jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Robert Singh | Saurav Saha | Suganiya Murugan
Source:  (article, 2025-06-11)
Field: Chemistry — Spectroscopy

VISIPEDIA-Automated Web-based System for Chemical Compound Analysis The analysis of chemical compounds plays a crucial role in fields ranging from education to drug discovery, yet accessing detailed molecular information typically requires specialized software and domain expertise. This paper introduces VISI-PEDIA, an automated, web-based system designed to simplify chemical compound analysis by focusing on essential molecular attributes. VISIPEDIA combines deep learning, image processing, and web technologies to provide a user-friendly, accessible tool for molecular data retrieval. By integrating the Flask web framework with RDKit, a cheminformatics library, VISIPEDIA enables users to input compound names and receive immediate, structured data output, including molecular formula, chemical weight, number of rings, and IUPAC (International Union of Pure and Applied Chemistry) name. Testing on 50 benchmark compounds showed 75 % accuracy in SMILES conversion and property extraction. Additionally, VISIPEDIA incorporates image-based molecular recognition powered by deep learning models, including Convolutional Neural Networks (CNNs) and ResNet18, to support structure analysis from molecular images, achieving 92.7% classification accuracy. This hybrid approach allows the platform to process and interpret both text-and image-based chemical data, making it versatile for educational and research applications. User feedback from 20 participants highlighted system usability and response time, affirming its utility. The system’s design emphasizes accessibility, empowering students, educators, and researchers to engage in molecular analysis without requiring extensive background knowledge. Potential applications of VISIPEDIA span academic research, chemistry education, and preliminary drug discovery research, establishing it as a scalable and impactful solution in the field. Computer science ; World Wide Web Analytical Chemistry and Chromatography
Authors: Juno Nam | Miguel Steiner | Max Misterka | Soojung Yang | Avni Singhal | Rafael Gómez‐Bombarelli
Source: The Journal of Physical Chemistry Letters (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Transferable Learning of Reaction Pathways from Geometric Priors Identifying minimum-energy paths (MEPs) is a crucial application of molecular simulations to understand chemical reaction mechanisms but is computationally demanding. We introduce MEPIN, a scalable machine-learning method that predicts MEPs from reactant and product configurations, without relying on transition-state geometries or preoptimized reaction paths during training. The MEPIN task is defined as predicting the deviation between ground-truth MEPs and purely geometric interpolations along the reaction coordinates. The model predicts a continuous reaction path using a symmetry-broken equivariant neural network architecture that generates a flexible number of intermediate structures. MEPIN is trained on an energy-based objective, and we report efficiency gains of also using geometric priors from geodesic interpolation as initial interpolations or as pretraining objectives. The approach generalizes across diverse chemical reactions and achieves accurate alignment with reference intrinsic reaction coordinates, as demonstrated in various small molecule reactions and [3 + 2] cycloadditions. Prior probability ; Geodesic ; Interpolation (computer graphics) ; Path (computing) ; Artificial neural network ; Scalability ; Equivariant map ; Orthogonality Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Source:  (review, 2025-02-22)
Field: Materials Science — Materials Chemistry

Review for "Machine Learning-Driven Design of Single-Atom Catalysts for Carbon Dioxide Valorization to High-Value Chemicals: A Review of Photocatalysis, Electrocatalysis, and Thermocatalysis" Electrocatalyst ; Photocatalysis ; Catalysis ; Carbon dioxide ; Atom (system on chip) ; Carbon atom ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Environmental science ; Computer science ; Engineering ; Organic chemistry ; Electrochemistry ; Physical chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Hasotikar, Nibha Nilay
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

AI in Polymer Chemistry: Exploring New Horizons in Material Development and Sustainability Abstract The advent of artificial intelligence (AI) has started to revolutionize numerous scientific fields, and polymer chemistry is no exception. This research paper explores the transformative potential of AI in the development and optimization of polymer materials, with a strong focus on sustainability. We begin by examining the limitations of traditional methods in polymer synthesis and characterization, which often involve time-consuming experimental trials and a lack of predictive power. By leveraging machine learning algorithms, computational modeling, and data-driven approaches, we demonstrate how AI can significantly accelerate the discovery of novel polymer formulations that exhibit enhanced performance characteristics. The paper highlights several case studies where AI techniques have been successfully implemented, such as the generation of polymer libraries, prediction of material properties, and optimization of processing conditions. We also discuss the integration of AI in the circular economy of polymers, emphasizing its role in designing biodegradable materials and improving recycling processes. Furthermore, we address the challenges and ethical considerations of deploying AI in polymer chemistry, including data quality issues and the necessity for interdisciplinary collaboration. In conclusion, this research underscores the potential of AI to not only drive innovations in material development but also to contribute to the creation of sustainable polymer solutions. By fostering a symbiotic relationship between AI and polymer chemistry, we can pave the way for a more sustainable future, unlocking new horizons in the field of materials science. Transformative learning ; New horizons ; Sustainability ; Sustainable development ; Quality (philosophy) ; Biomimetics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Venkata Surya Chaitanya Kolluru | Yiming Chen | Saifeldeen Abed Alrhman | Harshan Reddy Gopidi | Abhishek Panchal | Pieremanuele Canepa | Maria K. Y. Chan
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Metal Organic Frameworks for Zn-Ion Conductors Using Machine Learning Interatomic Potentials Metal organic frameworks (MOFs) are a relatively new class of materials with metal-complex based cations connected with organic linker chains. They offer a diverse range of properties due to combination of different cation complexes and linkers. Moreover, they contain pores which we can leverage for ion conduction. In this work, we explore the potential for Zn-ion conduction within the MOFs which can be utilized as electrodes or electrolytes in Zn-ion batteries using machine learning interatomic potentials (MLIPs). We benchmark several universal MLIP models—MACE [1], MatGL [2], ChgNet [3], and Fairchem [4]—by evaluating their ability to predict single-point energies and forces in MOFs. We apply fine-tuning for these uMLIPs with custom MOF datasets and utilize the models to find the Zn insertion sites and energy landscapes on a much larger ARCMOF dataset [5] with more than 280,000 MOFs. Further, we screen potential Zn-ion conductors by predicting the energy barriers and deriving ion migration pathways using the MLIP-predicted energy landscapes. This work provides a new avenue leveraging recent advancements in MLIPs to accelerate materials design for energy storage applications accurately. References: Batatia, I., Kovacs, D. P., Simm, G., Ortner, C., &amp; Csányi, G. (2022). MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems, 35 , 11423–11436. Chen, C., &amp; Ong, S. P. (2023). A universal graph deep learning interatomic potential for the periodic table. Nature Computational Science, 2 , 718–728. Deng, B., Zhong, P., Jun, K., et al. (2023). CHGNet: a pretrained universal neural network potential for charge-informed atomistic modelling. Nature Machine Intelligence, 5 , 1031–1041. Barroso-Luque, L., Shuaibi, M., Fu, X., Wood, B. M., Dzamba, M., Gao, M., Rizvi, A., Zitnick, C. L., &amp; Ulissi, Z. W. (2024). Open Materials 2024 (OMat24) inorganic materials dataset and models. arXiv preprint arXiv:2410.12771 . Burner, J., Luo, J., White, A., Mirmiran, A., Kwon, O., Boyd, P. G., Maley, S., et al. (2023). ARC–MOF: A diverse database of metal-organic frameworks with DFT-derived partial atomic charges and descriptors for machine learning. Chemistry of Materials, 35 (3), 900–916. Leverage (statistics) ; Artificial neural network ; Metal-organic framework ; Interatomic potential ; Electrical conductor ; Energy (signal processing) ; Benchmark (surveying) ; Thermal conduction Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced battery technologies research
Authors: Wen Jiang | Jinxia Zhu | Huimin Zhang | Yuhang Zhang | Qiang Xiao | Weidong Zhu | Fu‐Min Zhang
Source: Advanced Functional Materials (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Triple‐Atom and High‐Entropy Catalysts: Advanced Architectures for Efficient Thermally Driven Organic Transformations Abstract Heterogeneous catalysis is essential for advancing sustainable chemical processes, with recent research highlighting the importance of atomic‐level precision and compositional complexity in optimizing catalytic performance. This review explores two emerging classes of catalysts—triple‐atom catalysts (TACs) and high‐entropy catalysts (HECs)—which have shown considerable promise in organic transformations. Notably, this review establishes a conceptual framework by positioning TACs as a strategic intermediate that bridges the well‐defined diatomic synergy of dual‐atom catalysts (DACs) and the configurational complexity of HECs. This unique positioning enables TACs to combine atomic precision with enhanced three‐center cooperativity, while HECs employ entropy‐driven stabilization to achieve robust catalytic activity. In conjunction with density functional theory calculations, mechanistic insights into the structure–performance relationships of these catalyst classes in representative transformations are comprehensively analyzed, with particular emphasis on their complementary roles in multistep reactions. The review first outlines the multifunctional active sites and innovative synthesis strategies of both catalyst families, emphasizing how TACs bridge the gap between DACs and HECs. Subsequently, advanced characterization techniques and the integration of machine learning approaches for rational catalyst design are discussed. Finally, based on the analysis and insights presented, future directions for development in this field are proposed. Catalysis ; Field (mathematics) ; Characterization (materials science) ; Rational design ; Bridge (graph theory) ; Density functional theory Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; High Entropy Alloys Studies
Authors: Nada Alfryyan | Muhammad Ramzan Saeed Ashraf Janjua | Rashid Iqbal | Khadijah Mohammedsaleh Katubi | M.S. Al-Buriahi
Source: Solid State Communications (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Crystallization behavior prediction: A quest to design efficient polymers for organic solar cells Crystallization ; Polymer ; Crystallization of polymers ; Cluster analysis ; Organic polymer ; Organic solar cell Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Block Copolymer Self-Assembly
Authors: Andrés Felipe Usuga | C. S. Praveen | Aleix Comas‐Vives
Source: ChemRxiv (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Explainable GNN Framework Guided by Local Chemical Features to Predict Binding Energies in Bimetallic Alloys Adsorption energies are key catalytic descriptors that reveal adsorbate-site interactions on heterogeneous catalysts. However, their computation via DFT is time-consuming, limiting high-throughput screening. This work presents a machine learning (ML) methodology based on graph representations of local adsorption sites, using a Graph Neural Network (GNN) with per-atom local descriptors derived from accessible physicochemical properties. The approach is evaluated on two bimetallic datasets. The first includes AB-type bimetallic flat surfaces with varying A:B ratios, predicting binding energies for small monodentate adsorbates (C, N, O, S, H) with MSEs of 0.073/0.181 eV (train/test). The second dataset comprises reaction energies of key intermediates for CO2 hydrogenation on Ni-Ga-based surfaces. The GNN model achieves an impressive performance (MSE: 0.001/0.002 (train/test) eV) on complex atomic configurations, even bidentate ones. Beyond predictive performance, clustering analysis provides an explainable framework, showing how structural and electronic descriptors can rationally guide catalyst design and deepen understanding of adsorbate-metal interactions. Bimetallic strip ; Binding energy ; Chemistry ; Statistical physics ; Physics ; Atomic physics ; Catalysis ; Biochemistry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Electron and X-Ray Spectroscopy Techniques
Authors: Zhilong Wang | Wolfgang G. Zeier | Fengqi You
Source: JuSER Publikationsportal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries Solid-state batteries (SSBs) are pivotal for sustainable energy storage, delivering extended life span, low-temperature resilience, and enhanced safety. However, designing stable solid electrolytes and interfaces in SSBsremains a formidable challenge. As a disruptive catalyst for paradigm shifts spanning materials discovery andenergy system redesign, artificial intelligence (AI) is unleashing unprecedented possibilities—could it be the keybreakthrough for SSB innovation? Here, we critically review the progress of AI applications in electrolyte and in-terface engineering, covering key aspects such as stability, conductivity, mechanical properties, and interface re-sistance. This work emphasizes the integration of cutting-edge modeling strategies, including the materials’screening pipelines, machine learning force fields, and generative models. Furthermore, we conduct an in-depthanalysis of persistent challenges and propose a roadmap featuring multiscale modeling and multimodal modelswith physical constraints to build an intelligent ecosystem for SSB development. This review is expected to inspireinterdisciplinary collaborations and drive forward energy materials design, ultimately accelerating the develop-ment of sustainable and cutting-edge battery technologies. Interface (matter) ; Battery (electricity) ; Work (physics) ; Key (lock) ; Sustainable energy ; Generative grammar ; Efficient energy use Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Xinyi Fang | Elizabeth Murphy | Phillip Kohl | Youli Li | Craig J. Hawker | Christopher M. Bates | Mengyang Gu
Source: ChemRxiv (preprint, 2025-01-17)
Field: Materials Science — Materials Chemistry

Universal Phase Identification of Block Copolymers from Physics-informed Machine Learning Block copolymers play a vital role in materials science due to their diverse self-assembly behavior. Traditionally, exploring the phase space of block copolymer self-assembly and associated structure-property re- lationships involves iterative synthesis, characterization, and theory, which is labor-intensive both experimentally and computationally. Here, we intro- duce a versatile, high-throughput workflow towards materials discovery that integrates controlled polymerization and automated chromatographic separa- tion with a novel physics-informed machine learning algorithm for the rapid analysis of small-angle X-ray scattering data. Leveraging the expansive and high-quality experimental datasets generated by fractionating polymers using automated chromatography, this machine learning method effectively reduces data dimensionality by extracting chemical-independent features from SAXS data. This new approach allows for the rapid and accurate prediction of mor- phologies without repetitive and time-consuming manual analysis, achieving out-of-sample predictive accuracy of around 95% for both novel and existing materials in the training dataset. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combi- nation of controlled synthesis, automated chromatography, and data-driven analysis creates a powerful workflow that markedly expedites the discovery of structure-property relationships in advanced soft materials. Identification (biology) ; Copolymer ; Block (permutation group theory) ; Phase (matter) ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Nuclear magnetic resonance ; Biology ; Geometry ; Polymer Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies
Authors: Trevor Price | Saurabh Sivakumar | Matthew S. Johnson | Judit Zádor | Ambarish Kulkarni
Source: The Journal of Physical Chemistry C (article, 2025-04-13)
Field: Materials Science — Materials Chemistry

Automated Pynta-Based Curriculum for ML-Accelerated Calculation of Transition States Microkinetic models (MKMs) are widely used within the computational heterogeneous catalysis community to investigate complex reaction mechanisms, to rationalize experimental trends, and to accelerate the rational design of novel catalysts. However, constructing these models requires computationally expensive and manually tedious density functional theory (DFT) calculations for identifying transition states for each elementary reaction within the MKM. To address these challenges, we demonstrate a novel protocol that uses the open-source kinetics workflow tool Pynta to automate the iterative training of a reactive machine learning potential (rMLP). Specifically, using the silver-catalyzed partial oxidation of methanol as a prototypical example, we first demonstrate our workflow by training an rMLP to accelerate the parallel calculation of DFT-quality transition states for all 53 reactions, achieving a 7× speedup compared to a DFT-only strategy. Detailed analysis of our training curriculum reveals the shortcomings of using an adaptive sampling scheme with a single rMLP model to describe all reactions within the MKM simultaneously. We show that these limitations can be overcome using a balanced "reaction class" approach that uses multiple rMLP models, each describing a single class of similar transition states. Finally, we demonstrate that our Pynta-based workflow is also compatible with large pretrained foundational models. For example, by fine-tuning a top-performing graph neural network potential trained on the OC20 dataset, we observe an impressive 20× speedup with an 89% success rate in identifying transition states. This work highlights the synergistic potential of integrating automated tools with machine learning to advance catalysis research. Transition (genetics) ; Curriculum ; Mathematics education ; Computer science ; Psychology ; Pedagogy ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Zicun Li | Xinguo Ren | Jinbin Li | Ruijuan Xiao | Hongmin Li
Source: ArXiv.org (preprint, 2025-04-16)
Field: Materials Science — Materials Chemistry

Gas-solid Reaction Dynamics on Li$_6$PS$_5$Cl Surfaces: A Case Study of the Influence of CO$_2$ and CO$_2$/O$_2$ Atmospheres Using AIMD and MLFF Simulations In recent years, rapid progress has been made in solid-state lithium batteries. Among various technologies, coating the surface of electrodes or electrolytes has proven to be an effective method to enhance interfacial stability and improve battery cycling performance. Recent experimental studies showed that gas-solid reactions offer a convenient approach to form modified coating layers on the solid electrolyte. Here, we performed computational simulations to investigate this surface reaction process. Specifically, we simulated the gas-solid reactions of Li$_6$PS$_5$Cl(LPSC) solid-state electrolytes in pure CO$_2$ and in mixed CO$_2$/O$_2$ atmospheres using ab-initio molecular dynamics (AIMD) and machine-learning force fields (MLFF)-accelerated molecular dynamics (MD) approaches. In the former case, LPSC surfaces primarily form Li$_2$CO$_2$S because it is difficult to dissociate another oxygen atom from the second CO$_2$ molecule. While in CO$_2$/O$_2$ mixed atmosphere, O$_2$ molecules preferentially adsorb onto LPSC, which supplies oxygen sites for subsequent CO$_2$ adsorption to form carbonate -CO$_3$ units. This reaction pathway ultimately generates an interfacial product dominated by Li$_2$CO$_3$. These coatings exhibit distinct electronic and ionic conductivity characteristics, allowing the possibility to control coating compositions and configurations by adjusting the gas-solid reactions. Key criteria for applying this strategy are extracted from the current research. Electrolyte ; Molecular dynamics ; Coating ; Adsorption ; Molecule ; Lithium (medication) ; Reaction dynamics ; Ionic conductivity ; Conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Physical and Chemical Molecular Interactions
Authors: Chunghee Nam
Source: Korean Journal of Materials Research (article, 2025-07-27)
Field: Materials Science — Materials Chemistry

Optimization of Machine Learning Models for Predicting the Saturation Magnetization of SmCo Permanent Magnet Materials 1. . , . 1) . 2) NdFeB SmCo , . 2-4) Magnet ; Saturation (graph theory) ; Magnetization ; Pattern recognition (psychology) Machine Learning in Materials Science ; Electric Power Systems and Control ; Nuclear Physics and Applications