jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Trieu Nguyen | Aleksandra Karolak
Source: ChemRxiv (preprint, 2025-05-23)
Field: Materials Science — Materials Chemistry

Expanding Molecular Design with Graph Variational Autoencoders: A Comparative Study of Pair-Encoding and Character Tokenization Deep generative models are transforming early-stage drug discovery, yet most current approaches are not well suited for realistic, small-data settings and often rely on simplified molecular representations such as linear strings, overlooking the inherent graph-based structure of molecules. To address this, we first developed the Transformer Graph Variational Autoencoder (TGVAE), an AI model that learns directly from molecular graphs and generates Simplified Molecular Input Line Entry System (SMILES) strings. Building upon this foundation, here we introduce the improved architecture of TGVAE (TGVAEv2) and a DeeperGAT-VAE (DGVAE) model, an upgraded architecture that incorporates lightweight and deeper graph-attention blocks, designed to enable future applications on smaller molecular datasets. Both models address common challenges in training, such as over-smoothing in graph neural networks and posterior collapse in VAEs, ensuring stable and chemically precise molecule generation. Across standard benchmarks, TGVAEv2 and DGVAE achieve high validity, uniqueness, diversity, and novelty, while reproducing key drug-like property distributions. In addition to architectural improvements, we also expanded the input tokenization by incorporating SMILES pair-encoding, which captures larger and chemically relevant substructures compared to character-level tokens and supports the generation of more diverse and novel molecules. By comparing both approaches, we present how the DGVAE can achieve improved performance with fewer parameters and faster convergence while maintaining chemical accuracy. Evaluation against PubChem confirms that SMILES pair-encoding greatly expands the space of scaffolds and fragments unseen in public databases. These advancements not only broaden the accessible chemical space but also set a new benchmark for the application of graph-based AI models in molecular generation and drug discovery. Character (mathematics) ; Encoding (memory) ; Lexical analysis ; Computer science ; Graph ; Artificial intelligence ; Natural language processing ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Xiang Zhou | Cong Wang | Hui‐Min Gao | Yuanhui Ji | Hongliang Qian
Source: Smart Chemical Engineering (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Prediction of Drug Solubility in Complex Micro-Environments: Machine Learning Study Based on Hansen Solubility Parameters Although machine learning (ML) has been widely applied to drug solubility prediction, most existing models focus on single-solvent systems, and accurate prediction in complex micro-environments (mixed solvents) remains challenging. Herein, we report the first predictive framework that integrates mechanism-driven microscopic variables (molecular descriptors and Hansen solubility parameters) into three ML algorithms: artificial neural networks (ANN), support vector machines (SVM), and random forests (RF). Among them, RF achieved the best performance, with the coefficient of determination (R2) markedly improved from 0.830 to 0.988 when Hansen parameters were included as input features. Analysis of factor interactions revealed that solvent hydrogen-bonding capacity, polarity, mixing ratio, and temperature play key roles in modulating drug solubility, consistent with previous experimental studies. These results underscore the critical role of microscopic variables in capturing solubility behavior in mixed solvent systems. More broadly, this work demonstrates that integrating mechanism-driven descriptors with data-driven ML models offers a powerful and generalizable strategy for accurately predicting drug solubility in complex micro-environments. Solubility ; Support vector machine ; Artificial neural network ; Solvent ; Random forest ; Mixing (physics) Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Animesh Karmakar | Dhananjay Gupta | Tarak Karmakar
Source: ChemRxiv (preprint, 2025-04-16)
Field: Materials Science — Materials Chemistry

Understanding Dynamics of Nanocluster-Organic Frameworks and Gas Diffusion from Machine Learning Potential-based Simulations Nanocluster-organic frameworks (NOFs) have emerged as unique materials with broad applications in sensing, photocatalysis, and optoelectronics. These photofunctional materials have an excellent luminescence switching response to gases such as oxygen (\ce{O2}) and volatile organic compounds. However, the atomistic structural evolution of the NOFs and the mechanism governing small molecule diffusion inside them remain poorly understood. In this study, we developed machine learning potentials to accurately model an experimentally synthesized NOF, \ce{[Ag12(S^{t}Bu)8(CF3COO)4(bpy)4)]_n}, and investigated its structural and dynamic properties using molecular dynamics simulations. Furthermore, we used on-the-fly probability-enhanced sampling (OPES) simulations to capture the pore-to-pore transitions of O$_2$ gas in the NOF and construct the underlying free energy surface (FES). Our results reveal that the \ce{O2} gas predominantly localizes around the bipyridine (bpy) linker, consistent with previous experimental observations. In particular, the nominal pore-to-pore diffusion barrier of $\sim$16-18 kJ/mol for the \ce{O2} transition suggests its feasible diffusion at room temperature. This work presents the first-ever integrated approach for modelling a fully flexible NOF and gas diffusion within it with DFT-level accuracy. Our findings lay the foundation for future investigations on NOFs for potential energy storage, catalysis, and biosensing applications. Diffusion ; Dynamics (music) ; Chemical physics ; Computer science ; Statistical physics ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Ishneet Kaur | Alejandro Forigua | Hatem M. Titi | Yonatan Morocz | David Juncker | Christopher Moraes | Audrey Moores
Source: ChemRxiv (preprint, 2025-08-26)
Field: Materials Science — Biomaterials

Pure chitosan microfluidic spinning affords modular core-sheath fibers and hand-crafted 3D scaffolds with enhanced fibroblasts compatibility Microfluidic spun hydrogel fibers are appealing for tissue engineering and cell transplantation applications because they can feature hierarchical organization, have the ability to be woven or self-assembled into macro-objects, and can be easily functionalized or used for cell or chemical encapsulation. They have been developed as templates for reconstructing fiber-shaped tissues and mimic blood vessels, muscle fibers or neural networks in vivo. Alginates are overwhelmingly employed its core material to fabricate continuous hydrogel microfiber substrates, because of the simplicity of their processing. They, however, suffer from poor cell adherence and weak cell-matrix interactions. Alginates also need crosslinking, and thus easily lose shape and leach ions in the physiological environment. To overcome these challenges, we are reporting herein the first synthesis of pure chitosan fibers by microfluidics, avoiding the use of any crosslinking agent. These fibers have smooth surfaces and excellent cell viability of 85%, in the absence of any special coating, and a comparatively higher mechanical strength (695 MPa) than known alginate ones (2-4MPa). Our system also demonstrated the synthesis of chitin nanocrystals/chitosan composite microfibers with a smooth surface and crystals visible in the cross-section. Chitosan is a natural biopolymer obtained from the deacetylation of chitin, which is found in crustacean shells, insect cuticles, and fungi, and thus represents a sustainable source. Therefore, working with chitosan-derived materials helps us to address United Nations Sustainable Development Goals (UN SDGs) 6 and 14. The fibers of pure chitosan and chitosan composite exhibit high processability and can be woven into a variety of structures. These micro structured chitosan fibers have the potential to be used as templates to create fiber-shaped tissues or to develop into live building blocks for the assembly of very complex artificial tissues. Compatibility (geochemistry) ; Modular design ; Microfluidics ; Chitosan ; Spinning ; Core (optical fiber) ; Materials science ; Nanotechnology ; Computer science ; Composite material ; Chemical engineering ; Engineering Electrospun Nanofibers in Biomedical Applications
Authors: Satya Kokonda
Source:  (article, 2025-09-10)
Field: Chemistry — Inorganic Chemistry

Machine Learning for Designing ‘Undesignable’ Metal-Organic Frameworks Many crucial processes are too complex for computational modeling, requiring experimentation to identify promising materials. Here, a general methodology for application specific material design is presented, while photocatalysis is presented as a specific case-study. Metal-Organic Frameworks (MOFs) are a subset of highly promising porous nanomaterials, used in a variety of ’unmodellable’ applications. Reinforcement learning generated 60,000 novel MOFs optimized for CO/H20 selectivity. A predictor funnel system was created, iteratively removing low-scoring MOFs to 10,986 potential candidates, improving computational efficiency by 276%. While trained Crystal Graph Convolutional Neural Network (CGCNN) models predicted features for creating a fitness function incorporating stability, catalytic ability, material cost, sustainability, and adsorption while allowing the inclusion of application specific design criterion. This designed function provides a computational method to model photocatalytic performance- and filtered down to two promising MOFs which each pass a myriad of synthesis criteria, first a Cr-based MOF with photocatalyst score 230% higher than the control. Second, a Zn-based MOF outperforms the best control across all relevant metrics, demonstrating robustness against variable fitness functions. This work designed 20 materials, each 125% better than the control for this application. Furthermore, analysis revealed insightful design patterns, such as the significant influence of metal cluster N262 on catalytic performance, providing a method for future work to narrow the chemical space. By incorporating industrially applicable features such as cost or stability of the material, this work successfully designs industrially promising materials in otherwise unmodellable processes such as drug delivery, while paving a method for multi-objective optimization incorporating 260% more features than prior work. Robustness (evolution) ; Reinforcement learning ; Artificial neural network ; Convolutional neural network ; Fitness function ; Computational model ; Cluster analysis ; Graph Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Yuki Inada | M. Fujioka | Haruhiko Morito | Tohru Sugahara | Hisanori Yamane | Yukari Katsura
Source: Institutional Repositories DataBase (IRDB) (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Elemental Reactivity Maps for Materials Discovery When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials discovery. The essential issue for such prediction is the uncertainty of whether the unreported combinations are nonreactive or not just investigated, though the reactive combinations can be easily collected as positive data sets from the materials databases. To construct the negative data sets, we developed a process to select reliable nonreactive combinations by evaluating the similarity between unreported and reactive combinations. The machine learning models were trained by both data sets, and the prediction results were visualized by two-dimensional heatmaps: elemental reactivity maps to identify elemental combinations with high reactivity but no reported stable compounds. The maps predicted high reactivity (i.e., synthesizability) for the Co–Al–Ge ternary system, and two novel ternary compounds were synthesized: Co4Ge3.19Al0.81 and Co2Al1.26Ge1.74. Reactivity (psychology) ; Ternary operation ; Limiting ; Elemental analysis ; Similarity (geometry) ; Process (computing) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Catalysis and Oxidation Reactions
Authors: Mengxin Yang (10684155) | Shiyu Wang (813972) | Guanli Song (5439419) | Longhui Cheng (16511486) | Haisheng Ren (1609681)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

CBH-BDC Enhanced Δ‑ML for Predictingthe Accurate Standard Enthalpy of Formation The standard enthalpy of formation (Δ<sub>f</sub><i>H</i>°) is a fundamental thermodynamic property that is essential for understanding various physicochemical processes. Our group recently developed the connectivity-based hierarchy with the bond difference correction (CBH–BDC) method for calculating the accurate Δ<sub>f</sub><i>H</i>°. However, it encounters challenges in high-accuracy electron energy calculations and is restricted by BDC parameters that are limited to specific elements. In this work, we introduce a CBH–BDC enhanced delta machine learning (Δ-ML) approach that utilizes effective and interpretable molecular descriptors derived from connection-based hierarchy fragments and BDC, enabling the accurate prediction of Δ<sub>f</sub><i>H</i>° while bypassing high-level quantum calculations. The approach is validated using 464 species with experimental Δ<sub>f</sub><i>H</i>° and applied to extrapolate Δ<sub>f</sub><i>H</i>° from density functional theory (DFT) accuracy to CCSD(T) accuracy for 120,416 stable organic molecules in the QM9 database. It demonstrates significant improvements in accuracy, enabling the construction of a high-quality Δ<sub>f</sub><i>H</i>° database for chemical deep learning. Enthalpy ; Quantum chemical ; Hierarchy ; Property (philosophy) ; Density functional theory ; Energy (signal processing) ; Molecule ; Quantum Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Juan Miguel Álvarez Domínguez | Mads-Peter Verner Christiansen | Konstantin M. Neyman | Barbara Hammer | Albert Bruix
Source: ArXiv.org (preprint, 2025-09-24)
Field: Materials Science — Materials Chemistry

Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials The characterization of nanostructued materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformations. Global optimization approaches allow predicting stable structures for targeted materials but addressing the configurational and compositional search spaces is both computationally demanding and inefficient, especially when first principles calculations are required. In this work, we implement and evaluate a computationally efficient grand canonical global optimization algorithm able to identify stable structures and chemical states of targeted systems under given reaction conditions (e.g. reactant pressure and temperature). The algorithm leverages an on-the-fly trained machine-learning interatomic potential based on sparse Gaussian Process Regression and the smooth overlap of atomic positions descriptor to reduce the number of first principles energy evaluations carried out during global optimization searches. The \textit{ab initio} thermodynamics framework is incorporated to approximate the Gibbs energy of evaluated candidates, performing environment-aware optimizations over multiple stoichiometries. We demonstrate the computational performance of this approach and its ability to reproduce some literature examples. Interatomic potential ; Global optimization ; Gaussian ; Gaussian process ; Process (computing) ; Molecular dynamics ; Optimization problem ; Energy (signal processing) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Catalysis and Oxidation Reactions
Authors: Zohra Hamiani | Mohammed Beldjilali | Amina Berrichi | Abdelkader Ech-Chergui Nebatti | Ridha Hassaine | Nihel Dib | Ginesa Blanco | Redouane Bachir
Source: Research on Chemical Intermediates (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Artificial intelligence driven design of FeAg(PO4)2 nanocatalyst for benzimidazole synthesis and L-cysteine detection Mesoporous material ; Bimetallic strip ; Catalysis ; Benzimidazole ; Specific surface area ; Fluorescence ; Quenching (fluorescence) ; Raman spectroscopy ; Band gap Advanced Nanomaterials in Catalysis ; Multicomponent Synthesis of Heterocycles ; Electrochemical sensors and biosensors
Authors: Patrick T. Gemperline | Rajendra Paudel | Rama K. Vasudevan | R. Comès
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2025-03-18)
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 LaFeO3 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 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 oscillations 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 LaFeO3 samples on Nb-doped SrTiO3 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 LaFeO3 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 ; Electron diffraction ; Data analysis ; Computer science ; Diffraction ; Data science ; Energy (signal processing) ; Learning analytics ; Optics ; Physics ; Data mining Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Corin Wagen
Source: ChemRxiv (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning Enables Rapid Macroscopic pKa Prediction Accurate prediction of macroscopic pKa values remains a central challenge in computational chemistry, critical for modeling pH-dependent properties like solubility, membrane permeability, and charge state. Here we introduce Starling, a physics-informed neural network based on the Uni-pKa architecture trained to predict per-microstate free energies and compute macroscopic pKavalues via thermodynamic ensemble modeling. Unlike approaches that treat protonation events in isolation, Starling explicitly resolves protonation and tautomeric microstates, enabling robust handling of complex molecules with multiple ionizable sites. We show that Starling achieves comparable or superior accuracy to leading commercial tools on multiple benchmark datasets, and demonstrate its utility in predicting isoelectric points, logD profiles, and blood–brain-barrier permeability. By maintaining thermodynamic consistency and enabling rapid microstate-ensemble generation, Starling enables accurate physicochemical property prediction with broad relevance to drug discovery and molecular design. Physics ; Machine learning ; Artificial intelligence ; Data science ; Statistical physics ; Computer science Machine Learning in Materials Science
Authors: Hossein Najafzadeh | Zahra Raissi | Shole Golmohammady | Parivash Safari Kaji | Mahdad Esmaeili
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform This study aimed to develop and evaluate deep learning approaches for the classification of quantum emission signals from WS2 monolayer nanobubbles across multiple spectral bands, addressing challenges in quantum materials characterization and spectral distinguishability assessment. We utilized a dataset of quantum emission signals ranging from 604 to 629 nm, emitted from WS₂ monolayer nanobubbles on gold substrates, categorized into four spectral bands (604.06–608.24 nm, 611.07–616.34 nm, 617.42–623.35 nm, and 624.16–636.57 nm). Our methodology involved signal preprocessing through normalization and moving average smoothing, followed by transformation into 128 × 128 RGB images using Continuous Wavelet Transform (CWT) with Complex Morlet wavelet. Three convolutional neural network architectures (ResNet50, VGG16, and Xception) were implemented and evaluated using fivefold cross-validation across six possible band pair combinations. All models demonstrated exceptional classification performance, with VGG16 achieving the highest overall mean accuracy of 99.4%, followed by Xception (99.1%) and ResNet50 (98.2%). Perfect classification accuracy (100%) was consistently achieved for spectrally distant band pairs, particularly Band 1 versus Band 4 (20.5 nm separation), while the most challenging classification involved adjacent bands (Band 2 vs. Band 3, 6.27 nm separation) with VGG16 achieving 96.5% accuracy. Statistical analysis using Friedman tests confirmed significant performance differences among models (χ2 = 8.67, p = 0.013). Xception demonstrated remarkable computational efficiency, achieving optimal convergence in as few as 2 epochs for certain band combinations while maintaining ultralow training loss values (8.23 × 10⁻6). Deep learning models, particularly when combined with CWT preprocessing, provide a robust framework for quantum emission signal classification with significant implications for quantum photonics, quantum cryptography, and quantum sensing applications. Our approach bridges the gap between classical machine learning and quantum materials characterization, establishing quantifiable metrics for evaluating spectral distinguishability in quantum information systems. The demonstrated ability to achieve high classification accuracy with minimal training through transfer learning addresses data scarcity challenges inherent to quantum systems, offering a promising direction for future quantum technology development. Deep learning ; Pattern recognition (psychology) ; Convolutional neural network ; Quantum ; Wavelet ; Transformation (genetics) ; Normalization (sociology) ; RGB color model 2D Materials and Applications ; Mechanical and Optical Resonators ; Advanced Chemical Sensor Technologies