jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas Gasevic (9774408) | Marcel Müller (1548316) | Jonathan Schöps (22168081) | Stephanie Lanius (22168084) | Jan Hermann (3345281) | Stefan Grimme (1321575) | Andreas Hansen (1512838)
Source: Figshare (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Chemical SpaceExploration with Artificial “Mindless”Molecules We introduce MindlessGen, a Python-based generator for creating chemically diverse, “mindless” molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the <i>MB2061</i> benchmark set, containing 2061 molecules with high-level PNO-LCCSD(T)-F12 reference data for H<sub>2</sub>-promoted decomposition reactions. This set provides a challenging benchmark for testing, validating, and training density functional approximations (DFAs), semiempirical methods, force fields, and machine learning potentials using molecular structures beyond conventional chemical space. For DFAs, we initially hypothesized that highly parametrized functionals might perform poorly on this set. However, no consistent relationship between the fitting strategy and accuracy was observed. A clear Jacob’s ladder trend emerges, with ωB97X-2 achieving the lowest mean absolute error (MAE) of 8.4 kcal·mol<sup>–1</sup> and r<sup>2</sup>SCAN-3c offering a robust cost-efficient alternative (19.6 kcal·mol<sup>–1</sup>). Furthermore, we discuss the performance of selected semiempirical methods and contemporary machine-learning interatomic potentials. Benchmark (surveying) ; Chemical space ; Set (abstract data type) ; Molecule ; Generator (circuit theory) ; Decomposition ; Space (punctuation) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Kenji HAYASHIDA | Nobutoshi SUDOH
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Enhancing the Accuracy and Stability of Surrogate Models Using Fully Connected Neural Networks Numerical Analysis of the process for single crystal growths is high-cost computing due to the coupled heat conduction, flow, and chemical reaction calculations. Therefore, fast and highly accurate surrogate models are demanding. This study targets the high precision and stability of the surrogate models for the heat conduction calculations. We used the fully connected neural networks, and explored hyper-parameters. The activation function is a key parameter to enhance the accuracy of the surrogate models, and softplus function shows the higher precision than the standard relu function. The high accuracy is reproduced for some regression targets, and the softplus function can also generate the stable precision. Surrogate model ; Stability (learning theory) ; Artificial neural network ; Function (biology) ; Process (computing) ; Surrogate data ; Thermal conduction ; Function approximation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Phase Equilibria and Thermodynamics
Authors: Paolo Sortino | Salvatore Contino | Federica Villa | Luca Cruciata | Roberto Pirrone
Source:  (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Exploiting Double Representation and Gated Multi-Head Cross-Attention for Polymers Properties Prediction The accurate prediction of chemical properties in polymers is a significant challenge in Materials Science, often hindered by the complexity of polymer structures and the limitations of traditional modeling approaches that rely on classical string notations like P-SMILES. In this respect, a multimodal approach where also structural information of polymers is taken into account, can lead to improved performance. The present work tackles this challenge with a novel neural framework that integrates a graph-based embedding into the string embedding of a Transformer neural network, aiming at performance improvement by leveraging structural information provided by the graph representation. We called our approach Double Representation (DR) and in this work we explore the relationships between string- and graph-based polymer description by evaluating two distinct Transformer architectures on different polymer data sets that are reported in the literature. Our results reveal that the DR approach markedly improved the performance of the ChemBERTa model across various polymer properties. The introduction of a particular Gated Multi-Head Cross Attention operation to combine the two embeddings has improved the results by leveraging the strengths and advantages of both representations. These findings not only contribute to the advancement of predictive modeling in Polymer Science but also have significant implications for material design and development, setting the stage for future research aimed at refining property prediction methodologies through innovative data integration techniques. Embedding ; Representation (politics) ; Notation ; Transformer ; Graph ; Polymer ; External Data Representation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: Mariana Rossi | Kevin Rossi | Alan M. Lewis | Mathieu Salanne | Andrea Grisafi
Source: ArXiv.org (preprint, 2025-01-19)
Field: Materials Science — Surfaces, Coatings and Films

Learning the Electrostatic Response of the Electron Density through a Symmetry-Adapted Vector Field Model A current challenge in atomistic machine learning is that of efficiently predicting the response of the electron density under electric fields. We address this challenge with symmetry-adapted kernel functions that are specifically derived to account for the rotational symmetry of a three-dimensional vector field. We demonstrate the equivariance of the method on a set of rotated water molecules and show its high efficiency with respect to number of training configurations and features for liquid water and naphthalene crystals. We conclude showcasing applications for relaxed configurations of gold nanoparticles, reproducing the scaling law of the electronic polarizability with size, up to systems with more than 2000 atoms. By deriving a natural extension to equivariant learning models of the electron density, our method provides an accurate and inexpensive strategy to predict the electrostatic response of molecules and materials. Symmetry (geometry) ; Physics ; Field (mathematics) ; Electron ; Quantum electrodynamics ; Theoretical physics ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Geometry ; Pure mathematics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electrochemical Analysis and Applications
Authors: Chuandong Ye | Changhe Zhou
Source:  (article, 2025-07-08)
Field: Materials Science — Surfaces, Coatings and Films

Efficient grating design using deep learning and numerical optimization Gratings have important applications in spectral analysis, communication, laser technology, biomedicine, and materials science, and their design and optimization are crucial for enhancing performance. Traditional design methods are complex and time-consuming. This paper proposes a novel grating design and optimization method based on deep learning to improve efficiency and performance. By digitizing the grating structural parameters, a high-quality dataset is constructed. A backpropagation neural network (BPNN) is then used to train the dataset, capturing the complex nonlinear relationship between the grating structural parameters and their performance. Based on the trained BPNN model, inverse design is achieved using an Inverse BPNN, which can directly derive the optimal grating structural parameters according to the desired performance indicators. Experimental results show that this deep learning-based method achieves significant results in grating design and optimization. For example, for one-dimensional surface slanted gratings under green light, the highest diffraction efficiency is improved to 96.27%. This achievement not only verifies the effectiveness and superiority of the method but also demonstrates the great potential of deep learning in optical device design. Grating ; Artificial neural network ; Backpropagation ; Deep learning ; Inverse ; Diffraction grating ; Inverse problem Optical Coatings and Gratings ; Metamaterials and Metasurfaces Applications ; Advanced optical system design
Authors: Xiongjun Fu | Xiaogang Cheng | Yugang Chen | Haopeng Wang | Lizhu Dong | Xinyao Ge | Yongxia Wang | Ying Wei | Linghai Xie
Source: Precision Chemistry (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

MODNet: A Monocular Object-Based Depth Estimation Network for AI Robotic Chemists With the rapid development of AI chemists, the application of machine-learning methods has brought transformative changes to the field of chemistry. Computer vision technology is also gradually becoming a critical sensory input for AI-driven chemists. However, for a complete AI chemist platform, intelligent robotic arm operation is essential. Our work focuses on researching machine vision-assisted intelligent robotic arm operations for AI chemists. Based on the CViG_II data set, this paper proposes a chemical apparatus object detection and distance estimation model called MODNet. Compared to the original YOLOv8 algorithm, the proposed method improves experimental accuracy to 95.8%, effectively achieving high-precision detection of chemical apparatus. Additionally, the model combined a monocular distance measurement method is used to detect the real distance of the target, with an error margin of less than 5% and real-time inference frame rate greater than 60 fps, enabling rapid and accurate object detection and distance detection in video streams. Moreover, we attempted to utilize the MODNet auxiliary system to provide real-time guidance for robotic arm grasping operations in chemical laboratory environments. Specifically, we conducted validation experiments on unit operations within the Spiro [fluorene-9,9'-xanthene] (SFX) synthesis process, such as chemical reagent preparation procedures. This provides a vision-assisted solution for autonomous chemical synthesis by AI robotic chemists. Monocular ; Margin (machine learning) ; Robotic arm ; Field (mathematics) ; Robotics ; Object detection ; Object (grammar) ; Robot Machine Learning in Materials Science ; Advanced Neural Network Applications ; Robot Manipulation and Learning
Authors: Anna Kapeliukha (20466532) | Serhii Hlotov (22318107) | Mykola Protopopov (21577274) | Igor Dzyuba (22318110) | Maryna Vasylchuk (22318113) | Dmitriy M. Panov (1768396) | Olga O. Tarkhanova (16699810) | Yurii S. Moroz (119826)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Freedom Space 3.0:ML-Assisted Selection of SyntheticallyAccessible Small Molecules Advances in machine learning (ML) have revolutionized the exploration of chemical space, enabling the creation of subsets tailored for specific applications. Herein, we describe the development of Chemspace Freedom Space 3.0, a chemical library of synthetically accessible small molecules derived from ML-based filtering of building blocks. Our approach employs a model trained on a custom molecular representation to refine the selection of building blocks prior to enumeration, enhancing the quality and synthetic feasibility of the derived molecules. Freedom Space 3.0 comprises 5 billion molecules, generated using ten well-validated chemical transformations, and is complementary to Enamine REAL Space. We computationally evaluate the physicochemical properties, chemical diversity, and synthetic accessibility of the molecules from Freedom Space 3.0. Furthermore, experimental validation demonstrates a success rate of over 80% within a 4–6 week synthesis on a set of 700 molecules, proving the potential for Freedom Space 3.0 to accelerate hit finding and hit follow-up workflows. Chemical space ; Degrees of freedom (physics and chemistry) ; Space (punctuation) ; Representation (politics) ; Set (abstract data type) ; Selection (genetic algorithm) ; Quality (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Xun Fu (2081062) | Xiaogang Cheng (8166234) | Yugang Chen (2358241) | Haopeng Wang (1770376) | Lizhu Dong (20723609) | Xinyao Ge (22765475) | Yongxia Wang (436910) | Ying Wei (214901) | Linghai Xie (1550764)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

MODNet: A Monocular Object-Based Depth EstimationNetwork for AI Robotic Chemists With the rapid development of AI chemists, the application of machine-learning methods has brought transformative changes to the field of chemistry. Computer vision technology is also gradually becoming a critical sensory input for AI-driven chemists. However, for a complete AI chemist platform, intelligent robotic arm operation is essential. Our work focuses on researching machine vision-assisted intelligent robotic arm operations for AI chemists. Based on the CViG_II data set, this paper proposes a chemical apparatus object detection and distance estimation model called MODNet. Compared to the original YOLOv8 algorithm, the proposed method improves experimental accuracy to 95.8%, effectively achieving high-precision detection of chemical apparatus. Additionally, the model combined a monocular distance measurement method is used to detect the real distance of the target, with an error margin of less than 5% and real-time inference frame rate greater than 60 fps, enabling rapid and accurate object detection and distance detection in video streams. Moreover, we attempted to utilize the MODNet auxiliary system to provide real-time guidance for robotic arm grasping operations in chemical laboratory environments. Specifically, we conducted validation experiments on unit operations within the Spiro [fluorene-9,9′-xanthene] (SFX) synthesis process, such as chemical reagent preparation procedures. This provides a vision-assisted solution for autonomous chemical synthesis by AI robotic chemists. Monocular ; Margin (machine learning) ; Robotic arm ; Field (mathematics) ; Robotics ; Object detection ; Object (grammar) ; Robot Machine Learning in Materials Science ; Advanced Neural Network Applications ; Robot Manipulation and Learning
Authors: Lejie Tian | Jianxi Liu | Christof Wöll
Source: KITopen (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Surface-anchored, oriented, monolithic Metal–Organic framework thin films: Surface and interface phenomena in crystalline MOF architectures Thin film ; Characterization (materials science) ; Modular design ; Coupling (piping) ; Epitaxy ; Interface (matter) ; Surface energy ; Rational design Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies
Authors: L.V. Krasnov | D. A. Malikov | М. И. Киселева | Sergei V. Tatarin | Stanislav I. Bezzubov
Source: ChemRxiv (preprint, 2025-04-02)
Field: Chemistry — Spectroscopy

BigSolDB 2.0: a dataset of solubility values for organic compounds in organic solvents and water at various temperatures Solubility is one of the key properties of organic compounds that determines their applications in chemistry, materials science and pharmaceuticals. However, predicting solubility values in any solvent except water from a molecular structure still remains a challenging task in modern cheminformatics, not least due to the lack of large and diverse datasets. In this study, we present a dataset containing 103944 experimental solubility values within a temperature range from 243 to 425 K for 1448 organic compounds measured in 213 individual solvents extracted from 1595 peer-reviewed articles. The molecular structures of solutes and solvents as well as solubility data are standardized and provided in a machine-readable format, allowing straightforward data-driven analysis. We have also developed a web-tool for interactive visualization and search within the dataset. This dataset can serve as a comprehensive benchmark for developing machine learning for predicting solubility. Solubility ; Organic solvent ; Environmental chemistry ; Chemistry ; Organic chemistry ; Environmental science ; Chemical engineering ; Engineering Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Chenyi Zhou | Rubin Huo | Boyi Situ | Zihan Yan | Zhe Zhang | Yusong Tu
Source: ArXiv.org (preprint, 2025-03-06)
Field: Materials Science — Materials Chemistry

Unveiling the Oxidation Mechanisms of Octa-Penta Graphene: A Multidimensional Exploration from First-Principles to Machine Learning Octa-penta graphene (OPG), a novel carbon allotrope characterized by its distinctive arrangement of pentagonal and octagonal rings, has garnered considerable attention due to its exceptional structure and functional properties. This study systematically investigates the oxidation mechanisms of OPG and elucidates the oxygen migration patterns on the OPG monolayer through first-principles calculations and machine-learning-based molecular dynamics (MLMD) simulations. Specifically, the oxidation processes on OPG-L and OPG-Z involve exothermic chemisorption, where oxygen molecules dissociate at the surfaces, forming stable epoxy groups. Furthermore, the integrated-crystal orbital Hamilton population (ICOHP) and Bader charge analyses provide insights into the physical mechanisms of oxygen atom adsorption. Importantly, we found that oxidation also impact the electronic properties of OPG, with OPG-L retaining its metallic characteristics post-oxygen adsorption, whereas OPG-Z undergoes a transformation from a metallic to a semiconducting state due to the introduction of oxygen. Oxygen migration on OPG monolayer involves breaking and reforming of C-O bonds, with varying stability across adsorption sites and limited migration along the basal plane. MLMD simulations corroborate these migration patterns, offering detailed migration trajectories consistent with theoretical predictions. These findings enhance the understanding of oxygen migration dynamics on OPG, facilitate its experimental validations, and highlight its potential as a novel 2D material for applications in batteries, heat-resistant materials, and oxidation-resistant coatings. Monolayer ; Oxygen ; Graphene ; Population ; Molecular dynamics ; Stability (learning theory) ; Metal ; Electronic structure ; Atom (system on chip) Graphene research and applications ; Electrocatalysts for Energy Conversion ; 2D Materials and Applications
Authors: Feifan Yin | Yiming Zhang | Zefang Wang | Xin Zhong | Hanyu Liu
Source: Superconductivity (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Concurrent superconductivity and ultrahardness in pressure-induced BC19 Conventional hard and superhard materials, such as diamond and cubic boron nitride, are attractive for both scientific and industrial applications, but their intrinsically poor electrical conductivity limits broader use. This motivates the exploration of novel materials that combine superior hardness with excellent superconductivity. Herein, we performed a comprehensive structure search of the B–C system under pressures ranging from 0 to 100 GPa using machine-learning-potential (attention-coupled neural network, ACNN) based crystal structure prediction (CALYPSO). A stable BC 19 phase was identified at 50 GPa, featuring a diamond-like covalent framework with metallic character. Interestingly, BC 19 shows anisotropic superconductivity with an estimated superconducting critical temperature ( T c ) of 24 K at ambient pressure. Further analysis indicates that the high superconductivity of BC 19 originates from the strong coupling between the σ electrons and stretching vibrations of the covalent B-C framework. Additionally, BC 19 demonstrates superhard characteristics with a Vickers hardness of 76 GPa, exceeding that of cubic boron nitride. These results suggest that pressure-stabilized BC 19 represents a promising theoretical candidate for concurrent superconductivity and ultrahard mechanical performance. Superconductivity ; Diamond ; Anisotropy ; Vickers hardness test ; Electrical resistivity and conductivity ; Coupling (piping) ; Boron ; Phase (matter) ; Boride Boron and Carbon Nanomaterials Research ; Superconductivity in MgB2 and Alloys ; Diamond and Carbon-based Materials Research
Authors: Jakob Blahusch | Konstantin S. Jakob | Johannes T. Margraf | Karsten Reuter | Bettina V. Lotsch
Source: ChemRxiv (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

Predict Before You Precipitate: Learning Templating Effects in HOI Antimony and Bismuth Halides Hybrid organic-inorganic (HOI) antimony and bismuth halides exhibit diverse structural features and have been studied intensely for their promising electronic and optical properties. There are well-explored structure-property relations for these materials. However, a thorough understanding of synthesis routes and templating effects is lacking, turning their targeted synthesis into an open challenge. In this study, we assemble a literature data set of established HOI material candidates and train an explainable machine learning (ML) classification model to explore the templating effects in more detail. With a classification accuracy upwards of 70%, our model is effective in predicting HOI structure types based on the reactants and points out several structural and electrostatic design features for the organic cation that influence the inorganic substructure most strongly. We further demonstrate the validity of our classifier on 9 newly synthesized members of this materials class and propose incremental learning routes to expand the model in future research. Bismuth ; Antimony ; Halide ; Materials science ; Psychology ; Chemistry ; Inorganic chemistry ; Metallurgy Machine Learning in Materials Science
Authors: Shriti Dham
Source:  (preprint, 2025-06-29)
Field: Materials Science — Polymers and Plastics

Feasibility of Utilizing Machine Learning to Identify a More Sustainable Alternative to Polyester in Textiles The mass production of textiles has created several concerns for the environment and its future, with the increase of greenhouse gases that are released during the production of Polyester.Machine learning is a recent technology commonly used in polymer science to consider vast amounts of data, relatively quickly.The aim of the project is to find an alternative polymer that possesses similar characteristics to polyester with a considerably lower environmental impact.In this study, we used a publicly available dataset, PI1M, containing information on over one million polymers to train artificial neural net.We looked into predicting key features for these polymers, including glass transition temperature, density, melting temperature, oxygen permeability, compressibility, and bulk modulus.Through the process of machine learning modeling, several properties of new polymers were successfully predicted.An agreement was found between the true and predicted values for each of these features.The results show the presence of a possibly more sustainable alternative to Polyester in textiles.This study demonstrates the feasibility of using machine learning to discover new, more sustainable polymers. Polyester ; Computer science ; Engineering ; Business ; Materials science ; Composite material Textile materials and evaluations