jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuhuan Zhu | Qiang Chen | Luyan He | Chunlin Shang | Li Jiang | Donghong Guan | Guirong Yao | Wenkai Guo
Source:  (article, 2026-02-12)
Field: Chemistry — Organic Chemistry

Supplementary material to "Revisiting the critical role of stabilized Criegee intermediates (sCIs) in sulfuric acid formation: coupling mechanistic updates with interpretable machine learning" Sulfuric acid ; Coupling (piping) ; Reaction mechanism ; Reaction intermediate ; Work (physics) Coordination Chemistry and Organometallics ; Organic Chemistry Cycloaddition Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Han-Zhi Luo | Cheng Shang | Zhi‐Pan Liu
Source: Journal of Chemical Theory and Computation (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Linear-Scaling and Memory-Efficient Implementation of van-der-Waals Interaction (DFT-D3) for Large Systems The van der Waals (vdW) interaction is ubiquitous in materials and is long-range by nature. To facilitate vdW-included atomic simulations in large systems with tens of thousands of atoms, we developed LASP-D3, a Compute Unified Device Architecture (CUDA) implementation of the DFT-D3 method on graphics processing unit (GPU) devices, which realizes fast vdW corrections compatible with state-of-the-art machine-learning potential calculations. Our implementation achieves a linear-scaling time complexity, O(N), for large periodic systems, being up to 2 orders of magnitude faster than all current versions for systems above 100,000 atoms, and significantly reduces GPU memory consumption compared to existing PyTorch-based GPU implementations. By combining LASP-D3 with the generalized global neural network potential developed by us, we show that the leading solid electrolyte LiTaCl<sub>6</sub> can achieve high conductivity, where the vdW interaction plays a key role in governing Li-ion diffusion and the simulated conductivity reproduces experimental measurements. Key (lock) ; Software ; Field (mathematics) 2D Materials and Applications ; Mechanical and Optical Resonators ; Strong Light-Matter Interactions
Authors: Karnjit Parmar | Seth A. Bawel | Mihai V. Popescu | Binh Khanh Mai | Jacob C. Timmerman | Bilal Altundas | Robert S. Paton | Scott E. Denmark
Source: Journal of the American Chemical Society (article, 2026-01-06)
Field: Chemistry — Organic Chemistry

The Atroposelective Iodination of 2-Amino-6-arylpyridines Catalyzed by Chiral Disulfonimides Actually Proceeds via Brønsted Base Catalysis: A Combined Experimental, Computational, and Machine-Learning Study A thorough mechanistic investigation into the disulfonimide (DSI)-catalyzed atroposelective iodination of 2-amino-6-arylpyridines with <i>N</i>-iodosuccinimide is described. While initially hypothesized to proceed via Brønsted-acid catalysis through activation of NIS, experimental evidence led us to reconsider the possible function of the DSI catalyst. We now propose an unprecedented mechanism in which the conjugate base of the DSI functions instead as a Brønsted-base, with the rate- and stereodetermining step involving an enantioselective deprotonation of a Wheland intermediate. The experimental data was used to initiate a DFT exploration of the reaction mechanism, which, after thorough analysis of the transition state structures and reaction coordinate, confirmed our revised hypothesis. The unique structural features of the highest-performing catalyst were explored further with a chemoinformatics-based workflow, leading to a detailed elucidation of those factors contributing to the origin of enantioselection. Deprotonation ; Catalysis ; Halogenation ; Conjugate ; Enantioselective synthesis ; Base (topology) Axial and Atropisomeric Chirality Synthesis ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Luca Cibinel | Trond Linjordet | Johan Pensar | David Balcells | Riccardo De Bin | Basil Ell
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible datasets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modelling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) dataset, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 50k TMCs. These descriptions are both qualitative and quantitative in nature, encompassing the compositional nature of TMCs in terms of their constituting ligands, as well as the entirety of their molecular graphs. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models. Vocabulary ; Probabilistic logic ; Representation (politics) ; Knowledge graph ; Graph ; RDF ; Knowledge representation and reasoning ; Field (mathematics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Asymmetric Hydrogenation and Catalysis
Authors: Luca Cibinel | Trond Linjordet | Johan Pensar | David Balcells | Riccardo De Bin | Basil Ell
Source: ArXiv.org (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible datasets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modelling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) dataset, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 50k TMCs. These descriptions are both qualitative and quantitative in nature, encompassing the compositional nature of TMCs in terms of their constituting ligands, as well as the entirety of their molecular graphs. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models. Vocabulary ; Probabilistic logic ; Representation (politics) ; Knowledge graph ; Graph ; RDF ; Knowledge representation and reasoning ; Field (mathematics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Asymmetric Hydrogenation and Catalysis
Authors: Meisam Samadzadeh | Mahshid Danesh | Atefeh Zarepour | Arezoo Khosravi | Ali Zarrabi | Siavash Iravani
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

MXenes in diabetes: diagnostic and therapeutic applications This review highlights the promising role of MXenes and their composites in diabetes management, emphasizing their dual utility in diagnostics and therapeutics. MXenes' exceptional electrical conductivity, hydrophilicity, mechanical robustness, and tunable surface chemistry facilitate the design of sensitive and selective biosensors for real-time and non-invasive monitoring of key diabetes biomarkers like glucose and acetone. Therapeutically, MXene-based materials enhance healing of diabetic complications such as foot ulcers by modulating inflammation, scavenging reactive oxygen species, promoting angiogenesis, and supporting tissue regeneration <i>via</i> multifunctional hydrogels, patches, and scaffolds. Despite these advances, challenges remain including environmentally harmful synthesis methods, limited scalability, oxidation-induced instability under physiological conditions, and insufficient biocompatibility data. Future efforts are directed toward developing greener and scalable synthesis routes, improving MXene stability through surface modifications, and integrating MXenes with cutting-edge technologies such as wearable devices, 3D bioprinting, and bioelectronics. Additionally, the review uniquely explores the incorporation of artificial intelligence and machine learning techniques to enable personalized and adaptive diabetes management. By providing a comprehensive synthesis of recent developments, current limitations, and innovative future directions, this review offers novel insights aimed at accelerating the clinical translation of MXene-based platforms to significantly enhance diabetes diagnosis and treatment. MXenes ; Biocompatibility ; Regeneration (biology) ; Wearable computer ; Biocompatible material ; Diabetes mellitus MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Graphene and Nanomaterials Applications
Authors: Saravanamuthukumar Ponraj | Ahmad Baharuddin Abdullah
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Physics-informed boosting-based machine learning prediction of martensitic start temperature and thermal hysteresis in Cu–Al–Ni shape memory alloy Shape-memory alloy ; Hysteresis ; Alloy ; Thermal ; Thermal hysteresis ; Martensite Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; High Entropy Alloys Studies
Authors: Narjes Ansari | César Feniou | Nicolaï Gouraud | Daniele Loco | Siwar Badreddine | Baptiste Claudon | Félix Aviat | Marharyta Blazhynska | Kevin E. Gasperich | Guillaume Michel | Diata Traoré | Corentin Villot | Thomas Plé | Olivier Adjoua | Louis Lagardère | Jean-Philip Piquemal
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials. Heuristics ; Convergence (economics) ; Quantum computer ; Quantum machine learning ; Quantum ; Ab initio ; Drug discovery ; Position (finance) Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Muhammad Fahad Shinwari | Muhamad Zahim Sujod | Norhafidzah binti Mohd Saad | Sheeraz Iqbal | Junaid Ali | Islam Ahmed
Source: International Journal of Science and Research Archive (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Artificial Intelligence: Scientific Research’s Powerful Paradigm Artificial intelligence (AI), together with modern machine learning (ML) and deep learning (DL), is reshaping research and practice across science, engineering, business, and everyday life. As scientific instruments and simulations generate increasingly large, high‑throughput datasets, ML methods have become essential for extracting patterns, organizing information, forecasting outcomes, and supporting evidence‑based decisions. This rewritten overview surveys how AI is being developed and applied across foundational disciplines, including information science, mathematics, medical science, materials science, geoscience, life sciences, physics, and chemistry. For each area, it highlights major bottlenecks (such as data scarcity, noisy observations, complex dynamics, and expensive experimentation) and explains how AI tools can help address them. It also outlines emerging research directions that deepen the integration of AI into scientific workflows. The goal is to provide a broad, readable guide to where AI can accelerate fundamental research and what practical challenges still need attention. Deep learning ; Applications of artificial intelligence ; Big data ; Everyday life ; Scientific discovery Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Aakash Ashok Naik | Nidal Dhamrait | Katharina Ueltzen | Christina Ertural | Philipp Benner | Gian-Marco Rignanese | Janine George
Source: Open MIND (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

A critical assessment of bonding descriptors for predicting materials properties Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuable concept for predicting materials properties. Over the years, various theoretical frameworks have been developed to characterize bonding in solid-state materials. However, integrating bonding information from these frameworks into machine learning pipelines at scale has been limited by the lack of a systematically generated and validated database. Recent advances in high-throughput bonding analysis workflows have addressed this issue, and our previously computed Quantum-Chemical Bonding Database for Solid-State Materials was extended to include approximately 13,000 materials. This database is then used to derive a new set of quantum-chemical bonding descriptors. A systematic assessment is performed using statistical significance tests to evaluate how the inclusion of these descriptors influences the performance of machine-learning models that otherwise rely solely on structure- and composition-derived features. Models are built to predict elastic, vibrational, and thermodynamic properties typically associated with chemical bonding in materials. The results demonstrate that incorporating quantum-chemical bonding descriptors not only improves predictive performance but also helps identify intuitive expressions for properties such as the projected force constant and lattice thermal conductivity via symbolic regression. Workflow ; Set (abstract data type) ; Support vector machine ; Material properties ; Characterization (materials science) ; Thermal conductivity ; Scale (ratio) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Computational Drug Discovery Methods
Authors: Yu Miyazaki | Atsuhiro Tomita | Akihide Hayashi | So Takemoto | Mizuki Takemoto | Hodaka Mori
Source: arXiv (Cornell University) (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

PFP/MM: A Hybrid Approach Combining a Universal Neural Network Potential with Classical Force Fields for Large-Scale Reactive Simulations Universal machine-learning interatomic potentials (uMLIPs) enable reactive molecular simulations with near-DFT accuracy, yet applying them efficiently to large, realistic condensed-phase systems remains computationally demanding. Here we present PFP/MM, a hybrid approach that combines a uMLIP, PreFerred Potential (PFP), with molecular mechanics (MM) to enable both large-scale and long-time simulations that are challenging for uMLIP-only calculations. Using an alanine dipeptide in explicit water, we achieve multi-ns/day enhanced sampling and obtain a Ramachandran plot consistent with established basins. For an intramolecular nucleophilic addition reaction in a polar solvent environment, we reproduce the expected solvent-induced stabilization in the free-energy profile. We further apply the approach to a cytochrome P450 Compound I hydroxylation reaction and obtain a free-energy landscape consistent with the accepted reaction mechanism. These results demonstrate that uMLIP-based reactive simulations can be applied to diverse condensed-phase processes in large, realistic environments. Ramachandran plot ; Molecular dynamics ; Intramolecular force ; Plot (graphics) ; Force field (fiction) ; Discretization ; Potential energy ; Artificial neural network Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Leonardo G. J. M. Voltarelli | Natalia Osiecka | Marcin Piwowarczyk | Ewa Ewa Juszynska-Galazka | Rafael S. Zola | Matjaz Perc | Haroldo V. Ribeiro
Source: arXiv (Cornell University) (preprint, 2026-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable liquid crystal phase classification via two-by-two ordinal patterns Liquid crystal textures encode rich structural information, yet mapping these images to mesophase identity remains challenging because visually similar patterns can arise from distinct structures. Here we present a simple, interpretable representation that maps textures to a 75-dimensional frequency vector of two-by-two ordinal patterns, grouped into eleven symmetry-based types to characterize a large-scale dataset spanning seven mesophases. Combined with a simple machine learning classifier, this lightweight representation yields near-perfect phase recognition, including the difficult distinction between smectic A and smectic B mesophases. Our approach generalizes to unseen compounds and accurately distinguishes between phase identity and material origin. Unlike deep learning methods, each ordinal pattern is readily interpretable, and model explanations augmented with network visualizations of pattern interactions reveal the specific types and pairwise dependencies that drive each mesophase decision, providing compact, physically meaningful summaries of texture determinants. These results establish two-by-two ordinal patterns as an interpretable and scalable tool for liquid crystal image analysis, with potential applications to other complex patterned systems in materials science. Mesophase ; Pairwise comparison ; Representation (politics) ; Pattern recognition (psychology) ; Texture (cosmology) ; Identity (music) ; Image (mathematics) ; Simple (philosophy) Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Material Dynamics and Properties
Authors: Gourab Datta | Safura Sharifi | Yaser Banad
Source: Scientific Reports (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

TRACER: a reliability-first GemNet baseline for trustworthy computational materials discovery Computational materials discovery is increasingly driven by graph neural networks (GNNs); however, deployment is constrained by uncertain robustness and inadequately validated uncertainty quantification (UQ). The ALIGNN, MACE, and Open Catalyst models have made strides, but there is still no standardized framework for reproducibility, fairness, and dependability. We introduce Transparent and Reliable Accuracy, Confidence, and Error Ranking (TRACER), a comprehensive, transparent, and repeatable reliability-first pipeline built on a GemNet-based Graph Neural Network (GNN). Using a held-out test set investigate robustness via sensitivity to graph cutoff, architectural depth, and training-data fraction. Achieving competitive accuracy, single GemNet model records a Mean Absolute Error (MAE) of 0.0370 eV [Formula: see text] on JARVIS-DFT, representing a 25.8% reduction in MAE compared to an identical split ALIGNN baseline. UQ benchmarking demonstrates that deep ensembles provide informative uncertainty estimates that strongly correlate with error, proving superior for "hard-case" identification and triage. We also present valuable negative results: FiLM-based domain adaptation provides no significant benefit on this single-domain task, and a material-aware "Gate-Hard" heuristic is outperformed by simple variance-only ranking for identifying high-error cases. The framework offers substantial operational utility, demonstrating F1 score of 0.378 (N=3 ensemble, 20% budget) compared to 0.325 for random selection in capturing high-error cases compared to random selection when working with tight computational budgets. Confirmed by generalization to Matbench Perovskites, TRACER provides reproducible and confidence-aware computational materials discovery, as well as a strong predictor. Robustness (evolution) ; Benchmarking ; Graph ; Domain adaptation ; Ranking (information retrieval) ; Software deployment ; Pipeline (software) ; Artificial neural network ; Sensitivity (control systems) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Scientific Computing and Data Management
Authors: Davide Rigoni | Alessandro Sperduti | Stefano Moro
Source: Journal of Chemical Information and Modeling (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

MolVE: An Open-Source Web Platform for Visualizing and Evaluating AI-Designed Molecules to Aid in Prioritization Advances in artificial intelligence and deep generative models have enabled the rapid generation of novel molecular structures for advanced material science and drug discovery. However, the effective evaluation of these candidates still depends, in the end, on expert judgment, which is often fragmented and difficult to scale. This work introduces MolVE, an open-source, web-based platform designed to enable asynchronous, distributed, and collaborative evaluation of molecules by experts. The platform combines secure user authentication, data set management, and interactive 2D/3D visualizations, enabling chemists and pharmacologists to curate, annotate, and evaluate molecules efficiently. Additionally, it offers APIs for seamless integration with programming languages and provides a Python service to run machine learning and deep learning models. MolVE is a comprehensive, ready-to-deploy application built with React, Node.js, Express, and PostgreSQL, and is fully containerized using Docker. This setup enables scalable deployment in both academic and industrial settings. Scalability ; Python (programming language) ; Software deployment ; Cheminformatics ; Deep learning ; Set (abstract data type) ; Generative grammar ; Web service Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management