jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joakim Brorsson | Henrik Klein Moberg | Joel Hildingsson | J. Gastaldi | Tobias Mattisson | Anders Hellman
Source: ArXiv.org (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Material exploration through active learning -- METAL The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to the inherent problem of navigating vast compositional spaces. Thanks to the emergence of machine learning, however, suitable tools are now readily available. Here, the task of finding oxygen carriers for chemical looping processes has been tackled by leveraging active learning-based strategies combined with first-principles calculations. High efficiency and efficacy have, moreover, been achieved by exploiting the power of recently developed machine learning interatomic potentials. Firstly, the proposed approaches were validated based on an established computational framework for identifying high entropy perovskites that can be used in chemical looping air separation and dry reforming. Chief among the insights thus gained was the identification of the best performing strategies, in the form of greedy or Thompson-based sampling based on uncertainty estimates obtained from Gaussian processes. Building on this newfound knowledge, the concept was applied to a more complex problem, namely the discovery of high entropy oxygen carriers for chemical looping oxygen uncoupling. This resulted in both qualitative as well as quantitative outcomes, including lists of specific materials with high oxygen transfer capacities and configurational entropies. Specifically, the best candidates were based on the known oxygen carrier CaMnO3 but also contained a variety of additional species, of which some, e.g., Ti; Co; Cu; and Ti, were expected while others were not, e.g., Y and Sm. The results suggest that adopting active learning approaches is critical in materials discovery, given that these methods are already shifting research practice and soon will be the norm. Entropy (arrow of time) ; Oxygen ; Turbocharger ; Task (project management) ; Variety (cybernetics) ; Gaussian ; Cross-entropy method Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; High Entropy Alloys Studies
Authors: Daniel Hedman
Source: Research Square (preprint, 2026-03-09)
Field: Materials Science — Materials Chemistry

Vapor-solid-solid growth of single-walled carbon nanotubes Carbon nanotube ; Surface diffusion ; Carbon fibers ; Catalysis ; Chirality (physics) ; Diffusion ; Chemical vapor deposition ; Nanoparticle ; Molecular dynamics Carbon Nanotubes in Composites ; Graphene research and applications ; Machine Learning in Materials Science
Authors: J. H. Ding | Yunhui Meng | Lijia Zhang | Xiaobai Li | Mengyao Pan | Wenqi Dong | Kun Song | Yongpeng Liu | Shilong Yang | Hongwei Ma
Source: Analytical Chemistry (article, 2026-02-13)
Field: Chemistry — Spectroscopy

Machine Learning-Assisted Detection of Phosgene and Acetyl Chloride via a Dual-Probe Fluorescent Platform with Differential Reactivity Phosgene and acyl chlorides are highly toxic chemicals that pose serious threats to human health and environmental safety, yet their rapid and reliable detection remains a major challenge due to their high reactivity and environmental interferences. Herein, we report the rational design and synthesis of two donor-π-acceptor (D-π-A) fluorescent probes, <b>TPA-APPA</b> and <b>TPA-HPO</b>, which incorporate excited-state intramolecular proton transfer and hybridized localized and charge-transfer characteristics. These probes exhibit fast, highly selective, and sensitive responses toward phosgene, with <b>TPA-APPA</b> showing distinct fluorescence enhancement and colorimetric changes for both phosgene and acetyl chloride at low detection limits. Benefiting from its excellent photostability, <b>TPA-APPA</b> was successfully applied to bioimaging in live cells and zebrafish. Furthermore, by integrating machine learning algorithms, including convolutional neural networks and the Swin Transformer, we developed a ceramic-fiber detection strip capable of intelligent recognition and classification of fluorescence signal variations. This hybrid system achieved high classification accuracy for toxicant vapors, demonstrating the strong potential of coupling advanced fluorescent probes with machine learning for next-generation toxic gas monitoring and bioimaging applications. Phosgene ; Fluorescence ; Toxicant ; Reactivity (psychology) ; Chloride ; Intramolecular force Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Sulfur Compounds in Biology
Authors: Dorota Glosz | Aleksandra Żabińska | Martyna Dyczkowska | Katarzyna Jędrzejowska | Jędrzej Kobylarczyk | Monika Srebro-Hooper | James G.M. Hooper | Robert Podgajny
Source: Coordination Chemistry Reviews (article, 2026-02-14)
Field: Chemistry — Physical and Theoretical Chemistry

Anion···π interactions and hydrogen bonds in cyanidometallate-based co-crystal systems: novel synthons, functional properties, and stories behind them Synthon ; Hydrogen bond ; Intermolecular force ; Molecular recognition ; Crystal engineering ; Reactivity (psychology) ; Characterization (materials science) ; Non-covalent interactions ; Density functional theory Crystallography and molecular interactions ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Y. Mizoi | M. Mihara | Soshi Ishitani | K M Kojima | Makoto Fukushima | Miki Fukutome | Ryunosuke Imai | Shungo Ide | Masayoshi Kamon | Gen Takayama | Akira Sato | S. Shimizu | Ryo Taguchi | K. Yasuda
Source: Interactions (article, 2026-02-26)
Field: Chemistry — Spectroscopy

Enhancement of spin polarized RI and muon beam imaging via machine learning techniques Image resolution ; Spins ; Deep learning ; Spin (aerodynamics) ; Iterative reconstruction ; Medical imaging ; Quantum ; Pattern recognition (psychology) Advanced NMR Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications ; Computational Physics and Python Applications
Authors: Yuan Jiang | Jinshan Li | Tinghuan Yuan | Jun Wang | Bin Tang | Xinping Mao | Gang Li | Ruihao Yuan
Source: Journal of Materials Informatics (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Uncertainty estimation affects predictor selection and its calibration improves materials optimization Uncertainty is crucial when the available data for building a predictor are insufficient, which is ubiquitous in machine-learning-driven materials studies. However, the impact of uncertainty estimation on predictor selection and materials optimization remains incompletely understood. Here, we demonstrate that in active learning, uncertainty estimation significantly influences predictor selection, as well as that the calibration of uncertainty estimation can improve the optimization. The idea is validated on three alloy datasets (Ni-based, Fe-based, and Ti-based) using three commonly used algorithms - support vector regression (SVR), neural networks (NN), and extreme gradient boosting (XGBoost) - which yield comparable predictive accuracy. It is shown that XGBoost presents more reliable uncertainty estimation than SVR and NN. Using the directly estimated uncertainty for the three predictors with similar accuracy, we find that the optimization is quite different. This suggests that uncertainty estimation plays a role in predictor selection. The uncertainty estimation is then calibrated to improve reliability, and its effect on optimization is compared with the uncalibrated case. Among the nine cases considered (three models and three datasets), eight show improved optimization when calibrated uncertainty estimation is used. This work suggests that uncertainty estimation and its calibration deserve greater attention in active learning-driven materials discovery. Calibration ; Estimation ; Uncertainty analysis ; Selection (genetic algorithm) ; Boosting (machine learning) ; Uncertainty quantification ; Support vector machine ; Measurement uncertainty Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Alexandre Blanco-González | Thea K. Schulze | Evianne Rovers | Joe G. Greener
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Training a force field for proteins and small molecules from scratch Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural network that assigns all force field parameters for diverse molecules using continuous atom typing. The freely-available model, called Garnet, was trained on quantum mechanical, condensed phase and protein nuclear magnetic resonance data without the use of existing parameters. The resulting force field shows comparable performance to current force fields on small molecules, folded proteins, protein complexes and disordered proteins. It shows similar results to popular approaches for relative binding free energy predictions across a range of targets. Assessing different functional forms shows that the double exponential potential is a flexible and accurate alternative to the Lennard-Jones potential. Garnet provides a platform for automated, reproducible force field discovery that brings the benefits of machine learning to classical force fields. Force field (fiction) ; Transferability ; Limiting ; Magnetic field ; Field (mathematics) ; Range (aeronautics) ; Small molecule ; Scratch ; Quantum Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Sunday Joseph Ogenyi | Yusuf Shaidu | Nicola Seriani | Omololu Akin Ojo
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Efficient development of a neural network potential for pure silica zeolites The use of machine learning interatomic potentials (MLIPs) has increased the size and timeframe of molecular dynamics simulations by offering accuracy comparable to density functional theory (DFT) at a fraction of the computational cost. However, developing MLIPs typically requires large training datasets, which carry substantial computational expense when generated with DFT. In this work we show that, for crystal structures with similar compositions but different topologies, such as pure silica zeolites, a hierarchical clustering strategy can be used to identify a small set of representative polymorphs and, thus, construct an efficient compact dataset <i>via</i> an active learning workflow. Furthermore, this active learning phase was carried out using a non-compute intensive invariant neural network potential. From the <i>ab initio</i> data generated during active learning, we then developed two MLIPs based on invariant and equivariant architectures, respectively. Both neural network potentials achieve energy errors of 3 meV per atom or less on the datasets when compared with <i>ab initio</i> results. In addition, structural, mechanical, thermal, and phonon dispersion properties of the zeolites predicted by the MLIPs exhibit excellent agreement with the corresponding <i>ab initio</i> calculations, demonstrating the high fidelity of the developed models. Artificial neural network ; Invariant (physics) ; Molecular dynamics ; Dispersion (optics) ; Density functional theory ; Phonon ; Atom (system on chip) ; Interatomic potential ; Cluster analysis Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced Physical and Chemical Molecular Interactions
Authors: Hiroaki Matsukawa | Katsuyuki Koizumi | Katsuto Otake
Source: Fluid Phase Equilibria (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Prediction of pure-component parameters of perturbed chain-statistical associating fluid theory equations of state using graph convolutional neural network-based modeling • Developed a GCNN to predict PC-SAFT pure-component parameters • GCNN predicts parameters directly from molecular structure information • Systematic comparison clarifies strengths different molecular representation models • Graph-based representations effectively capture molecular size and shape effects • Developed GCNN architecture guides machine-learning-based parameter prediction Substance-specific parameters are required to perform calculations using equations of state (EoS), which are powerful tools for predicting physical properties. These parameters are typically obtained from experimentally measured physical properties; however, predicting physical properties requires prior knowledge of the same properties, leading to a circular dependency problem. To address this issue, machine learning-based models that predict substance-specific parameters directly from molecular information without relying on measured physical properties were previously proposed. Among the existing approaches, we found that the pure-component parameters of the perturbed chain-statistical associating fluid theory (PC-SAFT) EoS strongly depends on molecular descriptors related to size and shape. Several molecular structure representation-based models have since been developed for machine learning applications. In this study, we develop a graph convolutional neural network (GCNN)-based molecular structure representation method that treats molecules as graph structures to predict the pure-component parameters of the PC-SAFT EoS. The developed GCNN model was compared with other existing molecular structure representation-based models, including previously reported group contribution- and molecular fingerprinting-based models. The GCNN predicted pure-component parameters with estimation accuracy comparable to those of other existing models while accommodating the broadest range of molecular structures. This study thereby expands the applicability of EoS-based property estimation. Representation (politics) ; Molecular graph ; Graph ; Molecular dynamics ; Convolutional neural network ; Range (aeronautics) ; Graph theory ; Property (philosophy) Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Material Dynamics and Properties
Authors: Mengjie Chen | Ao Liang | Yuning Zhang | Xiaolin Xu | Siqi Niu | Dexin Ma
Source: Journal of Spectroscopy (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Research on Maize Variety Identification Based on Hyperspectral Imaging Technology Maize, as a globally vital food and economic crop, requires rapid and accurate variety identification to ensure food security. This study developed an innovative nondestructive discrimination method by integrating hyperspectral imaging (400–1000 nm) with machine learning algorithms. Hyperspectral images of 1200 maize seeds from six varieties were acquired and preprocessed using mean centering (MC) to eliminate baseline drift. Principal component analysis (PCA) and successive projections algorithm (SPA) were employed for feature wavelength selection. Three machine learning algorithms—backpropagation neural network (BP‐NN), random forest (RF), and support vector machine with radial basis function (SVM‐RBF)—were systematically compared. The results demonstrated that the RF model with PCA feature selection (MC‐PCA‐RF) achieved optimal performance, with a test set accuracy of 92.33% ( p &lt; 0.05), significantly outperforming traditional approaches such as manual inspection, chemical analysis, and molecular markers. This study proposes a practical integration of existing preprocessing and feature extraction methods, forming an effective pipeline for maize variety identification, which significantly improves classification accuracy and operational feasibility in precision agriculture. Hyperspectral imaging ; Preprocessor ; Pattern recognition (psychology) ; Principal component analysis ; Support vector machine ; Feature selection ; Random forest ; Feature (linguistics) ; Pipeline (software) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Shane Thompson | Daniel Gunlycke
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Auto-regressive Neural Quantum State Sampling for Selected Configuration Interaction Accurate ground-state energy calculations remain a central challenge in quantum chemistry due to the exponential scaling of the many-body Hilbert space. Variational Monte Carlo and variational quantum eigensolvers offer promising ansatz optimization approaches but face limitations in convergence as well as hardware constraints. We introduce a particular Selected Configuration Interaction (SCI) algorithm that uses auto-regressive neural networks (ARNNs) to guide subspace expansion for ground-state search. Leveraging the unique properties of ARNNs, our algorithm efficiently constructs compact variational subspaces from learned ground-state statistics, which in turn accelerates convergence to the ground-state energy. Benchmarks on molecular systems demonstrate that ARNN-guided subspace expansion combines the strengths of neural-network representations and classical subspace methods, providing a scalable framework for classical and hybrid quantum-classical algorithms. Ansatz ; Subspace topology ; Linear subspace ; Hilbert space ; Convergence (economics) ; Scalability ; Artificial neural network ; Scaling ; Quantum ; Monte Carlo method Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: NASEEM, YALNAZ
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Structure-Guided Discovery of Isoindolinone–Triazolopyrimidine Hybrids as Potential Skp1–Skp2 Inhibitors: Integrated Docking, Molecular Dynamics, and Machine learning modelling Component (thermodynamics) ; Plasma protein binding ; Linker ; Binding site ; Hybrid Synthesis and pharmacology of benzodiazepine derivatives ; Cancer therapeutics and mechanisms ; Synthesis and Reactivity of Heterocycles
Authors: Andres  CM Bran | Théo A Neukomm | Daniel  Paul Armstrong | Zlatko Jončev | Philippe Schwaller
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation Leverage (statistics) ; Mechanism (biology) ; Retrosynthetic analysis ; Chemical space ; Limiting ; Concept learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Keke Song | Jiahui Liu | Yuanxu Zhu | Shunda Chen | Zheyong Fan | Yanjing Su | Ping Qian
Source: Materials Genome Engineering Advances (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Solute Segregation in Polycrystalline Aluminum From Hybrid Monte Carlo and Molecular Dynamics Simulations With a Unified Neuroevolution Potential ABSTRACT The fundamental mechanisms of solute segregation and their impacts on material properties remain elusive, primarily due to the complexity and computational challenges in modeling. To address this, we present a specialized GPU implementation of highly efficient hybrid Monte Carlo and molecular dynamics (MCMD) algorithms in the open‐source GPUMD package. Using this efficient MCMD approach, combined with a general‐purpose machine‐learning‐based neuroevolution potential for 16 elemental metals and their alloys, we simulate the segregation of 15 solutes in polycrystalline Al. Our results reveal distinct segregation patterns for these solutes (Ag, Al, Au, Cr, Cu, Mg, Mo, Ni, Pb, Pd, Pt, Ta, Ti, V, W, Zr) in polycrystalline Al. We further investigate the impact of solutes on the strength of polycrystalline Al, analyzing the mechanisms of solute strengthening and embrittlement at the atomistic level. Our findings indicate the critical roles of grain boundaries cohesion and the nucleation and movement of Shockley dislocations in determining the material's strength. We anticipate that our efficient GPU‐accelerated MCMD implementation in GPUMD, along with the insights into solute segregation behavior in polycrystalline Al, will be valuable for the design of Al alloys and other multi‐component materials, including medium‐entropy materials, high‐entropy materials, and complex concentrated alloys. Crystallite ; Nucleation ; Molecular dynamics ; Grain boundary ; Monte Carlo method ; Embrittlement ; Neuroevolution ; Kinetic Monte Carlo Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Zhitong Ma | Yuanfang Wang | Xunli Wang | Qingbai Tian | Xing Xu | Hanghang Zhao
Source: Chinese Chemical Letters (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

MXene-based single-atom catalysis for enhanced Fenton-like reactions in water treatment Catalysis ; Water treatment ; Yield (engineering) ; Reaction conditions MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction ; Advanced oxidation water treatment
Authors: Iman Najafipour | Santanu Chaudhuri
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches Graphene, its derivatives such as graphene oxide and reduced graphene oxide, and related carbon nanostructures including carbon nanotubes, possess exceptional mechanical, thermal, and electronic properties. These features make them highly attractive for applications ranging from energy storage and filtration to flexible electronics and biomedical systems. Molecular simulation methods, including atomistic molecular dynamics, coarse grained modeling, and dissipative particle dynamics, have become essential tools for investigating the behavior of these materials at multiple length and time scales. This review presents recent advances in the modeling of graphene-based systems, highlighting how different simulation techniques are employed to explore mechanical deformation, defect evolution, thermal transport, and interfacial behavior. Special attention is given to the development of coarse-grained models, the role of force field selection, and the integration of machine learning to improve computational efficiency and predictive accuracy. Future directions include the use of high-performance computing, artificial intelligence assisted simulations, and scalable frameworks for large scale modeling. Overall, molecular simulations continue to play a critical role in the rational design and optimization of graphene-derived materials for advanced technological applications. Graphene ; Multiscale modeling ; Scalability ; Dissipative particle dynamics ; Electronics ; Molecular dynamics ; Carbon fibers ; Field (mathematics) ; Oxide Graphene research and applications ; Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies
Authors: Paulmar Pushparaj John | Ramadoss R | John Solomon I | Jayabalan C
Source: Composite Interfaces (article, 2026-04-09)
Field: Materials Science — Polymers and Plastics

Effect of chemical treatment and biosilica fillers on the mechanical, thermal, and tribological properties of Ricinus communis stem fiber composites: a machine learning and RSM approach Tribology ; Ricinus ; Response surface methodology ; Fiber ; Composite number Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Aluminum Alloys Composites Properties