jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdallah Brahim Elhadj Ali | Pale Wang-yang | Yapara Kanabet | Stève-Jonathan Koyambo-Konzapa
Source: Oriental Journal Of Chemistry (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Prediction of new ESIPT Molecules by Machine Learning and Quantum Modelling The discovery of novel compounds exhibiting Excited State Intramolecular Proton Transfer (ESIPT) is critical for advancing technologies in optoelectronics, biosensors, and smart materials. Conventional approaches dependent on exhaustive quantum chemical simulations are inefficient for exploring expansive molecular libraries. We introduce an integrated computational strategy that combines Density Functional Theory (DFT) with supervised machine learning to efficiently classify ESIPT propensity. Using a curated set of 100 organic molecules, we implemented and compared three classifiers: Random Forest (RF), Support Vector Machine (SVM), and a Deep Neural Network (DNN). The RF model delivered the most robust performance, attaining 94.2% accuracy and a 91.1% F1-score under stratified cross validation. Analysis of feature importance identified key electronic descriptors, including absorption wavelength and LUMO energy, as primary predictors. This hybrid DFT-ML pipeline provides a rapid and reliable method for the virtual screening and targeted design of new ESIPT active materials. Virtual screening ; Pipeline (software) ; Random forest ; Feature (linguistics) ; Density functional theory ; Support vector machine ; Convolutional neural network ; Intramolecular force ; Quantum Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Yuan Zhou | Junxin Yang | Shuailong Hu | Fen Wang | Qiuxiao Chen | Zijun Chen | Erbo Xiao | Ziyue You | Zhijin He | Shiyu Rao | Chao Yang | Le-Hua Liu
Source: Materials Futures (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Record-low elastic modulus in <i>β</i> -titanium alloys designed using a domain adversarial neural network Abstract The development of β -titanium alloys with bone-mimicking elastic moduli remains a significant challenge. Although machine learning has the potential to accelerate alloy discovery, traditional methods often face data limitations such as sparsity, compositional discontinuity, and feature heterogeneity, leading to overfitting and restricting the exploration of novel compositional spaces. In this study, we introduce a domain-adversarial neural network framework that balances predictive accuracy with the generalization ability of unexplored composition space through integrated feature alignment and adversarial training. Using this approach, we successfully developed a non-intuitive β -Ti alloy with an ultra-low elastic modulus of 28 ± 3 GPa, providing new insights beyond conventionally designed biomedical titanium alloys. This work establishes a screening framework for materials discovery in small-sample data spaces, with broad implications for the design of biomedical and other alloy systems. Overfitting ; Artificial neural network ; Generalization ; Domain (mathematical analysis) ; Feature (linguistics) ; Elastic modulus ; Titanium alloy ; Adversarial system Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Artificial Intelligence in Healthcare and Education
Authors: Zhede Zhao | Tao Hu | Shuyu Bi | Dongwei Guan | Songzhe Xu | Chaoyue Chen | Weidong Xuan | Zhongming Ren
Source: Journal of Materials Informatics (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Advances in graph neural networks for alloy design and properties predictions: a review Graph neural networks (GNNs) have become a transformative modeling paradigm in materials science, offering a data-efficient and structure-aware approach for learning from complex material systems. This review focuses on the recent progress of GNNs in alloy design and property prediction. We begin by introducing the foundational concepts of graph representations and the general architecture of GNNs, including node embeddings, message passing, and pooling strategies. The review then categorizes major types of GNNs, including supervised and unsupervised learning, with a focus on the achievements and applications of GNNs in materials modeling, and discusses their strengths and inherent limitations in the context of materials modeling. Particular emphasis is placed on the application of GNNs in the alloy domain, covering a diverse range of data types, from atomic structures and compositions to microstructural images, and target properties, such as mechanical strength, thermal stability, and phase stability. We highlight how GNNs are integrated into alloy composition optimization, multi-property prediction, and frontier research workflows. The review concludes with a summary of multi-model and multiscale approaches and outlines key challenges and future directions for constructing generalizable, physics-informed GNN frameworks for alloy discovery. Pooling ; Transformative learning ; Context (archaeology) ; Focus (optics) ; Graph ; Representation (politics) ; Artificial neural network Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Electrocatalysts for Energy Conversion
Authors: JingChun Wang | Meenu Upadhyay | Eric D. Boittier | Kham Lek Chaton | Valerii Andreichev | M. Devereux | Raidel Martin Barrios | Shimoni Patel | Sena Aydin | Kai Töpfer | Markus Meuwly
Source: Small Structures (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Cluster Models for Next‐Generation, Machine‐Learning‐Based Energy Functions for Molecular Simulations Energy functions for pure and heterogeneous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine‐learning‐based potential energy surfaces (ML‐PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel‐ and neural network‐based approaches for intramolecular degrees of freedom are combined with distributed charge models for long range electrostatics to describe the interaction energies of condensed phase systems. The main focus is on illustrative examples ranging from pure liquids (dichloromethane (DCM), water) to chemically and structurally heterogeneous systems (eutectic liquids, CO on amorphous solid water), reactions (Menshutkin), and spectroscopy (triatomic probes for protein dynamics). For all examples, small to medium‐sized clusters are used to represent and improve the total interaction energy compared with reference quantum chemical calculations. Although remarkable accuracy can be achieved for some systems (chemical accuracy for DCM and water), it is clear that more realistic models are required for van der Waals contributions and improved water models need to be used for more quantitative simulations of heterogeneous chemical and biological systems. van der Waals force ; Intramolecular force ; Representation (politics) ; Cluster (spacecraft) ; Degrees of freedom (physics and chemistry) ; Molecular dynamics ; Electrostatics ; Range (aeronautics) ; Potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Xujian Wang | Haocheng Tang | Xiongwu Wu | Bernard R. Brooks | Junmei Wang | Wan-Lu Li
Source: ChemRxiv (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Advancing Computational Enzymology with Multiscale Machine Learning/Molecular Mechanics: Catalytic Mechanism and Stereoselectivity in Diels–Alderases Enzymes catalyze complex chemical transformations with remarkable efficiency and selectivity, yet their atomistic mechanisms remain challenging to capture because conventional simulations trade accuracy for efficiency. Here we introduce a reactive machine learning/molecular mechanics (ML/MM) framework that bridges quantum chemistry with long-timescale sampling, enabling direct exploration of enzymatic transition states and free-energy landscapes. Coupled with metadynamics, this approach achieves nanosecond sampling of bond-forming reactions and quantitatively predicts activation barriers, mutational effects, and stereoselectivity. Applied to Diels–Alderases, the framework not only reproduces experimental activity andendo/exo preferences with sub-kcal/mol accuracy but also uncovers how pathway dynamics and local electrostatics preorganize substrates for selective outcomes. By uniting reactivity, conformational dynamics, and predictive power, this work establishes reactive ML/MM as a broadly applicable strategy for mechanistic enzymology and a foundation for the rational design of new biocatalysts. Mechanism (biology) ; Electrostatics ; Rational design ; Molecular dynamics ; Work (physics) ; Catalysis ; Static electricity ; Stereoselectivity ; Enzyme catalysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: YunZheng Zhou | Minwei Chen
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Tracking Oxidative Functional Group Formation in Polymer-Based Sports Protective Equipment through Chemometric Deep Learning Deep learning ; Preprocessor ; Oxidative phosphorylation ; Degradation (telecommunications) ; Artificial neural network ; Generalization ; Tracking (education) ; Chemometrics Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Fault Detection and Control Systems
Authors: Taichi SOMEGO | Yasuhiro Ikabata | Midori Goto
Source: Journal of Computer Chemistry Japan (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Development of Machine Learning Models to Reproduce Coupled-Cluster Molecular Energies We attempted to reproduce molecular energies at the coupled-cluster level from molecular structures by constructing machine learning models that reproduce atomic environment energies, defined as the difference between the atomic energy in a molecule and that of the corresponding isolated atom. To obtain atomic energies from quantum chemical calculations, we adopted energy density analysis. Our results showed that the prediction accuracy of coupled-cluster molecular energies was comparable to that for density functional theory. Development (topology) ; Quantum chemical ; Energy (signal processing) ; Density functional theory ; Molecule ; Quantum ; Total energy ; Molecular dynamics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Physical and Chemical Molecular Interactions
Authors: Haolong Chen | Yuanlin Yi | Zhaotao Liu | Zhanli Liu | HuanLin ZHOU
Source: International Journal of Heat and Mass Transfer (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Identification of thermal conductivities for nonhomogeneous materials based on the hybrid machine learning model Convolutional neural network ; Discrete cosine transform ; Thermal conductivity ; Thermal conduction ; Feature (linguistics) ; Artificial neural network ; Thermal ; Temperature measurement ; Identification (biology) Thermal properties of materials ; Numerical methods in inverse problems ; Composite Material Mechanics
Authors: Sihan Fan | Hongyang Zhao | Yang Gao | Likun Pan | Fu-Zhen Xuan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Optimizing Electrocatalytic Hydrogen Evolution via Rational Design of Electrolyte Environments Electrolyte ; Overpotential ; Rational design ; Generalizability theory ; Quantitative structure–activity relationship ; Electrocatalyst ; Ionic bonding ; Ionic liquid Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Battery Materials and Technologies
Authors: M. ZILBERGLEIT | G. MOROZOV
Source: Herald of Polotsk State University Series B Industry Applied Sciences (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

PRACTICAL ASPECTS OF APPLYING NEURAL NETWORKS AND GENERATIVE MODELSIN TRAINING CHEMICAL ENGINEERING STUDENTS(USING MICROSOFT COPILOT AS AN EXAMPLE) Due to the expanding use of neural networks in various industries, challenges arise in preparing students to apply artificial intelligence in the educational process. The article examines the prospects and practical aspects of employing artificial neural networks and large language models (using Microsoft Copilot as an example) in the training of students specializing in chemical engineering. Specific examples are provided of solving problems in courses such as Processes and Equipment of Chemical Production, General Chemical Technology, and Chemical Reaction Kinetics. It is demonstrated how the use of artificial intelligence enables the automation of routine calculations, visualization of results (material balances, kinetic curves), and modeling of chemical reactor operation. The introduction of neural network technologies into the educational process is shown to significantly reduce calculation time and shift students’ focus from mathematical derivations to the physico-chemical analysis of processes. Artificial neural network ; Automation ; Visualization ; Process (computing) ; Training (meteorology) ; Generative grammar Machine Learning in Materials Science ; Electric Power Systems and Control ; Ergonomics and Human Factors
Authors: Jayashrita Debnath | Gerhard Hummer
Source: Journal of Chemical Theory and Computation (article, 2026-01-29)
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 cluster trajectories into relevant metastable states. Most of these algorithms require the selection of 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 a 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, making trajectory analysis more robust. Feature (linguistics) ; Folding (DSP implementation) ; Computation ; Feature selection ; Trajectory ; Selection (genetic algorithm) ; Artificial neural network ; Random forest Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhiyu Shi | Aditya Lele | Ahren W. Jasper | Stephen J. Klippenstein | Yiguang Ju
Source:  (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

<i>Ab Initio</i> -Trained Machine Learning Molecular Dynamics Model for Radical Reactions in Hydrogen Combustion Machine learning-based molecular dynamics (ML-MD) has emerged as a powerful tool that bridges the accuracy of quantum mechanical methods with the efficiency of classical molecular dynamics. However, existing ML-MD models often fall short in accurately predicting individual reaction rates due to incomplete sampling of reaction pathways and the absence of a multifidelity framework that combines data of varying precision. In this work, we develop and validate integrated ab initio-trained ML-MD (aML-MD) models for two key radical reactions in hydrogen combustion: H + HO2 → H2 + O2 and O + HO2 → O2 + OH. Using the DeePMD-kit framework, we first construct an aML-MD model trained solely on high-accuracy ab initio data and demonstrate its ability to predict individual reaction rates across distinct pathways via quasi-classical trajectory (QCT) calculations with excellent agreement to PES-based results. We then apply transfer learning to build another aML-MD model that incorporates a large set of moderate-accuracy DFT/PES data and a small subset of ab initio data. This multifidelity approach reduces computational cost by over fivefold while maintaining predictive accuracy within 20% of PES-based benchmarks. Further NEB simulations indicate that transfer learning yields a more accurate reaction barrier, thereby enhancing the model’s fidelity in capturing the underlying reaction dynamics. The results demonstrate the effectiveness of combining multifidelity data and transfer learning to enable general-purpose ML-MD models capable of accurately capturing complex reaction dynamics, with promising applications to larger, multi-reaction chemical systems. Ab initio ; Transfer of learning ; Molecular dynamics ; Experimental data ; Set (abstract data type) ; Force field (fiction) ; Combustion ; Computational model ; Fidelity Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Combustion Engine Technologies
Authors: José X. Soares | Daniela R. P. Loureiro | Rafael C. Castro | David S. M. Ribeiro | Ines Häusler | Tolga Wagner | Hüseyin Çelik | Sarmento J. Mazivila | Carlos Afonso | João L. M. Santos
Source: Chemistry of Materials (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Decoding Carbon Dot Synthesis: Machine Learning-Guided Analysis of Reaction Conditions Governing Fluorophore vs Nanoparticle Formation Carbon dots (CDs) are fluorescent nanoparticles whose success stems from accessible bottom-up synthetic methodologies, using readily available substrates as precursors. Despite considerable interest in CDs, uncertainty surrounds their luminescence origin as molecular fluorophores (MFs) formed during thermal treatment may be the true source of fluorescence. The realization that MFs coexist with CD formation has intensified focus on purification protocols, yet reaction conditions have remained comparatively neglected due to the extensive parameter space. In this work, the synthetic conditions governing CD formation were systematically investigated. Ubiquitous citric acid (CA) and ethylenediamine (EDA) served as our carbon and nitrogen sources, respectively. Both hydrothermal and microwave heating methodologies were employed. The resultant products were subjected to meticulous examination, with particular emphasis upon their fluorescence properties, quantified through quantum yield measurements, and their size distributions, assessed via high-pressure size exclusion chromatography. Machine learning algorithms were employed to establish correlations between synthesis parameters and the size and fluorescence characteristics of the resultant products. Reaction mixtures of CA/EDA 1:1 at pH 8.5, subjected to microwave heating at 205 °C for 25 min, yield predominantly MFs, whereas reaction mixtures of CA/EDA 1:2 at pH 4.5, subjected to hydrothermal treatment at 260 °C for 15 h, yield CDs. Beyond furnishing guidance for tuning reactions toward either MF or CD formation, these findings permit elucidation of the underlying synthetic mechanisms: mildly acidic conditions favor polymerization and subsequent carbonization into CDs at elevated temperatures, while mildly basic conditions impede reactivity, arresting the synthetic pathway at the molecular level. Yield (engineering) ; Carbon fibers ; Fluorescence ; Nanoparticle ; Quantum yield ; Dispersity ; Polymerization ; Citric acid ; Fluorophore Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Melamine detection and toxicity
Authors: Mahyar Rajabi Kochi | Hanie Rezaei | Sartaaj Takrim Khan | Bhanu Teja Mamillapalli | Maryam Ebrahimiazar | Haoming Ye | Rose Moosavian | Mohammad Zargartalebi | Dave Sinton | Seyed Mohamad Moosavi
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for predicting temperature-dependent chemical properties Physics-informed machine learning model decouples chemistry from thermodynamic state, including temperature, to accurately predict fluid properties across thermodynamic conditions. It enables reliable design of new cooling fluids. Function (biology) ; Energy (signal processing) ; Artificial neural network ; Chemical energy ; Experimental data Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Wahyu Sasongko Putro | Yandhika Surya Akbar Gumilang | Farid Baskoro
Source: JURNAL HURRIAH Jurnal Evaluasi Pendidikan dan Penelitian (article, 2026-03-28)
Field: Chemistry — Inorganic Chemistry

Energy Density Prediction of Metal-Organic Frameworks (MOFs) From Synthesis Conditions Using Deep Neural Network (DNN): Hydrogen Storage Application The global transition toward sustainable energy systems necessitates efficient and scalable hydrogen storage technologies. Metal–organic frameworks (MOFs) have emerged as promising candidates for hydrogen storage due to their high surface area, tunable pore structures, and favorable surface chemistry that enhance adsorption performance. However, real-time experimental measurement of hydrogen uptake using physical sensing systems is costly, computationally intensive, and operationally complex. To address these limitations, this study proposes a data-driven soft-sensor framework based on machine learning to predict energy density for hydrogen storage applications from synthesis parameters. High-fidelity secondary data sourced from an open-access Kaggle dataset were utilized, focusing on synthesis descriptors including metal type, oxidation state, temperature, and reaction time. Recognizing the intrinsic influence of transition metals on structural stability and adsorption behavior, a per-metal modeling strategy was implemented to capture material-specific relationships. A Deep Neural Network (DNN) employing a Multi-Layer Perceptron (MLP) architecture trained via backpropagation was developed to model nonlinear interactions between structural variables and energy density. To enhance interpretability, complementary linear regression models were also constructed, yielding explicit predictive equations. Model performance was rigorously evaluated using statistical error metrics, achieving a Mean Squared Error (MSE) of 0.0821 and a Root Mean Squared Error (RMSE) of 0.2852, demonstrating strong predictive capability and generalization across different metallic linkers. The low error values confirm that artificial neural network–based soft sensors provide a reliable, low-latency alternative to physical sensing systems for monitoring hydrogen storage performance. This approach significantly reduces experimental burden, accelerates materials screening, and supports intelligent optimization of hydrogen-based fuel cell technologies, contributing to the advancement of scalable clean energy infrastructure Artificial neural network ; Backpropagation ; Hydrogen storage ; Mean squared error ; Perceptron ; Multilayer perceptron ; Scalability ; Linear regression ; Generalization Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Hydrogen Storage and Materials