jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. H. Bohachov | A. A. Kordyuk
Source: Low Temperature Physics (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Disentangling coherent and incoherent effects in superconductor photoemission spectra via machine learning Disentangling coherent and incoherent effects in the photoemission spectra of strongly correlated materials is generally a challenging problem due to the involvement of numerous parameters. In this study, we employ machine learning techniques, specifically convolutional neural networks (CNNs), to address the long-standing issue of bilayer splitting in superconducting cuprates. We demonstrate the effectiveness of CNN training on modeled spectra and confirm earlier findings that establish the presence of bilayer splitting across the entire doping range. Furthermore, we show that the magnitude of the splitting does not decrease with underdoping, contrary to expectations. This approach not only highlights the potential of machine learning in tackling complex physical problems but also provides a robust framework for advancing the analysis of electronic properties in correlated superconductors. Superconductivity ; Spectral line ; Convolutional neural network ; Coherent potential approximation ; Artificial neural network ; Bilayer Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Electronic and Structural Properties of Oxides
Authors: Florian M. Dietrich | Michael A. Bellucci | Matteo Salvalaglio
Source: The Journal of Chemical Physics (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Committor-regularized learning of differentiable collective variables from non-differentiable structural descriptors Collective variables (CVs) are essential for interpreting and accelerating rare events in molecular simulations, yet their construction is constrained by the requirement of differentiability with respect to atomic coordinates. This excludes many powerful structural descriptors, such as discrete local-structure classifiers, which are routinely used for analysis but cannot be applied directly to define bias potentials in enhanced-sampling molecular dynamics simulations. Here, we introduce a physics-informed machine-learning framework that transforms non-differentiable structural descriptors into fully differentiable, bias-ready collective variables. The approach uses graph neural networks to learn smooth surrogate representations of inherently discontinuous descriptors, trained directly from atomic coordinates while preserving their physical interpretability. To guide interpolation between metastable states, we introduce committor regularization, a physically motivated constraint derived from transition path theory that biases learning toward committor-like behavior along reactive pathways. We apply this framework to crystal nucleation in a supercooled copper melt, using polyhedral template matching (PTM), a discrete, non-differentiable classifier of local crystal structure, as the target descriptor. Neural network CVs are trained to reproduce the fractions of FCC and BCC environments inferred from PTM labels while remaining fully differentiable. We show that committor-regularized models yield reproducible free-energy surfaces across ensembles of independently trained CVs, overcoming the stochastic variability that commonly limits machine-learned collective variables. By bridging discrete physics-based descriptors and continuous differentiable representations, this work establishes a general route to constructing interpretable, reproducible, and physically grounded collective variables for enhanced sampling. Differentiable function ; Artificial neural network ; Constraint (computer-aided design) ; Classifier (UML) ; Matching (statistics) ; Invariant (physics) ; Metastability Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Boyuan Jian | Yatong Zhen | Ye Yang | Junhong Hao | Xiaoze Du
Source: ACS Omega (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Deconstruction Analysis and Regeneration Optimization of Solid Electrolyte Components for Energy Storage Based on the Finite Data Statistics Solid-state electrolytes have a superior theoretical energy density, higher safety, and longer cycle stability. However, researching solid-state electrolyte materials with high performance remains difficult. Machine learning methods can accurately predict performance, speed up the screening process, reduce expensive and time-consuming experimental trials, and have significant advantages in the optimization and fabrication of solid-state electrolyte materials. This research constructed a solid-state electrolyte database based on existing data, trained a random forest (RF) algorithm model to achieve performance prediction of Na superionic conductor (NASICON) solid-state electrolytes by using only the component information, and analyzed the effect of different components. The optimization results screened and provided the optimal ratio of 61 doping elements, and meanwhile verified the accuracy and optimization of the model prediction results. The effects of different components on the ionic conductivity of solid-state electrolytes, including the descriptors used and types of doping elements, were distinguished. This study provides a reference for the optimization direction of solid-state electrolyte materials and the selection of components, which contribute to the exploration and development of high-performance solid-state electrolyte materials. Electrolyte ; Conductor ; Conductivity ; Ionic conductivity ; Energy storage ; Fabrication ; Energy (signal processing) ; Ionic bonding ; Electrical conductor Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Ya-Jun Yin | Lifang Yin | Yi Zhao | Xin Xu | Yu-Qi Xia | Jingjing Zhao | Jia-Qi Bai | Guangjun Nan | Ji-fen Wang | Lu-Yi Zou
Source: Chinese Chemical Letters (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Data-driven generation and efficient screening of MR-TADF materials Key (lock) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced NMR Techniques and Applications
Authors: Junge Zhang | Jiaxin Liu | Xuemei Lin | Weiqiang Zhou | Chuan Xu | Yuqiao Zhang | Long Zhang | Yi Guo | Xianghai Song | Jisheng Zhang | Xin Liu | Yangyang Yang | Huanhuan Zhu | Pengwei Huo
Source: Frontiers of Materials Science (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Advances in hydrogenation catalysis by porous materials supported palladium nanoparticles Bimetallic strip ; Nanomaterial-based catalyst ; Microporous material ; Catalysis ; Palladium ; Porosity ; Density functional theory ; Hydrogen storage Covalent Organic Framework Applications ; Catalysis for Biomass Conversion ; Asymmetric Hydrogenation and Catalysis
Authors: JIANGYANG TAN | HAIJIAN SHAO | Xing Deng | YINGTAO JIANG
Source: Materials Today Energy (article, 2026-01-21)
Field: Chemistry — Inorganic Chemistry

A hybrid graph neural network framework with transformer-based atomic embeddings for predicting hydrogen adsorption in metal-organic frameworks Interpretability ; Artificial neural network ; Embedding ; Metal-organic framework ; Graph ; Adsorption ; Complex system ; Supramolecular chemistry Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Anjali Anjali | Rajiv Yadav | Harvinder Singh | Rajwant Kaur
Source: International Journal of Drug Delivery Technology (article, 2026-04-06)
Field: Chemistry — Organic Chemistry

Recent Advances in Pyrazoline-Based Derivatives: Synthetic Approaches, Pharmacological Potential and Molecular Docking Perspectives Pyrazoline, a versatile five-membered nitrogen-containing heterocyclic nucleus, has emerged as a cornerstone in modern medicinal chemistry due to its diverse pharmacological profile and structural adaptability. This review provides a comprehensive overview of the synthetic strategies, therapeutic applications, and molecular docking insights of pyrazoline derivatives reported predominantly between 2020 and 2025. We explore traditional synthetic routes, such as the basecatalysed condensation of chalcones with hydrazines and 1,3-dipolar cycloadditions, alongside modern "green" methodologies including microwave-assisted, ultrasound-induced, and mechanochemical synthesis.The therapeutic section highlights the multimodal biological activities of pyrazolines, encompassing anticancer, antimicrobial, antiinflammatory, analgesic, and neuroprotective effects. Emphasis is placed on their role as kinase inhibitors (EGFR/HER2), DNA gyrase inhibitors and selective MAO-B inhibitors for neurodegenerative disorders. Furthermore, the integration of in silico studiesincluding molecular docking, QSAR modellingand molecular dynamics simulations is discussed as a pivotal tool for rational drug design. These computational methods have elucidated key binding interactions with targets like COX-2, HDAC, and tubulin while ADMET profiling confirms the "drug-like" potential of this scaffold. Finally, the review examines the evolving patent landscape and the burgeoning role of Machine Learning in accelerating the discovery of novel pyrazoline-based lead compounds. This synthesis of recent data underscores the pyrazoline nucleus as a high-value scaffold for the development of next-generation therapeutic agents In silico ; Drug discovery ; Docking (animal) ; Quantitative structure–activity relationship ; Drug target ; Pyrazoline Synthesis and biological activity ; Synthesis and Reactivity of Heterocycles ; Click Chemistry and Applications
Authors: Saeid Khairandesh | Marzieh Lotfi | Afsanehsadat Larimi | Ali Akbar Asgharinezhad | C. Ghotbi
Source: Scientific Reports (article, 2026-03-18)
Field: Chemistry — Inorganic Chemistry

Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications Hydrogen is a promising clean energy carrier, but its low energy density necessitates advanced storage solutions. Metal-Organic Frameworks (MOFs) offer high tunability and porosity for efficient hydrogen adsorption. This work combines Grand Canonical Monte Carlo (GCMC) simulations with machine learning, employing Feed-Forward (FNN) and Pattern Recognition (PRNN) neural networks optimized via Equilibrium Optimizer and Genetic Algorithm. The integrated approach predicts gravimetric and volumetric hydrogen storage capacities across 98,695 metal-organic frameworks under temperature-pressure swing conditions. Pore volume and void fraction emerged as dominant structural descriptors. The models identified 12 top-performing MOFs exceeding MOF-5 in both gravimetric (8.27 wt.%) and volumetric (51.94 g-H<sub>2</sub>/L) capacities, demonstrating the power of ML-accelerated screening for next-generation hydrogen storage materials. Gravimetric analysis ; Hydrogen storage ; Porosity ; Monte Carlo method ; Work (physics) ; Artificial neural network ; Energy storage ; Hydrogen Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Magnesium Oxide Properties and Applications
Authors: Anurupa Maiti
Source: ChemRxiv (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

A Simplified Architecture of a Neural Network for Improved Performance on Small Datasets in Photovoltaic Applications Oxide perovskites (ABO3) have emerged as chemically robust and environmentally benign alternatives to halide perovskites for photovoltaic applications. However, the vast compositional space of oxide perovskites makes systematic experimental or first-principles exploration impractical. In this work, we present a machine-learning (ML) based screening framework to identify ABO3 oxide perovskites with favourable photovoltaic characteristics, focusing on two key descriptors: electronic band gap and intrinsic carrier concentration. While conventional ML models such as Random Forest and XGBoost achieve reasonable classification accuracy, they often struggle with imbalanced datasets and fail to capture the physically meaningful ordering of intermediate classes. Similarly, standard neural networks tend to overfit small datasets and underperform in representing borderline semiconductor regions. To overcome these limitations, we propose a modified neural-network architecture, MOIMNet, which integrates a dual head design with a margin-based loss function. This innovation enforces structured separation between extreme classes while preserving the continuum of electronic properties in the latent space, thereby enhancing discrimination of the physically critical middle class. MOIMNet achieves balanced accuracy exceeding 0.83 and area-under-the-curve (AUC) values above >0.90 across all classes, outperforming conventional ML and standard neural networks in handling small, imbalanced datasets. Selected predictions were validated using density functional theory calculations performed with Quantum ESPRESSO, demonstrating strong agreement between ML screening and first-principles results. This integrated ML DFT framework, anchored by MOIMNet, provides a reliable and physics-aware strategy for accelerating the discovery of oxide perovskites for solar-cell applications. Photovoltaic system ; Overfitting ; Oxide ; Artificial neural network ; Key (lock) ; Density functional theory ; Band gap ; Quantum Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Zhenyao Fang | Ting-Wei Hsu | qimin yan
Source: AI for Science (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Learning atomic representations for data-driven materials design Learning the latent representations of atomic structures has become central to the application of machine learning (ML) in materials science, as such representations provide a unified framework for connecting atomic structures to material properties. Early physics-inspired descriptors facilitated efficient prediction of selected properties but were limited in flexibility and transferability. Recent advances in graph-based representations and graph neural networks (GNNs) have enabled data-driven feature learning frameworks that capture complex chemical environments, long-range interactions, and symmetry-governed responses directly from atomic structures. In this Perspective, we review recent progress in atomic representation learning for crystalline materials, with an emphasis on GNN architectures for predicting scalar, spectral, and tensorial properties. We discuss emerging challenges and opportunities related to high-fidelity datasets, model interpretability, and the integration of ML predictions with experimentally relevant phenomena, including disorder, dynamics, and finite-temperature effects. Finally, we outline future directions in which representation learning serves as a foundation for inverse materials design, leading to the systematic discovery and optimization of materials with targeted functional properties. Feature (linguistics) ; Representation (politics) ; Key (lock) ; Generalization ; Frame (networking) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Florian Boser | Jan C. Spies | Frank Glorius
Source: Journal of the American Chemical Society (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning The vast reaction data within scientific literature represents a rich resource for training predictive machine learning models. However, this resource is fundamentally compromised by a pervasive selection and reporting bias, resulting in imbalanced data sets. In this work, we introduce "Positivity is All You Need" (PAYN), a machine learning framework that addresses this data-scarcity problem by learning directly from biased, positive-only data. PAYN leverages a spy-based positive-unlabeled (PU) learning strategy, treating reported high-yielding reactions as the "positive" class and the vast, unexplored chemical space as the "unlabeled" class. To validate our approach, we simulated literature bias on fully labeled high-throughput experimentation (HTE) data sets, including Ni-catalyzed borylations, Buchwald-Hartwig and Suzuki-Miyaura couplings. We demonstrated that PAYN significantly improves the performance of models trained on biased data by balancing the data with augmented negative data points. This work establishes a robust strategy for leveraging biased data, paving a path toward more scalable and accessible data-driven strategies for accelerating synthesis design, optimization, and chemical discovery. Scalability ; Resource (disambiguation) ; Path (computing) ; Yield (engineering) ; Chemical space ; Labeled data ; Class (philosophy) ; Synthetic data Machine Learning in Materials Science ; Machine Learning and Data Classification ; Explainable Artificial Intelligence (XAI)
Authors: Pei-Hua Li | Meng Yang | Wen-Qing Liu | Xing-Jiu Huang
Source: Current Opinion in Electrochemistry (article, 2026-01-20)
Field: Chemistry — Electrochemistry

Probing electrochemical solid–solid and solid–liquid interface reaction characteristics based on in-situ technologies and theoretical simulations Interface (matter) ; Nanomaterials ; Electrolyte ; Electrochemistry ; Electrode ; Ion ; Kinetic energy Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Iryna B Boiko | Anton V. Zhemera | D. Horvath | Dmytro G. Krotko | Dmytro M. Volochnyuk | Ihor V Komarov | Alexandre Varnek | Serhiy V. Ryabukhin
Source: ChemRxiv (article, 2026-03-10)
Field: Chemistry — Organic Chemistry

Predicting Reactivity and Reaction Yields in Parallel Synthesis: Meeting of Expectations and Reality This study focuses on the essential problem in modern machine learning in chemistry: data quality and its impact on ML predictions. We explored a dataset of Biginelli reactions, obtained via a parallel synthesis protocol at Enamine LTD over more than 20 years. Prediction models, both classification (feasibility) and regression (estimated yield), were developed. We observed a relatively uniform performance of different ML methods, representations, and descriptor types. The performance of regression models is limited by the inaccuracies in the reported yield values, which were explored in depth (model outlier identification, resynthesis, and structural analysis). Our findings highlight the potential of machine learning models not only for predictive tasks but also as tools for quality control and deeper insights into the synthetic process-particularly when results deviate from expectations. They also demonstrate the critical impact of input data quality on the excellence of prediction models. Our models were finally evaluated on a set of newly synthesized molecules, selected from Enamine's REAL database, and found to be of satisfactory quality. Reactivity (psychology) ; Relation (database) ; Term (time) ; Work (physics) ; Set (abstract data type) Cyclization and Aryne Chemistry ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ce Liu | Zhaojie Wang | Xiaoqing Lu | Shoufu Cao | Yang-Gang Wang
Source: Journal of the American Chemical Society (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Sculpting Superior Subnanometer Catalysts Directly from Inert Gold Bulk gold, renowned for its catalytic inertness, can be transformed into an exceptionally active catalyst by engineering undercoordinated sites on its surface. However, directly sculpting such active sites from extended terraces remains a fundamental challenge. Here, we demonstrate a programmable strategy to dynamically generate subnanometer Au clusters directly from inert Au(111) surfaces, unlocking superior activity for CO oxidation. By integrating large-scale machine learning molecular dynamics with density functional theory, we decode the atomistic pathway of this restructuring process. Our approach employs thermal-CO pressure cycles to induce the ejection of step-edge atoms, forming mobile Au-CO complexes. Subsequent cooling kinetically traps these complexes into metastable, subnanometer clusters (3-6 atoms). A controlled reduction of CO exposure then precisely exposes the catalytically crucial undercoordinated sites while maintaining cluster stability. Crucially, these sculpted clusters exhibit a CO oxidation activity that far exceeds that of pristine step edges, terraces, or conventional single-atom sites. This work establishes reaction condition engineering as a powerful paradigm for sculpting active catalysts directly from bulk materials by bypassing traditional synthetic routes. Catalysis ; Inert ; Cluster (spacecraft) ; Molecular dynamics ; Work (physics) ; Active site ; Density functional theory Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Daniel S. Min | Fei Fan | Abigail G. Doyle
Source: Journal of Chemical Education (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Integrating Data Science and Machine Learning with an Aldol Condensation Laboratory We report the development of an undergraduate organic chemistry laboratory to introduce students to modern applications of data science tools and machine learning algorithms in organic chemistry. Data science and machine learning have become increasingly applied to organic chemistry systems built upon physical organic principles of reactivity to better analyze and interpret data. Given that postexperimental analysis is central to any scientific study, we envision that the incorporation of these techniques at an introductory level into the undergraduate chemistry education curriculum will be invaluable in exposing students to contemporary research tools and working with shared data. Herein we describe a two-part experiment, using the experimentally straightforward Claisen–Schmidt aldol condensation reaction with commercially available reagents, to introduce concepts of computational featurization and data processing for multivariate linear regression models at the undergraduate level that can easily be incorporated into organic instructional laboratories. Aldol condensation ; Curriculum ; Cheminformatics ; Chemistry education ; Undergraduate research ; Science education ; Condensation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Air Quality Monitoring and Forecasting