jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kempen, Luuk H. E. | Cheula, Raffaele | Andersen, Mie
Source: ArXiv.org (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis? Foundational machine learning interatomic potentials (MLIPs) are being developed at a rapid pace, promising closer and closer approximation to ab initio accuracy. This unlocks the possibility to simulate much larger length and time scales. However, benchmarks for these MLIPs are usually limited to ordered, crystalline and bulk materials. Hence, reported performance does not necessarily accurately reflect MLIP performance in real applications such as heterogeneous catalysis. Here, we systematically analyze zero-shot performance of 80 different MLIPs, evaluating tasks typical for heterogeneous catalysis across a range of different data sets, including adsorption and reaction on surfaces of alloyed metals, oxides, and metal-oxide interfacial systems. We demonstrate that current-generation foundational MLIPs can already perform at high accuracy for applications such as predicting vacancy formation energies of perovskite oxides or zero-point energies of supported nanoclusters. However, limitations also exist. We find that many MLIPs catastrophically fail when applied to magnetic materials, and structure relaxation in the MLIP generally increases the energy prediction error compared to single-point evaluation of a previously optimized structure. Comparing low-cost task-specific models to foundational MLIPs, we highlight some core differences between these model approaches and show that -- if considering only accuracy -- these models can compete with the current generation of best-performing MLIPs. Furthermore, we show that no single MLIP universally performs best, requiring users to investigate MLIP suitability for their desired application. Interatomic potential ; Ab initio ; Range (aeronautics) ; Energy (signal processing) ; Experimental data ; Relaxation (psychology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Manuel Grumet | Takeru Miyagawa | Olivier Pittet | Paolo Pegolo | Karin S. Thalmann | Waldemar Kaiser | David A. Egger
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport can disrupt crystalline symmetry, thereby lifting Raman selection rules. Here, we exploit the resulting low-frequency, diffusive Raman scattering as a spectral signature of fast ionic conduction and develop a machine learning-accelerated computational pipeline to identify promising solid electrolytes based on this feature. By overcoming the steep computational barriers to calculating Raman spectra of strongly disordered materials at finite temperatures, we achieve near-ab initio accuracy and demonstrate the predictive power of our approach for sodium-ion conductors, revealing clear Raman signatures of liquid-like ion conduction. This work highlights how machine learning can bridge atomistic simulations and experimental observables, enabling data-efficient discovery of fast-ion conductors. Raman spectroscopy ; Raman scattering ; Ionic bonding ; Fast ion conductor ; Ionic conductivity ; Thermal conduction ; Work (physics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Baoming Shi | Apala Majumdar | Lei Zhang
Source: Physical review. E (article, 2025-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network-based tensor models for liquid crystals with molecular-level information The phenomenological Landau-de Gennes (LdG) model is a powerful continuum theory to describe macroscopic liquid crystal (LC) phases. However, it is invariably less accurate and less physically informed than molecular-level models. We propose a neural network-based tensor (NN-tensor) model for LCs, supervised by an underlying molecular model. Our NN-tensor model not only attains energy precision comparable to the molecular model, but it also accurately captures the isotropic-nematic phase transition, which the LdG model cannot achieve. By embedding the NN-tensor model within a second neural network, we can efficiently compute stable LC configurations in a domain-free and mesh-free manner. We validate this approach with multiple examples for nematic LCs, demonstrating its ability to find physically relevant nematic configurations in diverse scenarios. We further apply the NN-tensor model to the more complex smectic LC phase. Strikingly, the NN-tensor model can quantitatively predict the smectic layer thickness and capture intricate microstructures such as Omega and T-shaped grain boundaries-features that current conventional approaches fail to resolve. These results demonstrate that the NN-tensor framework is a unified, efficient, and physically faithful route for computing rich LC configurations across multiple phases. Liquid crystal ; Tensor (intrinsic definition) ; Embedding ; Artificial neural network ; Phenomenological model ; Phase (matter) ; Energy (signal processing) Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Material Dynamics and Properties
Authors: Eduardo Aguilar-Bejarano | Viraj Deorukhkar | Simon Woodward
Source: Molecules (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach The range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/checking of such sets time consuming; thus, automated tools to help this process are highly desirable. Herein, we propose the use of Graph Neural Networks (GNNs) to identifying potential stereochemical misassignments in the primary asymmetric catalysis literature. Our method relies on the use of an ensemble of GNN models to predict the expected stereoselectivity of exemplars for a particular asymmetric reaction. When the majority of these models do not correlate to the reported outcome, the point is labeled as a possible stereochemical misassignment. Such identified cases are few in number and more easily investigated for their cause. We demonstrate the use of this approach to spot potential literature stereochemical misassignments in the ketone products resulting from catalytic asymmetric 1,4-addition of organoboron nucleophiles to Michael acceptors in two different databases, each one using a different family of chiral ligands (bisphosphine and diene ligands). Our results demonstrate that this methodology is useful for curation of medium-sized databases, speeding this process significantly compared to complete manual curation/checking. In the datasets investigated, human expert checking was reduced to 2.2% and 3.5% of the total data exemplars. Computer science ; Nucleophile ; Artificial intelligence ; Chemistry ; Data mining ; Machine learning ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Ivan Žugec (18538274) | Tin Hadži Veljković (21759895) | Maite Alducin (1302213) | J. Iñaki Juaristi (1302447)
Source: Figshare (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Dynamic TrainingEnhances Machine Learning Potentialsfor Long-Lasting Molecular Dynamics Molecular dynamics (MD) simulations are vital for exploring complex systems in computational physics and chemistry. While machine learning methods dramatically reduce computational costs relative to ab initio methods, their accuracy in long-lasting simulations remains limited. Here we propose dynamic training (DT), a method designed to enhance accuracy of a model over extended MD simulations. Applying DT to an equivariant graph neural network (EGNN) on the challenging system of a hydrogen molecule interacting with a palladium cluster anchored to a graphene vacancy demonstrates a superior prediction accuracy compared to conventional approaches. Crucially, the DT architecture-independent design ensures its applicability across diverse machine learning potentials, making it a practical tool for advancing MD simulations. Artificial neural network ; Molecular dynamics ; Ab initio ; Graph ; Training (meteorology) ; System dynamics ; Complex system ; Dynamics (music) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Akira Takahashi | Yu Kumagai | Atsuko Takamatsu | Fumiyasu Oba
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Extraction of Promising Material Groups and Common Features from High-Dimensional Data: A Case of Optical Spectra of Inorganic Crystals We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into classes based on similarities in both spectral data and chemical characteristics such as elemental composition and atomic arrangement. As a demonstration, we apply this method to an atomistic line graph neural network (ALIGNN) model trained on first-principles calculation data of 2,681 metal oxides, chalcogenides, and related compounds for optical absorption spectrum prediction. Our analysis reveals key elemental species and their coordination environments that influence optical absorption onset characteristics. The method proposed herein is broadly applicable to the classification and interpretation of diverse spectral data, extending beyond the optical absorption spectra of inorganic crystals. Spectral line ; Categorization ; Interpretation (philosophy) ; Absorption (acoustics) ; Absorption spectroscopy ; Graph ; Extraction (chemistry) ; Cluster analysis ; Metal Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Wanjun Zhao | Zhitao He | Zhizhen Wu | Yi Zhou | Zhao Ding | Hongxiang Chen | Yang Zhou
Source: ACS Applied Polymer Materials (article, 2025-11-05)
Field: Materials Science — Polymers and Plastics

Integrating SHAP Interpretability and Ensemble Machine Learning To Optimize Conductivity in Flexible Electronic Polymers Conductive polymers are essential materials for flexible electronics and energy storage, yet their electrical performance is highly sensitive to multivariate synthesis parameters that are difficult to optimize experimentally. Here, we develop a machine learning framework that accurately predicts polymer conductivity based on nine key synthetic variables. Among five tested algorithms, EXtreme Gradient Boosting (XGBoost) achieves the highest predictive accuracy (test R2 = 0.933), while Light Gradient Boosting Machine (LightGBM) shows strong performance with minimal overfitting. SHapley Additive exPlanations (SHAP) analysis reveals that dopant species is the dominant determinant of conductivity (59.85%), followed by oxidant species (17.5%) and polymer type (8.5%). The model further uncovers critical nonlinear relationships, including conductivity maximization at 30–60 °C, a sharp decline beyond a 2 mol/L dopant concentration, and negligible dependence on reaction duration after equilibrium. Notably, optimal conductivity arises under synergistic conditions of elevated reaction temperature (>80 °C) and low oxidant concentration (<0.2 mol/L), which promote molecular alignment and enhance charge carrier mobility. Our findings highlight that dopant-driven acid–base doping strength, rather than classical acid–base doping strength, serves as the primary factor controlling carrier transport in conductive polymers. This insight provides the design principles for organic electronic materials and demonstrates how machine learning can accelerate materials discovery and optimization. Interpretability ; Conductivity ; Dopant ; Conductive polymer ; Boosting (machine learning) ; Doping ; Electrical conductor ; Gradient boosting Conducting polymers and applications ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Jiangcheng Xu | Jun Dong | Kui Du | Wenwen Liu | Jiehai Peng | Wenbo Yu
Source: Processes (article, 2025-06-05)
Field: Chemistry — Organic Chemistry

RadicalRetro: A Deep Learning-Based Retrosynthesis Model for Radical Reactions With the rapid development of radical initiation technologies such as photocatalysis and electrocatalysis, radical reactions have become an increasingly attractive approach for constructing target molecules. However, designing efficient synthetic routes using radical reactions remains a significant challenge due to the inherent complexity and instability of radical intermediates. While computer-aided synthesis planning (CASP) has advanced retrosynthetic analysis for polar reactions, radical reactions have been largely overlooked in AI-driven approaches. In this study, we introduce RadicalRetro, the first deep learning-based retrosynthesis model specifically tailored for radical reactions. Our work is distinguished by three key contributions: (1) RadicalDB: A novel, manually curated database of 21.6 K radical reactions, focusing on high-impact literature and mechanistic clarity, addressing the critical gap in dedicated radical reaction datasets. (2) Model Innovation: By pretraining Chemformer on ZINC-15 and USPTO datasets followed by fine-tuning with RadicalDB, RadicalRetro achieves a Top-1 accuracy of 69.3% in radical retrosynthesis, surpassing the state-of-the-art models LocalRetro and Mol-Transformer by 23.0% and 25.4%, respectively. (3) Interpretability and Practical Utility: Attention weight analysis and case studies demonstrate that RadicalRetro effectively captures radical reaction patterns (e.g., cascade cyclizations and photocatalytic steps) and proposes synthetically viable routes, such as streamlined pathways for Tamoxifen precursors and glycoside derivatives. RadicalRetro’s performance highlights its potential to transform radical-based synthetic planning, offering chemists a robust tool to leverage the unique advantages of radical chemistry in drug synthesis. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Chemistry ; Stereochemistry Radical Photochemical Reactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: L. Kempen | Marius Juul Nielsen | Mie Andersen
Source: ACS Catalysis (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Breaking Scaling Relations with Inverse Catalysts: A Machine Learning Exploration of Trends in CO <sub>2</sub> -to-Formate Energy Barriers The conversion of CO2 into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing improved catalysts for this process is costly and time-consuming and can thus benefit from computational exploration of possible active sites. However, this is complicated by the complexity of the materials and reaction networks. Here, we present a workflow for exploring transition states of elementary reaction steps at inverse catalysts, which is based on the training of a neural network-based machine learning interatomic potential. We focus on the crucial formate intermediate and its formation over nanoclusters of indium oxide supported on Cu(111). The speedup compared to an approach purely based on density functional theory allows us to probe a wide variety of active sites found at nanoclusters of different sizes and stoichiometries. Analysis of the obtained set of transition state geometries reveals different structure–activity trends at the edge or interior of the nanoclusters. Furthermore, the identified geometries allow for the breaking of linear scaling relations, which could be a key underlying reason for the catalytic performance of inverse catalysts observed in experiments. Nanoclusters ; Inverse ; Workflow ; Scaling ; Density functional theory ; Speedup ; Inverse problem ; Catalysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Juliette Fenogli | Laurence Grimaud | Rodolphe Vuilleumier
Source: arXiv (Cornell University) (preprint, 2025-01-02)
Field: Chemistry — Physical and Theoretical Chemistry

Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids The integration of machine learning (ML) into chemistry offers transformative potential in the design of molecules with targeted properties. However, the focus has often been on creating highly efficient predictive models, sometimes at the expense of interpretability. In this study, we leverage explainable AI techniques to explore the rational design of boron-based Lewis acids, which play a pivotal role in organic reactions due to their electron-ccepting properties. Using Fluoride Ion Affinity as a proxy for Lewis acidity, we developed interpretable ML models based on chemically meaningful descriptors, including ab initio computed features and substituent-based parameters derived from the Hammett linear free-energy relationship. By constraining the chemical space to well-defined molecular scaffolds, we achieved highly accurate predictions (mean absolute error &lt; 6 kJ/mol), surpassing conventional black-box deep learning models in low-data regimes. Interpretability analyses of the models shed light on the origin of Lewis acidity in these compounds and identified actionable levers to modulate it through the nature and positioning of substituents on the molecular scaffold. This work bridges ML and chemist's way of thinking, demonstrating how explainable models can inspire molecular design and enhance scientific understanding of chemical reactivity. Boron ; Lewis acids and bases ; Chemistry ; Computer science ; Machine learning ; Artificial intelligence ; Organic chemistry ; Catalysis Various Chemistry Research Topics ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xiulin Wang | Suofu Nie | Huichao Yao | Sida Wu | Yanze Li | Junli Feng | Yanping Sui | Yuqing Zhang | Xinwei Wang | Xiuxia Zhang
Source: Molecules (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted DFT Screening of Nitrogen-Doped Graphene Diatomic Catalysts for Nitrogen Reduction Reaction This research seeks to investigate extremely efficient catalysts for the nitrogen reduction process (NRR), utilizing machine learning (ML)-aided density functional theory (DFT) computations. Specifically, we investigate dual transition metal atoms anchored on hexagonal nitrogen-doped graphene (TM<sub>1</sub>-TM<sub>2</sub>@N<sub>6</sub>G) as prospective high-activity catalysts for the NRR. The findings indicate that the synergistic effect of dual transition metal atoms in the TM<sub>1</sub>-TM<sub>2</sub>@N<sub>6</sub>G catalyst overcomes the intrinsic constraints of the linear relationship among intermediates, facilitating the activation and adsorption of N<sub>2</sub>, thereby exhibiting significant potential for ammonia synthesis through N<sub>2</sub> reduction. Particularly, four catalysts screened by ML and DFT exhibit good stability and excellent selectivity and activation towards N<sub>2</sub>. Among them, the catalysts Ti-Cr@N<sub>6</sub>G, Ti-Mo@N<sub>6</sub>G, and Ti-Pd@N<sub>6</sub>G possess two reaction pathways with minimum reaction energies of 0.55 eV, 0.50 eV, and 0.40 eV, respectively. Remarkably, Ti-Co@N<sub>6</sub>G, which features a single reaction pathway, exhibits a reaction energy lower than 0.05 eV, allowing the NRR to proceed spontaneously. It is noteworthy that incorporating ML into DFT calculations facilitates the rapid screening of all transition metal combinations, significantly accelerating the research on catalytic performance and optimizing the selection of catalysts. Catalysis ; Density functional theory ; Graphene ; Transition metal ; Selectivity ; Adsorption ; Activation energy ; Metal ; Reaction mechanism Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction
Authors: Do Hung Manh | Sang Uck Lee
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Local Environment-Aware Prediction of Catalytic Activity for the Hydrogen Evolution Reaction in Alloy Nanoparticles Via Machine Learning Interatomic Potentials Alloy ; Nanoparticle ; Catalysis ; Hydrogen ; Materials science ; Chemical engineering ; Nanotechnology ; Computer science ; Chemical physics ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Kazuki Ishizaki | Toru Asahi | Takuya Taniguchi
Source: ChemRxiv (preprint, 2025-01-29)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Optimization of Output Force in Photo-Actuated Organic Crystals Photo-actuated organic crystals, which can be remotely controlled by light, are gaining attention as next-generation actuator materials. In the practical application of actuator materials, not only the mode of deformation but also the output force is an important property. Since the output force depends on both the crystal properties and experimental conditions, it is necessary to explore the optimal conditions from a vast parameter space. In this study, we employed two types of machine learning for molecular design and experimental optimization, successfully maximizing the blocking force. Machine learning in molecular design led to the creation of a material pool of salicylideneamine derivatives. Bayesian optimization was used for efficient sampling from the material pool for force measurements, achieving a maximum blocking force of 37.0 mN. It was estimated that this method was at least 73 times more efficient than the grid search approach. Materials science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization
Authors: Ian Tong Pan | Joseph D. Romano
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Chemistry — Physical and Theoretical Chemistry

Enhancing Molecular Representation Learning Through the Combination of 3D and 2D Graph Machine Learning (Student Abstract) Molecular machine learning has broad applications across multiple domains such as drug development, environmental toxicology, and materials science. Various pre-trained frameworks using self-supervised representation learning have emerged to tackle the difficulty of obtaining large molecular datasets useful for training high-performing molecular machine learning models. In this study, we explore a novel representation learning framework trained using both 2D and 3D molecular data. Specifically, a 3D invariant graph neural network to learn how to capture 3D atomic information and then pass these atomic representations into a regular 2D graph neural network which can leverage molecular topology. Results from experiments demonstrate the representations produced by our method using both 3D and 2D molecular information lead to strong performance in downstream tasks. Computer science ; Graph ; Representation (politics) ; Artificial intelligence ; Mathematics education ; Machine learning ; Theoretical computer science ; Psychology Various Chemistry Research Topics