamic information of the original high dimensional feature space and makes trajectory analysis more robust. Feature (linguistics) ; Trajectory ; Folding (DSP implementation) ; Computation ; Feature selection ; Artificial neural network ; Data compression ; Random forest Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chen Hua | Jing Liu
Source: arXiv (Cornell University) (preprint, 2025-01-09)
Field: Materials Science — Materials Chemistry

Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor Superconductors, which are crucial for modern advanced technologies due to their zero-resistance properties, are limited by low Tc and the difficulty of accurate prediction. This article made the initial endeavor to apply machine learning to predict the critical temperature (Tc) of liquid metal (LM) alloy superconductors. Leveraging the SuperCon dataset, which includes extensive superconductor property data, we developed a machine learning model to predict Tc. After addressing data issues through preprocessing, we compared multiple models and found that the Extra Trees model outperformed others with an R2 of 0.9519 and an RMSE of 6.2624 K. This model is subsequently used to predict Tc for LM alloys, revealing In0.5Sn0.5 as having the highest Tc at 7.01 K. Furthermore, we extended the prediction to 2,145 alloys binary and 45,670 ternary alloys across 66 metal elements and promising results were achieved. This work demonstrates the advantages of tree-based models in predicting Tc and would help accelerate the discovery of high-performance LM alloy superconductors in the coming time. Alloy ; Superconductivity ; Tree (set theory) ; Materials science ; Artificial intelligence ; Metallurgy ; Computer science ; Machine learning ; Physics ; Condensed matter physics ; Mathematics ; Combinatorics Machine Learning in Materials Science