jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengxin Pu | Haisong Feng | Xin Song | Si Wang | Jie J. Bao | Xin Zhang
Source: ACS Applied Materials & Interfaces (review, 2025-07-25)
Field: Materials Science — Materials Chemistry

Transforming Catalysis with Machine Learning: Emerging Tools and Next-Gen Strategies Catalysis plays a central role in the modern chemical industry, yet the discovery of high-performance catalysts remains constrained by traditional experimental and ab initio calculation approaches. Recently, the rapid development of machine learning methods has caused a major revolution in the field of catalytic chemistry, which promises to accelerate the catalyst development with unprecedented efficiency. This review systematically introduces the fundamental concepts and workflows of ML in catalysis, followed by a comprehensive overview of both traditional machine learning─typically based on small data sets and shallow models─and deep learning (DL), which leverages large-scale data and complex architectures. We highlight key modeling strategies, algorithmic frameworks, and representative applications in catalyst design, reaction prediction, and surface adsorption systems. Finally, we discuss current challenges, including fragmented and inconsistent data, limited physical interpretability, and difficulties in integrating ML with experimental workflows, and propose future directions to address these issues and further advance the field. Interpretability ; Workflow ; Field (mathematics) ; Computer science ; Artificial intelligence ; Data science ; Machine learning ; Nanotechnology ; Management science ; Biochemical engineering ; Systems engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: Radu A. Talmazan | Jakob Gamper | Iván Castillo | Thomas S. Hofer | Maren Podewitz
Source: ChemRxiv (preprint, 2025-02-17)
Field: Materials Science — Materials Chemistry

From Data to Chemistry: Revealing Causality and Reaction Coordinates through Interpretable Machine Learning in Supramolecular Transition Metal Catalysis Supramolecular transition-metal catalysts with tailored reaction environments allow for the usage of abundant 3d metals as catalytic centres, leading to more sustainable chemical processes. However, such catalysts are large and flexible systems with intricate interactions, resulting in complex reaction coordinates. To capture their dynamic nature, we developed a broadly applicable, high-throughput workflow, leveraging quantum mechanics/molecular mechanics molecular dynamics (QM/MM MD) in explicit solvent, to investigate a Cu(I)-calix[8]arene catalysed C-N coupling reaction. The system complexity and high amount of data generated from sampling the reaction require automated analyses. To identify and quantify the reaction coordinate from noisy simulation trajectories, we applied interpretable machine learning techniques (Lasso, Random Forest, Logistic Regression) in a consensus model, alongside dimensionality reduction methods (PCA, LDA, tICA). Leveraging a Granger Causality model, we go beyond the traditional view of a reaction coordinate, by defining it as a sequence of molecular motions that led up to the reaction. Supramolecular chemistry ; Causality (physics) ; Catalysis ; Transition metal ; Chemistry ; Computer science ; Computational chemistry ; Organic chemistry ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling
Authors: Fedorov, Rostislav
Source: heiDOK (Heidelberg University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Supported Organic Electrode Materials The transition from metal–based cathodes to sustainable, earth-abundant alternatives is constrained by the poor electronic conductivity and high electrolyte solubility of most organic redox materials. This thesis develops an integrated quantum-chemical and machine-learning framework that overcomes these barriers and delivers a fast, data-driven route from molecular concepts to high-performance organic cathodes. First, a density-functional-theory (DFT) workflow was benchmarked against experimental data and used to create ReSolved, a curated dataset of approximately 19 000 closed- and open-shell organic molecules in five solvents. A graph neural network with a solvent-aware set-transformer read-out reproduces absolute reduction potentials with a mean absolute error of $\approx$0.20 eV and transfers accurately to unseen solvents. Coupled to an evolutionary algorithm, the model enables inverse design, rapidly proposing synthetically accessible molecules tailored to specific battery chemistries. To extend molecular insights to extended solids, the thesis introduces deCOFpose: an automated, topologically aware fragmentation algorithm that dissects crystalline covalent organic frameworks (COFs) into chemically meaningful nodes and linkers. Applied to the CoRE-COF database, deCOFpose successfully processes >70 % of structures, reveals systematic trends between fragment properties and COF band gaps, and feeds a modified PORMAKE builder that can enumerate millions of plausible frameworks for high-throughput screening. Addressing composite electrodes, a semi-empirical tight-binding protocol was combined with a symmetry-adapted, P6mm wallpaper-group equivariant graph neural network (WallpaperNet) to predict adsorption geometries, binding energies, and force fields for small molecules on graphene. Together, these contributions introduce a set of tools that span length scales from single molecules to periodic frameworks and composite materials, significantly reducing the screening time for viable organic cathodes. Beyond batteries, the methodologies, datasets, algorithms, and software are broadly applicable to catalysis and molecular sensing, furnishing a versatile platform for the accelerated discovery of sustainable, carbon-based functional materials. Workflow ; Artificial neural network ; Organic molecules ; Dimensionality reduction ; Chemical space ; Metal-organic framework ; Molecule ; Graph Machine Learning in Materials Science ; Covalent Organic Framework Applications ; Synthesis and Properties of Aromatic Compounds
Authors: Junfan Xia | Yaolong Zhang | Bin Jiang
Source: ArXiv.org (preprint, 2025-02-11)
Field: Materials Science — Materials Chemistry

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting discrete ab initio data faithfully to continuous and symmetry-preserving mathematical forms, MLPs have enabled accurate and efficient atomistic simulations in a large scale from first principles. In this review, we provide an overview of the evolution of MLPs in the past two decades and focus on the state-of-the-art MLPs proposed in the last a few years for molecules, reactions, and materials. We discuss some representative applications of MLPs and the trend of developing universal potentials across a variety of systems. Finally, we outline a list of open challenges and opportunities in the development and applications of MLPs. Molecule ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Engineering physics ; Engineering ; Organic chemistry Machine Learning in Materials Science
Authors: D. Rajagopal | Adrian Cierpka | Britta Nestler | Arnd Koeppe
Source: Batteries & Supercaps (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

Generative Deep Learning for Advanced Battery Materials The development of battery materials presents a complex multiscale challenge, where optimizing the properties of battery systems across various length scales is essential for achieving targeted performance, enhanced safety, lower costs, and resource availability. Traditional methods for solving this complex, multiscale problem rely on time‐intensive trial‐and‐error approaches, which hinder progress. However, integrating advanced machine learning (ML) frameworks significantly changes the landscape of battery materials research by enabling faster discovery, predictive modeling, and optimization of material properties. Among the ML frameworks, generative deep learning (DL) models stand out, as they capture the statistics of real‐world scenarios by learning an underlying condensed representation of a higher‐dimensional input space to generate information‐rich outputs. By merging computational techniques with experimental research, generative DL provides a significant paradigm shift in analyzing battery materials. This review aims to provide valuable insights into generative models, highlighting their potential to accelerate the characterization, screening, and design of battery materials. Generative grammar ; Battery (electricity) ; Deep learning ; Representation (politics) ; Generative model ; Resource (disambiguation) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Zhihong Wu (224503) | Lei Zhou (19808) | Pengfei Hou (5308403) | Yuyan Liu (385428) | Ruixing Wang (10723770) | Taoli Guo (22669879) | Jin-Cheng Liu (2563573)
Source: Figshare (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

A Machine Learning Interatomic Potential Data Setand Model for Catalysis with Local Fine-Tuning to Chemical Accuracy Heterogenous catalysis involves complex reactions with dynamic changes in catalyst morphology, challenging the capabilities of traditional density functional theory (DFT) methods. To address this, we introduce the catalytic large atomic model (CLAM), a comprehensive data set and pretrained model for machine learning interatomic potential, specifically designed for heterogeneous catalysis. CLAM is trained on a diverse data set that includes metal and alloy slabs with adsorbates, oxides, clusters, two-dimensional materials, and small molecules, ensuring high accuracy across a wide range of catalytic systems. Additionally, we introduce a “local fine-tuning” algorithm, which substantially improves the accuracy of machine learning interatomic potentials for structural optimizations and transition state searches. This approach achieves 94% prediction accuracy within chemical accuracy thresholds (<1 kcal/mol) for adsorption energies of small adsorbates on transition metal surfaces, while demonstrating 3.4× computational acceleration relative to conventional DFT calculations. Furthermore, the method maintains comparable performance in transition state searches, delivering 81% identification accuracy (<1 kcal/mol) with a 10.1× speed-up compared to standard DFT-based CI-NEB approaches. Furthermore, the pretrained CLAM model can accurately reproduce reported dynamic catalysis phenomena via molecular dynamics simulations without additional fine-tuning. Interatomic potential ; Set (abstract data type) ; Molecular dynamics ; Range (aeronautics) ; Density functional theory ; Identification (biology) ; Catalysis ; Experimental data Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Garvit Agarwal | Rishabh D. Guha | John L. Weber | M. Mondal | Sibali Debnath | Zachary K. Goldsmith | Aidan A. Fike | James Stevenson | Leif D. Jacobson | Mathew D. Halls | Richard A. Friesner
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Scalable and Generalizable Machine Learning Force Fields for Modeling Complex Battery Materials The rapid advancements in rechargeable Li-ion battery (LIB) technology has revolutionized several key industries such as automotive and consumer electronics . However, new battery chemistries are needed to improve the power density, safety, reliability, and lifetime of LIBs. Existing classical force fields are not accurate enough to predict bulk properties of LIB materials without time-consuming and customized parametrization. To move towards accurate and reliable modeling of battery chemistries, we developed a machine-learned force field (MLFF) using a charge recursive neural network (QRNN) architecture, which includes both long-range interactions and global charge redistribution. The MLFF is trained to model a large chemical space of industrially relevant liquid electrolyte chemistries and enables large scale molecular dynamics (MD) simulations of realistic electrolyte formulations. In this presentation, I will demonstrate our generalized active learning framework and sampling workflow used to generate accurate training data for liquid and amorphous systems. I will discuss large scale benchmarks carried out to evaluate the performance of MLFF against experimental data for key physical and transport properties of liquid electrolytes including density, viscosity and ionic conductivity. Our results indicate that MLFF outperforms the classical force fields in terms of quantitative agreement with experimental data across a broad range of electrolyte chemistries. I will also discuss the novel molecular level insights into the unique Li+ cation solvation structures predicted by MLFF and their validation using experimental nuclear magnetic resonance (NMR) spectroscopy. Finally, I will briefly discuss our recent work to develop a message passing neural network architecture to train universal MLFF for inorganic materials covering up to 94 elements from the periodic table allowing for efficient design of materials for cathodes, coatings and and solid-state electrolytes for applications in next-generation batteries. Battery (electricity) ; Scalability ; Artificial neural network ; Workflow ; Field (mathematics) ; Key (lock) ; Force field (fiction) ; Chemical space ; Scale (ratio) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Gaussian Processes and Bayesian Inference
Authors: Yan Liu | Yiming Mo | Youwei Cheng
Source: Industrial & Engineering Chemistry Research (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Analysis of Chemical Reaction Networks for Gas-Phase Reaction Systems Chemical reaction networks (CRNs) serve to describe the behavior of complex chemical reaction systems. Analyzing CRNs of a reactive system requires kinetic data that are typically obtained by time-consuming experiments or computational chemistry. Machine learning (ML) has emerged as a promising approach for rapid property prediction based on historical data. However, the accuracy of ML model predictions in kinetics remains a limitation for their application in CRN analysis. In this study, we integrate the cross-attention mechanisms in neural networks and CRN sensitivity and uncertainty analysis to enable the practical application of the ML models in reliable gas-phase CRN analysis. Specifically, a message-passing neural network (MPNN) architecture along with a cross-attention mechanism (CA-MPNN) was developed for accurate prediction of the reaction rate constants with prediction uncertainty. CA-MPNN model outperformed the conventional deep neural network architectures on most of the reaction property data sets. We combined reaction network sensitivity analysis and ML prediction uncertainty analysis to identify influential reactions with high-level uncertainty of the predicted rate constant, which are further calibrated using high-accuracy quantum chemistry methods to mitigate the problem of inaccurate machine learning predictions. Compared with the traditional workflow, this framework significantly reduces up to 80% computational cost to construct a reliable CRN in the demonstrated gas-phase pyrolysis and combustion applications. Chemical reaction ; Computer science ; Gas phase ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Li‐Cheng Xu | Miao‐Jiong Tang | Jisun An | Fenglei Cao | Qi Yuan
Source: Research Square (preprint, 2025-07-02)
Field: Materials Science — Materials Chemistry

A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning Task (project management) ; Computer science ; Artificial intelligence ; Machine learning ; Deep learning ; Engineering ; Systems engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Asad Khan | Basir Akbar | chong Kil to | Hilal Tayara
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Bridging Experiment and Deep Learning: Predicting of Electronic Properties in Organic Semiconductors Using Residual-Gated Graph Neural Networks Bridging (networking) ; Residual ; Organic semiconductor ; Deep learning ; Artificial neural network ; Artificial intelligence ; Graph ; Computer science ; Semiconductor ; Residual neural network ; Materials science ; Optoelectronics ; Theoretical computer science ; Algorithm ; Computer network Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electron and X-Ray Spectroscopy Techniques
Authors: Stefan Gugler | Markus Reiher
Source: ArXiv.org (preprint, 2025-04-25)
Field: Materials Science — Materials Chemistry

Molecular Similarity in Machine Learning of Energies in Chemical Reaction Networks Machine learning has emerged as a powerful tool for predicting molecular properties in chemical reaction networks with reduced computational cost. However, accurately predicting energies of transition state (TS) structures remains a challenge due to their distinct electronic characteristics compared to stable intermediates. In this work, we investigate the limitations of structural descriptors in capturing electronic differences between minima and TS structures. We explore $Δ$-machine learning approaches to predict correlation energy corrections using both Hartree-Fock (HF) and density functional theory (DFT) as reference methods. Our results demonstrate that learning the energy difference between DFT and coupled cluster methods outperforms direct learning and HF-based $Δ$-learning. We also assess the effectiveness of combining electronic descriptors with structural ones but find that simple electronic features do not significantly enhance the prediction of TS energies. These findings highlight the need for more sophisticated descriptors or integrated approaches to accurately predict the electronic energies of TS structures within chemical reaction networks. Maxima and minima ; Similarity (geometry) ; Electronic structure ; Density functional theory ; Artificial neural network ; Energy (signal processing) ; Molecular descriptor ; Cluster (spacecraft) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Kun Tang (75641) | Yujing Zhao (10116976) | Lei Zhang (38117) | Jian Du (360004) | Qingwei Meng (312005) | Qilei Liu (7906598)
Source: Figshare (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Accelerating TransitionState Search and Ligand Screeningfor Organometallic Catalysis with Reactive Machine Learning Potential Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations. In this work, a reactive machine learning potential (RMLP) model is developed to accelerate transition state optimizations and ligand screening for organometallic catalysis based on an automated transition state database construction method and a higher-order equivariant message passing neural network. In case studies involving the ethylene hydrogenation reaction catalyzed by organometallic catalysts, RMLP rapidly predicts potential energy surfaces along intrinsic reaction coordinate paths, achieving speeds nearly 3 orders of magnitude faster than those of rigorous quantum chemistry calculations. Meanwhile, it maintains comparable accuracy with a root-mean-square deviation of 0.307 Å for transition state geometries and a mean absolute error of 0.871 kJ·mol<sup>–1</sup> for reaction barriers on the external test set, significantly outperforming semiempirical quantum chemistry methods. Our RMLP model offers an effective alternative to both rigorous and semiempirical quantum chemistry approaches for rapid and precise transition state optimizations, facilitating high-throughput screening of advanced organometallic catalyst ligands. Catalysis ; Transition state ; Transition metal ; Organometallic chemistry ; Quantum ; Group 2 organometallic chemistry ; Ligand (biochemistry) ; State (computer science) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Liu, Yujie | Wang, Zhenyu | Lei, Hang | Zhang, Guoyu | Xian, Jiawei | Gao, Zhibin | Sun, Jun | Song, Haifeng | Ding, Xiangdong
Source: arXiv (Cornell University) (preprint, 2025-11-06)
Field: Materials Science — Materials Chemistry

Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for predicting materials' electrical conductivity, thermal conductivity and mechanical properties. Traditional experimental measurement suffers from high cost and low efficiency, while theoretical simulation and graph neural network-based machine learning methods--especially crystal graph convolutional neural networks (CGCNNs)--have become effective alternatives, achieving remarkable results in predicting material elastic properties. This study trained two CGCNN models using shear modulus and bulk modulus data of 10987 materials from the Matbench v0.1 dataset, which exhibit high accuracy (mean absolute error &lt;13, coefficient of determination R-squared close to 1) and good generalization ability. Materials were screened to retain those with band gaps between 0.1-3.0 eV and exclude radioactive element-containing compounds. The final predicted dataset comprises two parts: 54359 crystal structures from the Materials Project database and 26305 crystal structures discovered by Merchant et al. (2023 Nature 624 80). Ultimately, this study completed the prediction of shear modulus and bulk modulus for 80664 inorganic crystals. This work enriches existing material elastic data resources and provides robust support for material design, with all data openly available at https://doi.org/10.57760/sciencedb.j00213.00104. Shear modulus ; Artificial neural network ; Elastic modulus ; Crystal (programming language) ; Young's modulus ; Convolutional neural network ; Bulk modulus ; Modulus ; Material properties Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials