jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniyar Mazitov | Timur Gimadiev | A. Poyezzhayeva | Valentina A. Afonina | Timur Madzhidov
Source: Molecules (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions Reaction conditions (RCs) are a crucial part of reaction definition, and their accurate prediction is an important component of chemical synthesis planning. The existence of multiple combinations of RCs capable of achieving the desired result complicates the task of condition recommendation. Herein, we propose a conditional variational autoencoder (CVAE) generative model to predict suitable RCs. The CVAE model has been customized to generate diverse sets of valid conditions, ensuring high flexibility and accuracy, while circumventing the necessity for enumeration or combinatorial search of potential RCs. The efficacy of the CVAE approaches was evaluated using hydrogenation reactions and other H<sub>2</sub>-mediated reactions, predicting the set of catalysts, additives (acid, base, and catalytic poison), ranges of temperature, and pressure. The CVAE models predicted conditions with different "heads", each corresponding to specific condition components, and their respective losses. CVAE models were tested on two datasets: a small one containing 31K reactions with 2232 potential conditions' combinations and a big one having 196K reactions with ~7 × 10<sup>42</sup> potential conditions' combinations to evaluate the model's ability to predict varying complexity and diversity conditions. To optimize the accuracy of the models, we experimented with three latent distribution variants-Gaussian (g-CVAE), Riemannian Normalizing Flow (rnf-CVAE), and Hyperspherical Uniform (h-CVAE). In our experiments, the h-CVAE model demonstrated robust overall performance, making it the optimal choice for scenarios requiring high accuracy across multiple top-k predictions. Benchmarking analyses demonstrated the high performance of the CVAE models compared to state-of-the-art reaction condition prediction approaches. Autoencoder ; Set (abstract data type) ; Flexibility (engineering) ; Benchmarking ; Conditional probability ; Generative model ; Benchmark (surveying) ; Probability distribution ; Component (thermodynamics) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ekström Kelvinius, Filip | Andersson, Oskar | Parackal, Abhijith S. | Qian, Dong | Armiento, Rickard | Lindsten, Fredrik
Source: DiVA (Linkoping University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

WyckoffDiff– A Generative Diffusion Model for Crystal Symmetry Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its position or element. We propose a generative model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based descriptions of crystals. This is enabled by considering a crystal structure representation that encodes all symmetry, and we design a novel neural network architecture which enables using this representation inside a discrete generative model framework. In addition to respecting symmetry by construction, the discrete nature of our model enables fast generation. We additionally present a new metric, FrÃ©chet Wrenformer Distance, which captures the symmetry aspects of the materials generated, and we benchmark WyckoffDiff against recently proposed generative models for crystal generation. As a proof-of-concept study, we use WyckoffDiff to find new materials below the convex hull of thermodynamical stability. Symmetry (geometry) ; Generative model ; Generative grammar ; Representation (politics) ; Crystal (programming language) ; Position (finance) ; Benchmark (surveying) ; Discrete symmetry Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; Model Reduction and Neural Networks
Authors: Saba Farooq | Zainab Ngaini
Source: Discover Chemistry. (article, 2025-09-29)
Field: Chemistry — Organic Chemistry

Two pot and one pot synthesis of dipyrazolopyridines derivatives and applications Dipyrazolopyridines (DPPs) are distinctive heterocyclic compounds with broad applications in pharmaceutical and industrial fields. DPPs can be conveniently synthesised using commonly available reagents such as ethyl acetoacetate, hydrazine derivatives, ammonium hydroxide, and aldehyde derivatives. This review article explores the synthesis of DPP derivatives, highlighting various synthetic methods. Two-pot and one-pot synthesis of DPPs and their extensive applications in pharmaceuticals, material synthesis and environmental chemistry have also been comprehensively discussed. The current challenges and prospects of DPPs in various fields have also been emphasised due to the potential technological advancements and demand. One-pot synthesis ; Hydrazine (antidepressant) ; Aldehyde ; Chemical synthesis ; Organic synthesis Multicomponent Synthesis of Heterocycles ; Synthesis and Biological Evaluation ; Synthesis and biological activity
Authors: Ahmed Ahmed | Andrey A. Bezrukov | Debobroto Sensharma | Ciarán O’Malley | Michael J. Zaworotko | Davide Tiana | Constantina Papatriantafyllopoulou
Source: Molecules (article, 2025-04-29)
Field: Chemistry — Inorganic Chemistry

Synthesis and Characterisation of Multivariate Metal–Organic Frameworks for Controlled Doxorubicin Absorption and Release The development of drug carriers with efficient absorption and controlled delivery properties is crucial for advancing medical treatments. Metal-organic frameworks (MOFs) with tunable porosity and a large surface area represent a promising class of materials for this application. Among them, <b>NUIG4</b> stands out as a biocompatible MOF that exhibits exceptionally high doxorubicin (Dox) absorption (1995 mg dox/g <b>NUIG4</b>) and pH-controlled release properties. In this study, we report the synthesis and characterisation of multivariate MOFs (<b>MV-NUIG4</b>), which are analogues of <b>NUIG4</b> that maintain the same topology while incorporating different functional groups within their framework. Eight new <b>MV-NUIG4</b> MOFs have been synthesised through in situ reactions of the corresponding 4-aminobenzoic acid derivative with 4-formylbenzoic acid. The compounds were thoroughly characterised using a range of techniques, including powder X-ray diffraction, infrared spectroscopy, <sup>1</sup>H-NMR, and single-crystal X-ray crystallography. The experimental ratio of the reagents and ligand precursors for the synthesis of <b>MV-NUIG4</b> MOFs matched the ratio of the linkers in the final products. These structures incorporate additional functional groups, such as methyl and hydroxyl, in varying ratios. Computational modelling was used to provide further insight into the crystal structure of the MOFs, revealing a random distribution of the functional groups in the framework. The Dox absorption and release capacity of all analogues were studied, and the results revealed that all analogues displayed high drug absorption in the range of 1234-1995 mg Dox/g MOF. Furthermore, the absorption and release rates of the drug are modulated by the ratio of functional groups, providing a promising approach for controlling drug delivery properties in MOFs. Doxorubicin ; Absorption (acoustics) ; Multivariate statistics ; Chemistry ; Metal ; Metal-organic framework ; Materials science ; Computer science ; Organic chemistry ; Medicine ; Internal medicine ; Adsorption ; Chemotherapy ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; X-ray Diffraction in Crystallography
Authors: Hyunho Lee | Jei‐Pil Wang
Source: International Journal of Innovative Research and Scientific Studies (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Design and implementation of a fire-responsive cooling–suppression integrated system for mitigating fire risks in data-center GPU servers This study addresses rising fire risk and thermal stress in GPU-accelerated data-center servers by proposing a power-agnostic, self-actuating safety architecture. We integrate thermo-responsive, rupture-on-heat suppression capsules with a direction-steerable rotary cooling module and evaluate the hybrid on a 2U RTX A6000 server under realistic rack conditions. Instrumentation includes type-T thermocouples, high-speed videography, and infrared thermography. Blackout trials verify actuation without external power, sensors, or controllers. The steerable cooling reduced average GPU temperature by ΔT ≈ 9.2 °C (≈11.2%) and lowered fan power from 13 W to ~9 W (≈30.7%). Under a 200 ± 10 °C surrogate heat input, capsules discharged within ≤0.45 s, dispersing a non-conductive, non-corrosive clean agent over ≥25 cm radius; no electrical or corrosive damage was observed on proximal components. A heat-triggered, power-independent cooling–suppression hybrid can mitigate incipient fire risk while improving thermal and energy performance, removing the single-point-of-failure inherent to electrically actuated systems. The modular design and passive actuation suit unmanned or power-unstable environments (edge nodes, defense/finance servers), enable straightforward retrofit to existing chassis, and support compliance-oriented safety upgrades without dependence on facility power continuity. Modular design ; Blackout ; Server ; Rack ; Firefighting ; Power (physics) ; Thermal management of electronic devices and systems ; Water cooling ; Upgrade Thermal properties of materials ; Heat Transfer and Optimization ; Nuclear Engineering Thermal-Hydraulics
Authors: Xinru Gao | Meng Han | Changan Wang | Jinzhang Tao | Hongyi Gao
Source: Catalysts (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Rational Design of Ce–Ni Bimetallic MOF-Derived Nanocatalysts for Enhanced Hydrogenation of Dicyclopentadiene The development of highly efficient catalysts for the hydrogenation of dicyclopentadiene (DCPD) remains a critical challenge. In this study, we designed a series of bimetallic Ce–Ni metal–organic framework (MOF)-derived nanocatalysts by precisely tuning the Ce/Ni ratio and calcination temperatures. The optimized catalyst, Ni–CeO2(7:3) @C–400 °C, featuring highly dispersed carbon-coated Ni nanoparticles, achieved complete hydrogenation of DCPD to tetrahydrodicyclopentadiene (THDCPD) with 100% conversion and nearly 100% selectivity within 2 h under 100 °C and 2 MPa. The porous carbon framework significantly facilitated the diffusion and accessibility of DCPD molecules, combined with Ce species reconstructing the electronic structure of Ni active centers through electronic interactions, synergistically enhancing the hydrogenation efficiency. Furthermore, the catalyst demonstrated good structural stability. This work not only provides a robust strategy for the rational design of bimetallic MOF-derived catalysts but also highlights their potential for practical applications in industrial hydrogenation processes. Dicyclopentadiene ; Bimetallic strip ; Nanomaterial-based catalyst ; Bimetal ; Materials science ; Metallurgy ; Metal ; Composite material Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Aurélien Personnaz | Sihyun Sung | Raphaël Bourgeas | Sruthi Sunni | Florine Dupeux | Bukunmi Adediran | Rosicler L. Barbosa | Anne Sophie Humm | Euan Colaco-Osorio | José Antonio Márquez
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

AXIS: A Lab-in-the-Loop Machine Learning approach for generalized detection of macromolecular crystals Abstract Macromolecular crystallography provides mechanistic understanding of biological processes and can be applied in drug design. Nowadays, the use of robotic systems for crystal growth and diffraction analysis is widespread and high throughput protein-to-structure pipelines for ligand and fragment screening are revolutionizing the field. However, the identification of crystals is still largely carried out through manual inspection, sometimes involving tens of thousands of images, which represents a bottleneck in an otherwise highly automated process. Here we describe AXIS , an A I-based C rystal I dentification S ystem combining the DINOv2 computer vision model, state-of-the-art transfer learning and MARCO, the largest crystallization dataset available to date, for automated crystal detection. AXIS can operate both with visible and UV light images and integrates a Lab-In-The-Loop approach combining ML and expert inputs for continuous learning and specialization. AXIS enables automated annotation of large crystallization image datasets with performance and accuracy comparable to that of human experts and the Lab-In-The-Loop approach introduced here enables efficient adaptation to local conditions facilitating widespread application, which has been a major limitation to date. AXIS can help correct human errors in image annotation and removes critical bottlenecks, particularly in the context of extensive crystallization screens or high throughput applications like fragment and ligand screening unlocking the potential for higher levels of automation that are key both in fundamental and translational research. Appendix A. Graphical Abstract Bottleneck ; Automation ; Context (archaeology) ; Throughput ; Annotation ; Identification (biology) ; Protein crystallization Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Surya Kumari Joshi | Dinesh Kumar Chaudhary | Md Al Mamun | S. Manjura Hoque | Surya Prasad Adhikari | Rabindra Prasad Dhakal | Rameshwar Adhikari
Source: Journal of Nepal Chemical Society (article, 2025-08-18)
Field: Chemistry — Analytical Chemistry

Boehmite Nanoparticle-enhanced Jatropha Oil Nanofluid: Synthesis, Stability, and Viscosity Analysis The jatropha nanofluid (JNF) was prepared by blending jatropha oil (JO) with 0.1 wt.-% boehmite nanoparticles (B-NPs) and was subsequently characterized for its chemical composition, nanostructural stability, and flow properties using Fourier-transform infrared (FTIR) spectroscopy, zeta potential, and sedimentation tests, and a type B Ostwald viscometer, respectively. X-ray diffraction (XRD) analysis revealed the polycrystalline nature of boehmite with a crystallite size of 8.19 ± 0.57 nm, while Transmission electron microscopy (TEM) showed its platelet-like morphology. With a zeta potential value of 27.3 mV, the nanofluid was suggested to be moderately stable. The addition of B-NPs led to an increase in the kinematic viscosity of jatropha oil, attributed to the enhanced internal resistance caused by suspended nanoparticles. These nanoparticles alter the microstructure of the fluid and influence the formation of interaction layers and particle clusters. The observed enhancement in viscosity suggests that the B-NPs-modified jatropha oil holds potential as an environmentally friendly biolubricant or hydraulic fluid for mechanical applications. Nanofluid ; Boehmite ; Viscosity ; Nanoparticle ; Materials science ; Chemical engineering ; Jatropha ; Nanotechnology ; Composite material ; Chemistry ; Biodiesel ; Organic chemistry ; Engineering ; Catalysis Petroleum Processing and Analysis ; Biodiesel Production and Applications ; Nanofluid Flow and Heat Transfer
Authors: Jan Krupiński | Kazimierz Kiełkowicz
Source: Annals of Computer Science and Information Systems (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Detecting Spatial Ordering of Nanoparticles with Geometric Deep Learning Nanoparticle dispersion in heterogeneous catalysts plays a critical role in catalytic performance.We propose a robust and generalizable graph neural network (GNN) approach that combines the edge convolutional operator (EdgeConv) with a graph attention (GAT) layer to classify dispersion patterns in palladium on carbon (Pd/C) catalysts.Our method leverages GNNs to operate directly on particle location data extracted from scanning electron microscopy (SEM) images, thereby avoiding reliance on image features that may introduce bias.The proposed method offers an advantage over traditional image-based approaches, which risk overfitting to visual characteristics of the image that are unrelated to the spatial distribution of the nanoparticles.We validate the performance of our GNN architecture on multiple Pd/C samples with distinct carbon support types, achieving an accuracy of 89.84%, and demonstrate that our approach can reliably identify dispersion defects.The results highlight the potential of GNNs as a promising alternative for structure-based analysis and quality assessment of nanomaterialbased catalysts. Deep learning ; Pattern recognition (psychology) ; Nanoparticle ; Artificial neural network ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; X-ray Diffraction in Crystallography
Authors: Nilam A.Nikam Nilam A.Nikam | Sandhya P. Kadam Sandhya P. Kadam | Vivek Redasani
Source: International Journal of Pharmaceutical Research and Applications (review, 2025-03-01)
Field: Chemistry — Spectroscopy

A Comprehensive Review on Gas chromatography Gas chromatography is the general term for a range of analytical separation techniques used to analyze volatiles in the gas phase. Gas chromatography separates the analytes by separating the sample into two phases, the stationary phase and the mobile phase, by dissolving the sample components in a solvent and evaporating them. The mobile phase is chemically inertGas, which transports the analyte molecules through the heated column.Gas Chromatography is one of the only chromatography's that interacts with analytes without the use ofMobile phases. The stationary phase is either a solid adsorbent, calledGas-Solid Chromatography (GSC), or a liquid on an inert support, called GasLiquidChromatography (GLC). Gas chromatography is an instrumental technique used forensically in drug analysis, arson, and toxicology analysis of other organic compounds. Gas chromatography ; Chromatography ; Chemistry Analytical Chemistry and Chromatography
Authors: Ana Zeković
Source: Computation (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Mathematical Optimization in Machine Learning for Computational Chemistry Machine learning (ML) is transforming computational chemistry by accelerating molecular simulations, property prediction, and inverse design. Central to this transformation is mathematical optimization, which underpins nearly every stage of model development, from training neural networks and tuning hyperparameters to navigating chemical space for molecular discovery. This review presents a structured overview of optimization techniques used in ML for computational chemistry, including gradient-based methods (e.g., SGD and Adam), probabilistic approaches (e.g., Monte Carlo sampling and Bayesian optimization), and spectral methods. We classify optimization targets into model parameter optimization, hyperparameter selection, and molecular optimization and analyze their application across supervised, unsupervised, and reinforcement learning frameworks. Additionally, we examine key challenges such as data scarcity, limited generalization, and computational cost, outlining how mathematical strategies like active learning, meta-learning, and hybrid physics-informed models can address these issues. By bridging optimization methodology with domain-specific challenges, this review highlights how tailored optimization strategies enhance the accuracy, efficiency, and scalability of ML models in computational chemistry. Computer science ; Machine learning ; Artificial intelligence ; Hyperparameter ; Bayesian optimization ; Reinforcement learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Abubakar Siddique | Muhammad Masood Khan | Waseem Aslam | Muhammad Usama Shahid | Khaled Saleem S. Alatawi | Mohammad R. Altimania | Євген Зайцев | Hafiz Mudassir Munir
Source: Energy Science & Engineering (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

Influence of Nanoparticles on Dielectric Properties of High‐Voltage Epoxy‐Based Nanocomposite Insulators: Simulation and Experimental Analysis ABSTRACT There is a need to develop an improved insulating material that surpasses traditional insulators in terms of insulation and efficient operation under extreme weather conditions. Epoxy nanocomposites (NCs) have gained considerable attention due to their intrinsic material properties. This article focuses on investigating the impact of various nanoparticles, including TiO 2 , SiO 2 , Fe 3 O 4 , MgO, and Al 2 O 3 , when incorporated into neat epoxy resins (ERs) at different concentrations. To assess their suitability for practical use, the electrical and physical properties of these materials, such as AC breakdown voltage (BDV), relative permittivity, and thermal conductivity, are examined. To evaluate the insulating properties of the proposed nano‐filled solids, simulations are performed using COMSOL Multiphysics software. For the result validation, the performance of these nano‐filled base epoxy insulators is compared with conventional porcelain (sand, clay, kaolin, and feldspar) insulators as well as with neat ER insulators. The research also includes a practical synthesis of five types of NCs using (TiO 2 , MgO, Fe 3 O 4 , SiO 2 , and Al 2 O 3 ) and porcelain. The development of nano‐filled solids using neat ER takes place in the laboratory of Khwaja Fareed University in Pakistan. Several tests have been conducted in the lab to authenticate the usefulness of the proposed nano‐filled materials; in addition, a comparison has been made between simulation results and experimental findings. The overall results indicate that the proposed nano‐filled materials with a concentration of 0.002% w/v of titania (TiO 2 ) exhibit superior insulating properties. This makes them a viable alternative for insulating high‐voltage equipment. Multiphysics ; Epoxy ; Nanocomposite ; Dielectric ; Nanoparticle ; Thermal ; Voltage High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties