jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ivan Žugec | Tin Hadži Veljković | M. Alducin | J. I. Juaristi
Source: Journal of Chemical Information and Modeling (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Dynamic Training Enhances Machine Learning Potentials for 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. Training (meteorology) ; Dynamics (music) ; Molecular dynamics ; Computer science ; Artificial intelligence ; Machine learning ; Psychology ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hugo Pétremand (22370906) | Julia Witte (19948112) | Oliver Kröcher (1519639) | Emanuele Moioli (5714159)
Source: Figshare (article, 2025-10-04)
Field: Materials Science — Materials Chemistry

Machine Learning-BasedKinetic Modeling of the CO<sub>2</sub> Methanation Reaction over anIndustrial Catalyst In industrial practice, it is common to use commercially available catalysts to facilitate chemical reactions. Unfortunately, due to the confidential composition and complex properties of the catalysts, a precise understanding of the reaction mechanism and intermediates is often difficult to obtain. This complicates the development of robust kinetic models, delaying an effective reactor design. This raises the question of whether machine learning (ML)-based regressions can reliably describe kinetics without requiring detailed reaction mechanisms. In this work, this research question was addressed by performing kinetic experiments on a commercial Ni/ZrO<sub>2</sub>-based CO<sub>2</sub> methanation catalyst (Himetz by Kanadevia Corporation) in an isothermal, fixed bed reactor. A 216-point data set was thus obtained at various temperatures (220–300 °C), partial pressures (0.8–3 bar) and gas hourly space velocities (50,000–700,000 h<sup>–1</sup>) combinations. The data set was employed to perform several ML-based regressions, and to compare them in terms of adequacy of the fit, according to root mean squared error (RMSE). The two best performing ML-based models, Gaussian Process Regression and 1-layer neural network were selected and assessed against two standard kinetic modeling approaches: power law and Langmuir–Hinshelwood-Hougen-Watson (LHHW). The ML-based models outperformed the power law according to RMSE and were comparable to the LHHW model to describe the kinetic regime of the catalyst. When implemented in a reactor model, the ML-based regressions also accurately predicted methane yield with results comparable to the state-of-the-art LHHW model and even outperformed the LHHW model by addressing nonideal behavior in a nonisothermal reactor. This showed that ML-based kinetics are promising in situations where little mechanistic information is available, generating models that can be employed for reactor design purposes. Methanation ; Set (abstract data type) ; Yield (engineering) ; Process (computing) ; Catalysis ; Kinetic energy ; Methane ; Work (physics) ; Data set ; Soft sensor Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Ryan J. Miller | Alexander E. Dashuta | Brayden Rudisill | David L. Van Vranken | Pierre Baldi
Source: ArXiv.org (preprint, 2025-04-22)
Field: Materials Science — Materials Chemistry

Interpretable Deep Learning for Polar Mechanistic Reaction Prediction Accurately predicting chemical reactions is essential for driving innovation in synthetic chemistry, with broad applications in medicine, manufacturing, and agriculture. At the same time, reaction prediction is a complex problem which can be both time-consuming and resource-intensive for chemists to solve. Deep learning methods offer an appealing solution by enabling high-throughput reaction prediction. However, many existing models are trained on the US Patent Office dataset and treat reactions as overall transformations: mapping reactants directly to products with limited interpretability or mechanistic insight. To address this, we introduce PMechRP (Polar Mechanistic Reaction Predictor), a system that trains machine learning models on the PMechDB dataset, which represents reactions as polar elementary steps that capture electron flow and mechanistic detail. To further expand model coverage and improve generalization, we augment PMechDB with a diverse set of combinatorially generated reactions. We train and compare a range of machine learning models, including transformer-based, graph-based, and two-step siamese architectures. Our best-performing approach was a hybrid model, which combines a 5-ensemble of Chemformer models with a two-step Siamese framework to leverage the accuracy of transformer architectures, while filtering away "alchemical" products using the two-step network predictions. For evaluation, we use a test split of the PMechDB dataset and additionally curate a human benchmark dataset consisting of complete mechanistic pathways extracted from an organic chemistry textbook. Our hybrid model achieves a top-10 accuracy of 94.9% on the PMechDB test set and a target recovery rate of 84.9% on the pathway dataset. Interpretability ; Leverage (statistics) ; Deep learning ; Test set ; Benchmark (surveying) ; Training set ; Applicability domain ; Set (abstract data type) Machine Learning in Materials Science ; Advanced Data Processing Techniques
Authors: Dobbelaere, Maarten | Kuddusi, Yasemen | Lengyel, Istvan | Stevens, Chris | Van Geem, Kevin
Source: Ghent University Academic Bibliography (Ghent University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Towards digital green chemistry and chemical reaction engineering with molecular machine learning Chemical reaction engineering ; Molecular machine ; Green chemistry ; Chemical reaction Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Chemistry and Chemical Engineering
Authors: Elaheh Kazemi-Khasragh | Rocío Mercado | Carlos González | Maciej Harańczyk
Source: ArXiv.org (preprint, 2025-09-15)
Field: Materials Science — Materials Chemistry

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning Copolymers are highly versatile materials with a vast range of possible chemical compositions. By using computational methods for property prediction, the design of copolymers can be accelerated, allowing for the prioritization of candidates with favorable properties. In this study, we utilized two distinct representations of molecular ensembles to predict the seven different physical polymer properties copolymers using machine learning: we used a random forest (RF) model to predict polymer properties from molecular descriptors, and a graph neural network (GNN) to predict the same properties from 2D polymer graphs under both a single- and multi-task setting. To train and evaluate the models, we constructed a data set from molecular dynamic simulations for 140 binary copolymers with varying monomer compositions and configurations. Our results demonstrate that descriptors-based RFs excel at predicting density and specific heat capacities at constant pressure (Cp) and volume (Cv) because these properties are strongly tied to specific molecular features captured by molecular descriptors. In contrast, graph representations better predict expansion coefficients (γ, α) and bulk modulus (K), which depend more on complex structural interactions better captured by graph-based models. This study underscores the importance of choosing appropriate representations for predicting molecular properties. Our findings demonstrate how machine learning models can expedite copolymer discovery with learnable structure-property relationships, streamlining polymer design and advancing the development of high-performance materials for diverse applications. Copolymer ; Polymer ; Artificial neural network ; Representation (politics) ; Molecular descriptor ; Set (abstract data type) ; Monomer ; Molecular dynamics ; Molecular graph Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Paola Lecca
Source: Journal of Physics Conference Series (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Guidelines to design the architecture of Artificial Neural Networks for application in physical chemistry Abstract Physical chemistry relies on physical and chemical principles, such as the conservation of mass, momentum, and energy, that are described in the mathematical structure of differential equations, whose solution simulates the behaviour of chemical processes. From a computational perspective, such differential equations present some challenges for both reaching the required level of calculation accuracy and the numerical techniques utilised to approximate them, especially in presence of stiffness and/or non-linearities. The use of deep neural network to handle these problems is gaining popularity especially in the industrial applications of physical chemistry such as processes modelling and control of chemical reactions. The paper focuses on popular systems of differential equation in chemical kinetics and, through some examples, offers guidelines for choosing a suitable artificial neural network architecture and training strategy (i.e. numbers of layers, number of neurons, activation function, and number of epochs). Indeed, the paper shows how the choice of neural network architecture for chemical engineering process modelling depends on various factors, including primarily the nature of the process under consideration, the quantity and kind of input and output variables, and, finally, the unique features of the physico-chemical process. The experiments carried out in this study were performed on a notebook MacBook Pro (Monterey 12.7.5), with a Dual-Core Intel Core i5 processor of 2.7 GHz with 1 processor, and 2 cores, just to give the reader an idea of what a user who does not have computers dedicated to artificial intelligence techniques can do. Artificial neural network ; Architecture ; Computer science ; Computer architecture ; Chemistry ; Cognitive science ; Artificial intelligence ; Psychology ; History Machine Learning in Materials Science ; Fault Detection and Control Systems ; Neural Networks and Applications
Authors: Klara Bonneau | Aldo S. Pasos-Trejo | Michael Plainer | Luca Sagresti | Jacopo Venturin | Iryna Zaporozhets | Alessandro Caruso | Edoardo Rolando | Andrea Guljas | Leon Klein | Maximilian Schebek | Filippo Albani | Raquel López-Ríos de Castro | Zakariya El Machachi | Lorenzo Giambagli | Cecilia Clementi
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Breaking the Barriers of Molecular Dynamics With Deep-Learning: Opportunities, Pitfalls, and How to Navigate Them Molecular Dynamics (MD) has established itself as a pivotal computational tool across various scientific domains, including chemistry, biology, and materials science. Despite its widespread utility, MD faces inherent challenges, such as accuracy limitations, computational speed, and sampling efficiency. In recent years, machine learning, particularly deep learning, has seen significant advancements and is increasingly being integrated into MD processes. This review explores how deep learning can mitigate the issues associated with MD by addressing them from multiple angles. However, deep learning techniques introduce their own set of hurdles, including the need for extensive data, issues of interpretability, high computational costs, and concerns regarding transferability. Here, we discuss recent progress in the field of deep learning to overcome these obstacles. Ultimately, our goal is to demonstrate that, by leveraging the advancements made in both the MD and the machine learning community, deep learning has the potential to significantly enhance the capabilities of MD, paving the way to new scientific discovery. Deep learning ; Set (abstract data type) ; Computational model ; Field (mathematics) ; Scientific field Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Carolin Mueller | Štěpán Sršeň | Brigitta Bachmair | Rachel Crespo-Otero | Jingbai Li | Sascha Mausenberger | Max Pinheiro | Graham A. Worth | Steven A. Lopez | Julia Westermayr
Source: UCL Discovery (University College London) (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Machine learning for nonadiabatic molecular dynamics: best practices and recent progress Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is crucial for designing nature-inspired functional molecules, with applications ranging from photosynthesis to pharmaceuticals. Non-adiabatic molecular dynamics simulations are powerful tools to investigate the photochemistry of molecules and materials, but demand extensive computing resources, especially for complex molecules and environments. To address these challenges, the integration of machine learning has emerged. Machine learning algorithms can be used to analyse vast datasets and accelerate discoveries by identifying relationships between geometrical features and ground as well as excited-state properties. However, challenges persist, including the acquisition of accurate excited-state data and managing the complexity of the data. This article provides an overview of recent and best practices in machine learning for non-adiabatic molecular dynamics, focusing on pre-processing, surface fitting, and post-processing of data. Molecular machine ; Best practice ; Organic molecules ; Ranging ; Excited state Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Masato Okada
Source: Science and Technology of Advanced Materials Methods (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

2024 Nobel prizes in physics and chemistry: from neural network models to materials engineering In this review, I will discuss the reasons for the 2024 Nobel Prize in Physics, the second neural network boom and its demise, which cannot be ignored, and the third neural network boom, backed by steady academic progress. In addition, I will discuss AI for Science, advocated by Demis Hassabis, winner of the Nobel Prize in Chemistry. The contributions of Japanese researchers whose work cannot be ignored will be described, and a new perspective will be presented on information creation, statistical mechanics, and data-driven science. AI for materials engineering, which is an extension of AI for science, is explained in terms of the 3 + 1 model of functional expression and the three levels of data-driven science proposed by our group. Artificial neural network ; Engineering ; Nanotechnology ; Engineering physics ; Polymer science ; Chemistry ; Computer science ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Neural Networks and Applications ; Mineral Processing and Grinding
Authors: Cheng Yan | Giulia Scalet
Source: Soft Matter (article, 2025-12-02)
Field: Materials Science — Polymers and Plastics

Bridging scales: machine learning for the rational design and modelling of shape memory polymers Shape memory polymers (SMPs) are a class of stimuli-responsive materials with significant potential across diverse fields including soft robotics, biomedical devices, and mechanical engineering. To realize a scale transition from small molecules to a mechanical structure with excellent SMP behaviour, investigators typically need both material design and constitutive model establishment. Traditionally, the design of SMPs relies on empirical methods, limiting the speed of discovery and property tuning. Moreover, the prediction of their behaviour generally depends on theoretical and numerical tools that, however, require an in-depth understanding of theoretical mechanics. In contrast, machine learning (ML) offers a powerful tool to possibly overcome these limitations and has increasingly drawn attention from investigators in multiple fields. In this perspective, we critically review recent advances in the application of ML techniques to SMPs. We discuss major conventional concepts in the field of SMPs, basic procedures and important approaches to ensure ML-assisted SMP design, how different ML tools have been employed to identify new SMP chemistries and to predict their thermo-mechanical and shape memory properties. Despite these successful advancements, ML-assisted SMP discovery and thermo-mechanical modelling remain at an early stage. We discuss how they are limited, <i>e.g.</i>, by incomplete structural representations and challenges in integrating thermal and temporal effects into the neural network. Finally, we outline future directions to explore and implement, including developing tools to capture complex SMP topologies and creating polymer-specific neural networks. The discussion allows us to provide new insights into the use of ML tools for the world of SMPs. Bridging (networking) ; Limiting ; Field (mathematics) ; Artificial neural network ; Shape-memory polymer ; Class (philosophy) ; Shape-memory alloy ; Scale (ratio) Polymer composites and self-healing ; Machine Learning in Materials Science ; Advanced Materials and Mechanics
Authors: Jiajing Li | Isaiah O. Betinol | Junshan Lai | Soresu Juyo | Jolene P. Reid
Source: ChemRxiv (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

Underexplored Catalysts as General Structures: Application of Machine Learning Techniques for Reaction-Specific Datasets The discovery of general catalysts has historically relied on determining the most widely applicable catalyst from ex-tensive experimentation. However, certain catalysts with potentially broad applicability remain underexplored due to limited reporting and small, biased datasets. In this study, we apply machine learning (ML) techniques tailored to these data-constrained environments to uncover general catalysts from limited historical data. Our approach surfaced several high-performing structures, including a rarely studied imidazolidinone bearing a benzyl-protected indole substituent. Despite minimal precedent, this scaffold showed broad generality upon experimental validation involving selected experiments from eight different reaction types. Moreover, retrospective modeling using only pre-2005 data further demonstrated that ML could have predicted the rise of now-prominent catalysts, such as diarylprolinol silyl ethers and imidazolidinones, well before their widespread adoption. These findings highlight the potential of ML to accelerate catalyst discovery, prioritize overlooked scaffolds, and compress timelines traditionally spanning decades. By integrating bias-aware modeling with focused experimentation, this strategy offers a more predictive and efficient pathway to identifying broadly effective catalysts. Computer science ; Catalysis ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Keerati Keeratikarn
Source: Spiral (Imperial College London) (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Physically informed machine learning for functional materials This thesis investigates the incorporation of physically informed machine learning techniques—particularly Gaussian Process (GP) regression and Atomic Cluster Expansion (ACE)—to effectively study and model the complex physical behaviours of functional materials. Understanding the electronic, thermal, and optoelectronic properties and applications of these materials requires accurate atomistic modelling. The atomistic modelling continues to pose computational difficulties due to the sophisticated anharmonic effects in crystalline inorganic systems and the complex electronic coupling dependencies in organic semiconductors. The first section presents a derivative Gaussian Process framework that effectively evaluates harmonic and cubic anharmonic lattice force constants directly from data obtained through density functional theory (DFT). This GP-based approach significantly improves computational efficiency, surpassing conventional finite-displacement techniques in predicting phonon dispersion, lifetimes, and thermal conductivity. Our model assesses multiple descriptor strategies, encompassing Cartesian and phonon-based representations, emphasising their influence on accuracy and computational efficiency. The second section enhances the Atomic Cluster Expansion methodology to precisely predict charge transfer integrals essential for charge transport in organic semiconductor materials. ACE models (ACEpotential.jl), employing systematically developed symmetry-adapted descriptors, effectively capture the electronic coupling's dependence on molecular orientation and separation in various molecular dimers, including naphthalene, thiophene, ethylene, and their fluorinated derivatives. Our results underscore the significance of descriptor selection and illustrate enhanced model generalisation across chemical systems. Our research collectively establishes a unified methodology that integrates rigorous physical insights with sophisticated machine learning techniques. This integration greatly enhances the computational resources for predicting material properties, facilitating efficient exploration and precise optimisation of innovative functional materials. The thesis concludes by delineating future research avenues, encompassing computational improvements, sophisticated descriptor formulation, and applications to non-equilibrium and real-time dynamic processes. Anharmonicity ; Kriging ; Gaussian process ; Density functional theory ; Coupled cluster ; Phonon ; Gaussian ; Process (computing) ; Computational model ; Coupling (piping) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Thermal properties of materials