jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rafał Stottko | Radosław Michalski | Bartłomiej M. Szyja
Source: Journal of Chemical Theory and Computation (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

RGBChem: Image-Like Representation of Chemical Compounds for Property Prediction In this work, we introduce RGBChem, a novel approach for converting chemical compounds into image representations, which are subsequently used to train a convolutional neural network (CNN) to predict the HOMO-LUMO gap for compounds from the QM9 database. By modifying the arbitrary order of atoms present in .xyz files used to generate these images, it has been demonstrated that expanding the initial training set size can be achieved by creating multiple unique images (data points) from a single molecule. This study shows that the presented approach leads to a statistically significant improvement in model accuracy, highlighting RGBChem as a powerful approach for leveraging machine learning (ML) in scenarios where the available data set is too small to apply ML methods effectively. Property (philosophy) ; Computer science ; Representation (politics) ; Image (mathematics) ; Artificial intelligence ; Information retrieval ; Data mining ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xuewen Xiao (5555720) | Abdul-Rahman Allouche (1546831) | Emmanuel Dartois (19563610) | Frédéric Magoulès (22402595) | Daniel Peláez (2399203)
Source: Figshare (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Efficient Explorationof High-Dimensional ConfigurationSpaces for the Generation of Chemical Datasets In this work, we introduce an automated methodology for the efficient and relatively inexpensive exploration of large high-dimensional chemical spaces, with particular focus on number-of-atoms-conserving processes, such as in mechanochemical reactions. Our approach combines: (1) a physically motivated stochastic global-landscape exploration phase (mechanochemical distortion), which efficiently overcomes entropic barriers, and (2) a local exploration phase in which the previously determined local basins are sampled with molecular dynamics and graph theory. Specifically, this last phase makes use of the (vdW-) transition state search using a chemical dynamical simulations algorithm. Our methodology requires minimal input from the user. As a case study, we have explored the conformational landscape, including transition states and minimum energy paths, of the C<sub>60</sub>H<sub>10</sub> hydrogen–carbon clusters owing to their astrochemical relevance as potential carriers of the aromatic infrared bands. From a single initial seed (geometry), we have obtained a series of 212 mechanochemically relevant conformers, and from just 3 of them, we have obtained a set of >13 000 minima spanning the domain of our interest. The underlying chemical network has been fully characterized and rationalized using statistical analysis tools. Our case study perfectly illustrates the potential of our approach in the automatic generation of chemical databases, in other words, annotated data for the training of data-hungry deep learning models in chemistry. Maxima and minima ; Focus (optics) ; Graph ; Relevance (law) ; Set (abstract data type) ; Phase transition ; Molecular dynamics ; Energy (signal processing) ; State (computer science) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Kunyang Sun (13154855) | Dorian Bagni (18777984) | Joseph M. Cavanagh (22264530) | Yingze Wang (6095966) | Jacob M. Sawyer (19137683) | Bo Zhou (15092) | Andrew Gritsevskiy (21081818) | Oufan Zhang (11503063) | Teresa Head-Gordon (212730)
Source: Figshare (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

SynLlama: Generating Synthesizable Molecules and TheirAnalogs with Large Language Models Generative machine learning models for exploring chemical space have shown immense promise, but many molecules that they generate are too difficult to synthesize, making them impractical for further investigation or development. In this work, we present a novel approach by fine-tuning Meta’s Llama3 Large Language Models (LLMs) to create SynLlama, which generates full synthetic pathways made of commonly accessible building blocks and robust organic reaction templates. SynLlama explores a large synthesizable space using significantly less data and offers strong performance in both forward and bottom-up synthesis planning compared to other state-of-the-art methods. We find that SynLlama, even without training on external building blocks, can effectively generalize to unseen yet purchasable building blocks, meaning that its reconstruction capabilities extend to a broader synthesizable chemical space than those of the training data. We also demonstrate the use of SynLlama in a pharmaceutical context for synthesis planning of analog molecules and hit expansion leads for proposed inhibitors of target proteins, offering medicinal chemists a valuable tool for discovery. Chemical space ; Context (archaeology) ; Space (punctuation) ; Generative grammar ; Training set ; Synthetic data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis
Authors: Baum, Dario | Förster, Arno | Visscher, Lucas
Source: arXiv (Cornell University) (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

Transfer learning of GW-Bethe-Salpeter Equation excitation energies A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like DFT or TDDFT results and the scarcity of high-fidelity data like many-body perturbation theory labels. We show that transfer learning provides an effective route to bridge this gap: graph neural networks pretrained on DFT and TDDFT properties can be finetuned with limited qs$GW$ and qs$GW$-BSE data to yield accurate predictions of quasiparticle and excitation energies. Assessing both full-model and readout-only finetuning across chemically diverse test sets, we find that pretraining improves accuracy, reduces reliance on costly qs$GW$ data, and mitigates large predictive outliers even for molecules larger or chemically distinct from those seen during finetuning. Our results demonstrate that multi-fidelity transfer learning can substantially extend the reach of many-body-level predictions across chemical space. Time-dependent density functional theory ; Excitation ; Transfer of learning ; Quasiparticle ; Perturbation theory (quantum mechanics) ; Outlier ; Density functional theory ; Yield (engineering) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Ziyi Quan | Yaxu Liu | Qidi Zhong | Bo Wang
Source: Chemical Science (article, 2025-12-23)
Field: Chemistry — Organic Chemistry

Metal-free radical borylations: mechanisms, catalytic strategies, and synthetic applications Metal-free radical borylation has emerged as a powerful and sustainable alternative to transition-metal-catalyzed methods, addressing challenges such as residual metal contamination, functional group sensitivity, and the high cost of precious metals. This review provides a comprehensive analysis of the mechanistic underpinnings and catalytic innovations driving this rapidly evolving field. We delve into the fundamental steps of radical generation, including homolytic B-X bond cleavage and the formation of alkyl, aryl, and boryl radicals through single-electron transfer (SET), hydrogen atom transfer (HAT), and energy transfer (EnT) pathways. Recent strategic developments are critically evaluated, including photoinduced processes such as electron donor-acceptor (EDA) complex activation, consecutive photoinduced electron transfer (ConPET), and polarity reversal catalysis and electrochemical activation using organic mediators and thermal initiation <i>via</i> radical chain processes. Unlike previous reviews, we place a special emphasis on catalytic strategies, substrate scope limitations, and emerging approaches for achieving regiocontrol. The synthetic utility of metal-free radical borylation is highlighted through its application in the late-stage functionalization of pharmaceuticals, natural products, and other complex molecules, enabling transformations that are often difficult to achieve using traditional methods. Finally, we outline the key challenges that continue to shape the field, including selective borylation of unactivated C(sp<sup>3</sup>)-H bonds and the scalability of photochemical and electrochemical systems, while also identifying promising future directions in enhancing atom economy, deepening mechanistic understanding, and integrating method development with cutting-edge technology such as high-throughput screening and machine learning. By bridging fundamental radical principles with practical synthesis, this review aims to serve as a roadmap for the continued development of metal-free boron chemistry. Borylation ; Electron transfer ; Catalysis ; Radical ; Organic synthesis ; Scope (computer science) ; Homolysis ; Photoinduced electron transfer Organoboron and organosilicon chemistry ; Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions
Authors: Kulko, Roman-David | Pletl, Alexander | Hanus, Andreas | Elser, Benedikt
Source:  (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Plastic Granules and Their Mixtures Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400–1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400–1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used. Partial least squares regression ; Spectrometer ; Fusion ; Spectroscopy ; Binary number ; Spectral line ; Range (aeronautics) ; Component (thermodynamics) ; Sensor fusion Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mineral Processing and Grinding
Authors: Rafał Stottko (21343537) | Radosław Michalski (2255191) | Bartłomiej M. Szyja (1826437)
Source: Figshare (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

RGBChem: Image-LikeRepresentation of Chemical Compoundsfor Property Prediction In this work, we introduce RGBChem, a novel approach for converting chemical compounds into image representations, which are subsequently used to train a convolutional neural network (CNN) to predict the HOMO–LUMO gap for compounds from the QM9 database. By modifying the arbitrary order of atoms present in .xyz files used to generate these images, it has been demonstrated that expanding the initial training set size can be achieved by creating multiple unique images (data points) from a single molecule. This study shows that the presented approach leads to a statistically significant improvement in model accuracy, highlighting RGBChem as a powerful approach for leveraging machine learning (ML) in scenarios where the available data set is too small to apply ML methods effectively. Property (philosophy) ; Representation (politics) ; Set (abstract data type) ; Convolutional neural network ; Training set ; Data set ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Hesam Makki | Colm Burke | Christian B. Nielsen | Alessandro Troisi
Source: ChemRxiv (preprint, 2025-04-01)
Field: Materials Science — Polymers and Plastics

Mapping the Structure-Function Landscape of Semiconducting Polymers The molecular design of semiconducting polymers (SCPs) has been largely guided by varying monomer combinations and sequences by leveraging a robust understanding of charge transport mechanisms. However, the connection between controllable structural features and resulting electronic disorder remains elusive, leaving design rules for next-generation SCPs undefined. Using high-throughput computational methods, we analyse 100+ state-of-the-art p- and n-type polymer models. This exhaustive dataset allows for deriving statistically significant design rules. Our analysis disentangles the impact of key structural features, examining existing hypotheses, and identifying new structure-property relationships. For instance, we show that polymer rigidity has minimal impact on charge transport, while the planarity persistence length, introduced here, is a superior structural characteristic. Additionally, we demonstrate the predictive power of machine learning models built on our dataset, laying the groundwork for a data-driven approach to SCP design and accelerating the discovery of materials with tailored electronic properties. Function (biology) ; Structure function ; Geography ; Economic geography ; Physics ; Biology ; Evolutionary biology Conducting polymers and applications
Authors: Futo Mitsuishi | Masato Sumita | Kei Terayama | Koji Tsuda
Source: ChemRxiv (article, 2025-10-24)
Field: Chemistry — Analytical Chemistry

Deep Learning-Assisted De Novo Molecule Identification through Vibrational Spectra Raman spectroscopy ; Spectral line ; Molecule ; Deep learning ; Raman scattering ; Isotropy ; Infrared spectroscopy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Various Chemistry Research Topics
Authors: Daniel E. Hooks | Michael McBride | Reeju Pokharel | Alexander Scheinker
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Pulse and Pulse-Reverse Parameters for Rhenium Deposition through Machine Learning Electrodeposition involves many control parameters. Choices of solvent, electrolytes, complexing agents, adsorbents, temperature, and power settings introduce myriad choices in processing conditions. Though many individual chemical and physical processes that influence choices in these processing conditions are understood at some level, the detailed interdependencies are difficult to unravel. Using experience and wealth of past studies, researchers can hypothesize parameters to use when developing deposition methods for new systems. However, trial and error remain integral to the development process, wherein many parameters are varied, and resulting coatings are characterized. Recent revolutionary advances in pulse and pulse-reverse power and electrolyte developments only add to the complexity; controlled current or voltage, on/off/reverse heights and durations alone add many variables. High throughput methods are being developed to more rapidly screen deposition parameters, in which deposition variables are varied and resulting films are characterized by methods including optical microscopy. In some cases in situ characterization methods have been demonstrated and remain in development, but even so, data is sparse given how much uncertainty there is in the time-dependent processing variables. Deep learning methods of various kinds have demonstrated impressive predictions in several fields in recent years. Examples include (1) real-time control of beamline dynamics, in which input parameters from injectors and magnetic lens controls result in downstream images of beam shape, with limited data on intermediate processes and (2) forward prediction of diffraction or microstructure data, among others. In many ways, these examples are very similar to the electrodeposition problem we have outlined: definitive chemistry and physics processes along with input parameters are known, dependencies and time evolution are not, and outcomes are measured downstream in the form of some characterization of output. Previously, we used high throughput trial and error approaches to optimize parameters for rhenium deposition, and input waveforms can be compared to microscopy characterization of the resulting film. Here, we will describe an approach for deep learning using pulse/pulse-reverse deposition parameters and resulting images for this system. Such approaches may enable much more rapid parameter refinement for electrodeposition of many materials. Deposition (geology) ; Characterization (materials science) ; Power (physics) ; Image processing ; Throughput ; Pulse (music) ; Pulse shaping ; Control (management) ; Pulsed laser deposition Machine Learning in Materials Science ; Electrodeposition and Electroless Coatings ; Electrophoretic Deposition in Materials Science
Authors: Yaroslav Belov | Ildar Amirov | Sergey Zimin
Source: St. Petersburg State Polytechnical University Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Calculation of the volume of nano- and microstructures formed on the surface of PbTe during ion-plasma treatment using machine learning The formation of porous Pb nano- and microstructures on the surface of PbTe films during low-energy ion-plasma treatment has been studied using scanning electron microscopy (SEM) combined with machine learning-based image analysis. PbTe epitaxial films with (111) crystallographic orientation were exposed to argon plasma at an ion energy of approximately 25 eV for varying durations (60–240 s). SEM imaging at a tilt angle of 70° enabled three-dimensional size estimation of the formed structures, which, together with automated image processing using the DLgram01 deep learning service, allowed for precise calculation of particle number, area, height, and volume. In this paper, a comparative analysis of the parameters of the Pb structure on the surface of lead telluride films with the orientation (111) and PbTe single crystals with the orientation (100) is carried out. This study demonstrates the effectiveness of machine learning for quantitative analysis of surface nanostructures after low-energy argon plasma treatment of PbTe surface. Microstructure ; Surface (topology) ; Scanning electron microscope ; Orientation (vector space) ; Surface energy ; Tilt (camera) ; Nanostructure ; Epitaxy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Copper-based nanomaterials and applications
Authors: Anjana Talapatra | Anup Pandey | Matthew S. Wilson | Ying Wai Li | Ghanshyam Pilania | Blas P. Uberuaga | Danny Pérez
Source: Computer Physics Communications (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Best of both worlds: Enforcing detailed balance in machine learning models of transition rates Balance (ability) ; Computer science ; Transition (genetics) ; Detailed balance ; Statistical physics ; Physics ; Chemistry ; Psychology Machine Learning in Materials Science ; Reservoir Engineering and Simulation Methods
Authors: Zexi Zhang | Chengda Zhou | Yufei Chen | Yu Qin Gu | Mao Chen
Source: Angewandte Chemie (article, 2025-10-12)
Field: Materials Science — Materials Chemistry

Copolymer Sequence Regulation Enabled by Reactivity Ratio Fingerprints via Machine Learning Abstract Sequence control is critical for tuning polymer properties in high‐end applications, where reactivity ratios serve as key parameters for analyzing and regulating sequences. Nevertheless, traditional determination methods exhibit low experimental efficiency and are typically confined to two‐component copolymerization. Here, we develop a machine learning platform, which leverages a novel design of “reactivity ratio fingerprints” ( r FPs) to determine reactivity ratios in binary and ternary copolymerizations. Deep learning models trained on millions of r FPs enable highly efficient (millisecond‐level) determination from sparse experimental data (random monomer structures, arbitrary reaction design). This approach demonstrates outstanding versatility to analyze reactivity ratios under diverse conditions (e.g., temperature, solvent). Notably, r FP‐guided reaction design promotes on‐demand sequence tailoring, compatible with a wide range of binary and ternary monomer combinations. Kinetic investigations and glass transition characterizations support the formation of varied sequence structures, facilitating the identification of binary and ternary azeotropic copolymerizations. This work not only unveils an attractive strategy for determining reactivity ratios but also offers a generalizable framework for sequencing complicated chain structures toward property engineering. Ternary operation ; Reactivity (psychology) ; Sequence (biology) ; Binary number ; Copolymer ; Monomer ; Identification (biology) Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Yuri Cho | Ksenia R. Briling | Yannick Calvino Alonso | Rubén Laplaza | Clémence Corminbœuf
Source: ChemRxiv (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Benchmarking Physics-inspired Machine Learning Models for Transition Metal Complexes with Diverse Charge and Spin States Physics-inspired machine learning (ML) models can be categorized into two classes: those relying solely on three-dimensional structure and those incorporating electronic information. In this work, we benchmark both classes for predicting quantum-chemical properties of transition metal complexes with diverse charge and spin states, using three complementary datasets. The evaluated methods include molecular representations (SLATM, FCHL, SOAP, and SPAHM family) combined with kernel ridge regression, as well as geometric deep learning models (MACE and 3DMol). We examine how the inclusion of electronic information affects predictive accuracy across datasets and target properties. Models that incorporate electronic information consistently outperform purely structure-based models for properties whose distributions are strongly governed by electronic characters, such as spin-splitting energies and frontier orbital energies. In contrast, structure-only models perform well for predicting the HOMO–LUMO gap and dipole moment magnitude, whose distributions are relatively insensitive to electronic characteristics. Geometric deep learning models with charge and spin embeddings (MACE-QS and 3DMol-QS) show the highest overall accuracy, with 3DMol offering the best computational efficiency among the tested models. These results clarify when geometric information is sufficient and when incorporating electronic information becomes essential, providing practical guidance for selecting effective physics-based ML models for transition metal complexes. Charge (physics) ; Deep learning ; Spin (aerodynamics) ; Electronic structure ; Benchmarking ; Benchmark (surveying) ; Dipole ; Computational learning theory Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions