jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leonardo Medrano Sandonas | Mirela Puleva | Ricardo Parra Payano | Martin Stöhr | Gianaurelio Cuniberti | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

Advancing Density Functional Tight-Binding method for Large Organic Molecules through Equivariant Neural Networks Semi-empirical electronic structure methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett. 11, 16 (2021)], we improved the accuracy of the semi-empirical density functional tight-binding (DFTB) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN$_{\rm rep}$ to bridge the gap between electronic DFTB components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN$_{\rm rep}$, we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable $\Delta_{\rm TB}$ many-body potentials, replacing the standard pairwise repulsive potential in the DFTB method. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems, going beyond the chemical space represented in the training quantum-mechanical datasets. Indeed, the enhanced performance of EquiDTB over the standard TB methods (DFTB and GFN2-xTB) is demonstrated by the accurate computation of the atomic forces of equilibrium and non-equilibrium small molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules---for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. Therefore, our work highlights that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method to achieve DFT-PBE0 level accuracy with high computational efficiency, paving the way for more reliable (bio)molecular simulations. Organic molecules ; Artificial neural network ; Equivariant map ; Tight binding ; Density functional theory ; Molecule ; Computer science ; Biological system ; Computational chemistry ; Chemistry ; Artificial intelligence ; Mathematics ; Biology ; Pure mathematics ; Electronic structure ; Organic chemistry Machine Learning in Materials Science
Authors: Yaoyuan Zhang | Mengqiu Long | Cheng Sui | Wu‐Xing Zhou
Source: Chinese Physics B (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Unveiling the thermal transport mechanisms in novel carbon-based graphene-like materials using machine-learning potential Abstract This study presents a systematic investigation of thermal transport properties in a novel class of carbon-based graphene-like materials (AKCs). Through first-principles calculations combined with the phonon Boltzmann transport equation and machine-learning potential, we analyzed the lattice thermal conductivity and its microscopic mechanisms in three structures: AKC60, AKC33, and AKC41. The research reveals that these materials exhibit significant in-plane thermal conductivity at room temperature (191.0 W/m·K, 122.6 W/m·K, and 248.3 W/m·K, respectively), though an order of magnitude lower than that of graphene. Through detailed analysis of phonon dispersion relations, group velocities, three-phonon scattering phase space, and Grüneisen parameters, we uncovered the physical origins of AKCs’ relatively lower thermal conductivity. The findings indicate that despite AKC60’s larger primitive cell, its better preservation of graphene’s honeycomb structure leads to superior harmonic properties, resulting in higher thermal conductivity than that of AKC33 with its smaller primitive cell. These discoveries provide valuable guidance for AKCs’ applications in future electronic devices. Graphene ; Materials science ; Thermal ; Carbon fibers ; Nanotechnology ; Computer science ; Composite material ; Physics ; Thermodynamics ; Composite number Thermal properties of materials ; Machine Learning in Materials Science
Authors: Mianji Huang | Jing Luo | Zhijun Guo | Yuzhe Zheng | Shengli Jin | Qu Shen | Waner Ding | Qinchen Zhu | Yinqiao Qi | Xinhai Zhao | Chunhui Shou | Shien Sun | Haidong Fan | Weiming Teng | Hongchang Shen | Cheng Hong | Hua Fang | Zhigang Song | Haiyan He
Source: Renewable Energy (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Materials-informed long-term photovoltaic energy yield prediction: Unveiling the dynamic degradation mechanisms of anti-reflective coatings via explainable machine learning Boosting (machine learning) ; Photovoltaic system ; Gradient boosting ; Decision tree ; Coating ; Mean squared error ; Energy (signal processing) ; Fidelity ; Superhydrophilicity Machine Learning in Materials Science ; Photovoltaic System Optimization Techniques ; Advanced Sensor and Energy Harvesting Materials
Authors: Junfeng Zhao | Lixin Tang | Jiyin Liu | Jian Wu | Xiangman Song
Source: The Journal of Physical Chemistry A (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

D3-ImgNet: A Framework for Molecular Properties Prediction Based on Data-Driven Electron Density Images Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet. This framework integrates group theory, density functional theory-related mechanisms, deep learning techniques, and multiobjective optimization mechanisms, embodying a methodological fusion of data analytics and system optimization. Initially, we focus on atomization energies as the primary target of our study, using the QM9 data set to demonstrate the framework's ability to predict molecular atomization energies with high accuracy and excellent exploration performance. We then further evaluate its predictive capabilities for dipole moments and forces with the QM9X data set, achieving satisfactory results. Additionally, we tested the D3-ImgNet framework on the S<sub>N</sub>2 reaction data set to demonstrate its ability to precisely predict the minimum energy paths of S<sub>N</sub>2 chemical reactions, showcasing its portability and adaptability in chemical reaction modeling. Finally, visualizations of the electronic density generated by the framework faithfully replicate the physical phenomenon of electron density transfer. We believe that this framework has the potential to accelerate property predictions and high-throughput screening of functional materials. Computer science ; Software portability ; Density functional theory ; Set (abstract data type) ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Pingyang Zhang | Shaodong Zhang | Yihan Qin | Tingting Du | Wei Lei | Xiangyu Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Thermal Switching Characteristics and Mechanism in Multi-Scale Graphene Foam Composites: A Machine-Learning-Based Molecular Dynamics Exploration Graphene ; Materials science ; Molecular dynamics ; Composite material ; Mechanism (biology) ; Scale (ratio) ; Thermal ; Mechanical engineering ; Nanotechnology ; Engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Mohammed Alqarni | Ali Alqarni
Source: Scientific Reports (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures Analysis of small-molecule drug solubility in binary solvents at different temperatures was carried out via several machine learning models and integration of models to optimize. We investigated the solubility of rivaroxaban in both dichloromethane and a variety of primary alcohols at various temperatures and concentrations of solvents to understand its behavior in mixed solvents. Given the complex, non-linear patterns in solubility behavior, three advanced regression approaches were utilized: Polynomial Curve Fitting, a Bayesian-based Neural Network (BNN), and the Neural Oblivious Decision Ensemble (NODE) method. To optimize model performance, hyperparameters were fine-tuned using the Stochastic Fractal Search (SFS) algorithm. Among the tested models, BNN obtained the best precision for fitting, with a test R² of 0.9926 and a MSE of 3.07 × 10⁻⁸, proving outstanding accuracy in fitting the rivaroxaban data. The NODE model followed BNN, showing a test R² of 0.9413 and the lowest MAPE of 0.1835. The Polynomial model yielded a lower test R² of 0.8200 and higher error rates, indicating its limitations in unravelling the underlying relationships for the solubility variations. This study shows that advanced machine learning models, particularly BNN and NODE, can predict pharmaceutical solubility and improve crystallization process design and optimization. Solubility ; Artificial neural network ; Henry's law ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Biological system ; Materials science ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Advanced Statistical Process Monitoring
Authors: Luke H. Elder | Alexey V. Onufriev
Source: The Journal of Physical Chemistry B (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution The accuracy of computational water models is crucial to atomistic simulations of biomolecules. Here we explore a decoupled framework that combines classical physics-based models with deep neural networks (DNNs) to correct residual error in hydration free energy (HFE) prediction. Our main goal is to evaluate this framework on out-of-distribution data (molecules that differ significantly from those used in training), where DNNs are known to struggle. Several common physics-based solvation models are used in the evaluation. Graph neural network architectures are tested for their ability to generalize using multiple data set splits, including out-of-distribution HFEs and unseen molecular scaffolds. Our most important finding is that for out-of-distribution data, where DNNs alone often struggle, the physics + DNN models consistently improve physics model predictions. For in-distribution data, the DNN corrections significantly improve the accuracy of physics-based models, with a final RMSE below 1 kcal/mol and a relative improvement between 40% and 65% in most cases. The accuracy of physics + DNN models tends to improve when the 6% of molecules with the highest experimental uncertainty are removed. This study provides insights into the potential and limitations of combining physics and machine learning for molecular modeling, offering a practical and generalizable strategy of using DNN as independent postprocessing correction. Artificial neural network ; Set (abstract data type) ; Artificial intelligence ; Machine learning ; Training set ; Computer science ; Data set ; Solvation ; Graph ; Statistical physics ; Physics ; Theoretical computer science ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mingyi Xue | Bojun Liu | Xuhui Huang
Source: ChemRxiv (preprint, 2025-10-16)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning in Chemistry: A Data Centred, Hands-on Introductory Machine Learning Course for Undergraduate Students Context (archaeology) ; Artificial neural network ; Chemistry education ; Perspective (graphical) ; Deep learning ; Property (philosophy) Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Abdul Wasim | Lars V. Schäfer | Jagannath Mondal
Source: Journal of Chemical Information and Modeling (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories With the remarkable stride in computing power and advances in Molecular Dynamics (MD) simulation programs, the crucial challenge of storing and sharing large biomolecular simulation data sets has emerged. By leveraging AutoEncoders, a type of artificial neural network, we developed a method to compress MD trajectories into significantly smaller latent spaces. Our method can save up to 98% in disk space compared to xtc, a highly compressed trajectory format from the widely used MD program package GROMACS, thus facilitating storage and sharing of simulation trajectories. Atom coordinates are very accurately reconstructed from compressed data. The method was tested across a diverse sets of biomolecular systems, including folded proteins, intrinsically disordered proteins, phospholipid bilayers, protein-ligand complexes, large protein complexes and membrane-bound protein systems. The reconstructed trajectories demonstrated consistent accuracy in recovering key biophysically relevant properties for proteins, lipids and composite systems. The compression efficiency was particularly beneficial for larger systems. This approach enables the scientific community to efficiently store and share large-scale biomolecular simulation data, potentially enhancing collaborative research efforts. The workflow, termed "compresstraj", is implemented in PyTorch and is publicly available at https://github.com/SerpentByte/compresstraj, offering a practical solution for handling the increasing volumes of data generated in biomolecular simulation studies. Computer science ; Artificial neural network ; Molecular dynamics ; Dynamics (music) ; Artificial intelligence ; Chemistry ; Computational chemistry ; Psychology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Supratim Ghosh | Nupur Jain | Raghavan B. Sunoj
Source: ACS Catalysis (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Efficient Machine Learning for Yield Prediction in Transition Metal-Catalyzed <i>meta</i> -C(sp <sup>2</sup> )–H Bond Activation and Other Reactions Developing machine learning (ML) models for yield prediction of chemical reactions has emerged as an important use case scenario in very recent years. In this space, reaction datasets present a range of challenges mostly stemming from imbalance and sparsity. Herein, we consider chemical language representations for reactions to tap into the potential of natural language processing models such as the ULMFiT (Universal Language Model Fine-Tuning) for yield prediction, which is customized to work across such distribution settings. We contribute a reaction dataset with more than 860 manually curated reactions collected from literature spanning over a decade, belonging to a family of catalytic meta-C(sp2)–H bond activation reactions of high contemporary importance. Taking cognizance of the dataset size, skewness toward the higher yields, and the sparse distribution characteristics, we developed (i) a time- and resource-efficient pretraining strategy for downstream transfer learning, and (ii) the CFR (classification followed by regression) model, which provided more accurate yield predictions than the traditional direct regression (DR) methods. Instead of the prevailing pretraining practice of using a large number of unlabeled molecules (1.4 million) from the ChEMBL dataset, we first created a pretraining dataset SSP1 (0.11 million) by using substructure-based mining from the PubChem database, which is found to be equally effective and more time-efficient in offering enhanced performance. The CFR model with the ULMFiT-SSP1 regressor achieved a notable RMSE of 8.40 ± 0.12 for the CFR-major and 6.48 ± 0.29 for the CFR-minor class in yield prediction on the title reaction, with a class boundary of yield at 53%. Furthermore, the CFR model is highly generalizable as evidenced by the significant improvement over the previous benchmarks on Buchwald–Hartwig coupling, Suzuki coupling, nickel-catalyzed C–O coupling, and USPTO reaction datasets. Our model provides a scalable and efficient framework that can assist in reaction discovery. chEMBL ; Yield (engineering) ; PubChem ; Class (philosophy) ; Support vector machine ; Probability distribution ; Cheminformatics ; Transfer of learning ; Chemical database Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: K. A. L. Lima | Rodrigo A. F. Alves | Elie A. Moujaes | Alexandre C. Dias | D. S. Galvõo | Marcelo Lopes Pereira | Luiz Antônio Ribeiro
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Physicochemical characterization of a new porous 2D semiconductor carbon allotrope, C<sub>16</sub>: an investigation <i>via</i> density functional theory and machine learning-based molecular dynamics This study comprehensively characterizes, with suggested applications, a novel two-dimensional carbon allotrope, C<sub>16</sub>, using density functional theory and machine learning-based molecular dynamics. This nanomaterial is derived from naphthalene and bicyclopropylidene molecules, forming a planar configuration with sp<sup>2</sup> hybridization and featuring 3-, 4-, 6-, 8-, and 10-membered rings. The cohesive energy of -7.1 eV per atom, the absence of imaginary frequencies in the phonon spectrum, and the retention of the system's topology after <i>ab initio</i> molecular dynamics simulations confirm the structural stability of C<sub>16</sub>. The nanomaterial exhibits a semiconducting behavior with a direct band gap of 0.59 eV and anisotropic optical absorption in the <i>y</i> direction. Assuming a complete absorption of incident light, it registers a power conversion efficiency of 13%, demonstrating relatively good potential for applications in solar energy conversion. Excluding the vacuum effect along the non-periodic <i>z</i> direction, the planar lattice thermal conductivity <i>κ</i><sub>L</sub> reaches ultralow values of 1.90 × 10<sup>-2</sup> W (m K)<sup>-1</sup>, 0.90 × 10<sup>-2</sup>, and 0.59 × 10<sup>-2</sup> for <i>T</i> = 300 K, 600 K, and 1000 K, respectively along both <i>x</i> and <i>y</i> directions. Very close to the Fermi level, the thermoelectric figure of merit (<i>zT</i>) can reach a maximum value of 0.93 at room temperatures along both planar directions, indicating an excellent ability to convert a temperature gradient into electrical power. Additionally, C<sub>16</sub> demonstrates high mechanical strength, with Young's modulus values of 500 GPa and 630 GPa in the <i>x</i> and <i>y</i> directions, respectively. Insights into the electronic, optical, thermoelectric, and mechanical properties of C<sub>16</sub> reveal its promising capability for energy conversion applications. Characterization (materials science) ; Density functional theory ; Materials science ; Molecular dynamics ; Semiconductor ; Porosity ; Carbon fibers ; Nanotechnology ; Chemical physics ; Porous medium ; Biological system ; Computational chemistry ; Chemistry ; Optoelectronics ; Composite material Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Ardiana Topi | Erdet Këlliçi | Daniel Hudhra | Dritan Topi
Source: Edelweiss Applied Science and Technology (article, 2025-07-02)
Field: Chemistry — Organic Chemistry

A machine learning-based classification of monocultivar olive oils—specifically Kalinjot, Ulli i bardhë Tirana, and Mixan—comparing their chemical composition Valued for its nutritional and economic value, olive oil (OO) has been subject to adulteration practices. This study aimed to develop a classification model based on chemical composition to identify OOs from three main cultivars: Kalinjot, Ulli i bardhë Tirana, and Mixan, which comprise the majority of plantations in Albania. Eighty-five OO samples spanning different crop years and locations were studied using three different machine-learning algorithm models. The performance metrics of the k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Random Forest are discussed in terms of their classification performance. The comparison of accuracy revealed that the Random Forest model outperformed the others, achieving an accuracy of approximately 93%, compared to 81% for kNN and 78% for SVM. This significant finding, along with the clear confusion matrix of Random Forest, selects it as the preferred model for distinguishing OO based on cultivar and geographic origin. This project will help oil and oil extraction companies verify the authenticity of their products and detect adulteration practices in the Albanian oil sector. Olive oil ; Chemical composition ; Composition (language) ; Chemistry ; Food science ; Art ; Organic chemistry Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses
Authors: Christopher J. Stein | Lisa Hetzel
Source: ChemRxiv (preprint, 2025-10-15)
Field: Materials Science — Materials Chemistry

Short-range machine-learning potentials for aqueous electrolyte solutions Generality ; Electrolyte ; Aqueous solution ; Observable ; Benchmark (surveying) ; Energy (signal processing) ; Residual ; Work (physics) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Fault Detection and Control Systems
Authors: Ogheneyoma Aghoghovbia | Joshua Ojih | Mingjun Hu
Source: Annual Reviews of Heat Transfer (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING FOR THERMAL PROPERTIES OF INORGANIC CRYSTALS Thermal ; Materials science ; Chemical engineering ; Physics ; Engineering ; Thermodynamics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure