jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saloni Joshi | Reema Gabrani | Ruby Beniwal
Source:  (article, 2025-02-20)
Field: Chemistry — Spectroscopy

Leveraging Machine Learning for Predicting Molecular Refractivity in Drug Toxicology The integration of electronics engineering into molecular refractivity prediction is transforming drug toxicology studies. By combining biosensors, machine learning algorithms, and high-speed data systems, scientists can better predict toxicological profiles of drugs. This interdisciplinary approach improves safety and efficacy in drug development. Electronics engineering enables real-time monitoring of drug metabolism through wearable devices and predictive models, identifying toxic metabolites early and reducing adverse reactions. These technologies enhance the assessment of metabolic processes, guiding safer drug development. Machine learning algorithms further refine predictions by analyzing large datasets, identifying toxicity patterns, and improving personalized treatment plans, ultimately reducing time and costs in toxicology testing. In this research work, machine learning classifier accuracy has been achieved 85% to identify toxic drugs and toxicity prediction accuracy by using the Bayesian Neural Network was 95.8%. Computer science ; Machine learning ; Artificial intelligence Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Akram Abedi Orang | Mojtaba Alaei | Artem R. Oganov
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Curie Temperature of Magnetic Materials with Machine Learning: Descriptor Engineering, Graph Neural Networks, and the Role of Curated Data Feature (linguistics) ; Curie temperature ; Graph ; Artificial neural network ; Feature engineering ; Pattern recognition (psychology) Machine Learning in Materials Science
Authors: Wuling Zhao | Mingting Zhou | Jialin Shao | Jingzheng Ren | Yusha Hu | Yulin Han | Yi Man
Source: Journal of Chemical Information and Modeling (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation The development of new materials is a time-consuming and resource-intensive process. Deep learning has emerged as a promising approach to accelerate this process. However, accurately predicting crystal structures using deep learning remains a significant challenge due to the complex, high-dimensional nature of atomic interactions and the scarcity of comprehensive training data that captures the full diversity of possible crystal configurations. This work developed a neural network model based on a data set comprising thousands of crystallographic information files from existing crystal structure databases. The model incorporates a self-attention mechanism to enhance prediction accuracy by learning and extracting both local and global features of three-dimensional structures, treating the atoms in each crystal as point sets. This approach enables effective semantic segmentation and accurate unit cell prediction. Experimental results demonstrate that for unit cells containing up to 500 atoms, the model achieves a structure prediction accuracy of 89.78%. Segmentation ; Artificial neural network ; Artificial intelligence ; Computer science ; Crystal (programming language) ; Natural language processing ; Pattern recognition (psychology) Machine Learning in Materials Science ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Maliheh Shaban Tameh (17832950)
Source: Figshare (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Machine Learning-AidedScreening and Design Rule Discoveryfor LWIR-Transparent Optical Materials The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800–1250 cm<sup>–1</sup>/8–12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely on costly inorganic materials, limiting their broader deployment. Here, we present a machine learning (ML)-driven framework for identifying highly LWIR-transparent hydrocarbons as candidate monomers for sulfur-organic hybrid polymers. Infrared (IR) spectra are predicted by using a communicative message-passing neural network (CMPNN), and the effect of spectral broadening (γ = 4–10) is systematically analyzed to guide flexible, use-driven γ selection. LWIR window transparency (wT) is estimated from spectra and directly predicted using regression models trained on CMPNN-derived fingerprints. These interpretable models enable the extraction of structure–property relationships, revealing chemically meaningful descriptors and substructural motifs linked to high transparency and supporting the formulation of design rules for IR-transparent materials. The approach demonstrates strong predictive accuracy and generalizability across training, test, and external data sets. Notably, several ML-prioritized motifs match experimentally validated comonomers used in IR polymer fabrication, and the model further proposes new, chemically plausible scaffolds. By bypassing the cost of density functional theory (DFT), the framework enables scalable, data-driven screening and provides chemically grounded design insights for next-generation LWIR-transparent materials. Transparency (behavior) ; Generalizability theory ; Limiting ; Artificial neural network ; Infrared ; Near-infrared spectroscopy ; Window (computing) Machine Learning in Materials Science ; Polydiacetylene-based materials and applications ; Hydrogels: synthesis, properties, applications
Authors: Sandeep Kumar
Source: International Journal of Applied and Behavioral Sciences (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Predicting Material Properties with Machine Learning on the Periodic Table Machine learning (ML) has emerged as a transformative tool in materials science, enabling the prediction of material properties with unprecedented accuracy and efficiency. By leveraging the periodic table as a structured feature space, researchers can utilize periodic trends and intrinsic elemental properties to train robust predictive models. This paper explores the intersection of machine learning and the periodic table, detailing methodologies, challenges, and applications in material property prediction. Key case studies are presented to illustrate the potential of ML-driven approaches in discovering new materials and optimizing their properties. Table (database) ; Computer science ; Artificial intelligence ; Machine learning ; Data mining History and advancements in chemistry
Authors: Yanbing Guo | Yongheng Wang | Wang Zhang | Zhikang Shen | Li Zeng
Source: Archives of Computational Methods in Engineering (review, 2025-08-28)
Field: Materials Science — Materials Chemistry

From Algorithms to Applications: A Comprehensive Review of Machine Learning in Computational Materials Science Computer science ; Machine learning ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Shariq Haseen | Peter Kroll
Source: Journal of the American Ceramic Society (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

A machine‐learning interatomic potential for Si‐C‐N‐H with application to polymer‐to‐ceramic processing of polysilazanes Abstract We developed a machine‐learning interatomic potential (MLIP) based on Moment Tensor Potentials for atomistic simulations in the Si‐C‐N‐H system. The MLIP was trained on ordered and disordered configurations—including crystalline phases, polymers, amorphous models, and high‐temperature ab initio molecular dynamics trajectories of chemical reactions—and achieves Density–Functional–Theory‐level accuracy for energies, forces, and stresses. We apply the MLIP to complex processes that were previously inaccessible at scale: self‐diffusion in Si 3 N 4 , negative thermal expansion in Si(NCN) 2 , fracture in SiC/Si 3 N 4 composites, and the polymer‐to‐ceramic transformation of polysilazanes. Large‐scale simulations over nanoseconds capture structural evolution during pyrolysis and reproduce experimental observations, such as the formation of nanometer‐sized graphitic segregations in SiCN ceramics. Our results demonstrate that the MLIP extends quantum‐level accuracy to technologically relevant problems, providing new opportunities for predictive modeling of ceramic materials and their transformations. Interatomic potential ; Molecular dynamics ; Amorphous solid ; Ceramic ; Ab initio quantum chemistry methods ; Ab initio ; Moment (physics) Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced ceramic materials synthesis
Authors: Lam, Jeff | Gorbunova, Olesya | Islam, Md Ariful | Tiwari, Ganesh | Adhikari, Tibendra
Source: The Open Repository - Binghamton (Binghamton University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of Ag-Sn Phases Predicted with Machine Learning Machine learning has the potential to accelerate materials discovery for targeted applications. In electronics packaging, continued miniaturization demands new solutions, with Sn-rich alloys emerging as promising lead-free interconnects. The Ag-Sn binary was recently explored with a combination of machine learning, evolutionary optimization, and ab initio modeling, revealing previously unknown Sn-rich intermetallics stable at elevated temperatures. In this project, these predictions have been tested experimentally using flux growth synthesis, where Sn, due to its low melting point, acts as flux for dissolving raw materials at high temperatures before crystallizing target phases through controlled cooling. To ensure safe processing conditions, Python-based software was developed to monitor and manage vapor pressure during high-temperature treatments. Energy dispersive X-Ray spectroscopy (EDX) analysis performed on synthesized alloys suggests binary compounds with compositions close to the predicted 1:2 and 1:4 stoichiometries are present. This work was supported by the Department of Energy through grant DE-SC0021202. Miniaturization ; Intermetallic ; Stoichiometry ; Energy (signal processing) ; Work (physics) ; Software ; Binary number ; Dissolution Machine Learning in Materials Science ; Electronic Packaging and Soldering Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Shin Kiyohara (11856677) | Yu Kumagai (1750672)
Source: Figshare (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Bayesian Optimization with Gaussian Processes Assistedby Deep Learning for Material Designs Machine learning (ML) approaches have become ubiquitous in the search for new materials in recent years. Bayesian optimization (BO) based on Gaussian processes (GPs) has become a widely recognized approach in material exploration. However, feature engineering has critical impacts on the efficiency of GP-based BO, because GPs cannot automatically generate descriptors. To address this limitation, this study applies deep kernel learning (DKL), which combines a neural network with a GP, to BO. The efficiency of the DKL model was comparable to or significantly better than that of a standard GP in a data set of 922 oxide data sets, covering band gaps, ionic dielectric constants, and effective masses of electrons, as well as in experimental data sets, the band gaps of 610 hybrid organic–inorganic perovskite alloys. When searching for the alloy with the highest Curie temperature among 4560 alloys, the standard GP outperformed the DKL model because a strongly correlated descriptor of the Curie temperature could be directly utilized. Additionally, DKL supports transfer learning, which further enhances its efficiency. Thus, we believe that BO based on DKL paves the way for exploring diverse material spaces more effectively than GPs. Bayesian optimization ; Kriging ; Gaussian process ; Deep learning ; Artificial neural network ; Kernel (algebra) ; Bayesian probability ; Materials informatics ; Data set Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms
Authors: Alfonso Enrique Ramírez Sanabria | Fabian Ramirez | Julián Muñoz-Ordóñez | Cristian Muñoz | Nelson J. Castellanos
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Literature-Based Machine Learning Framework for Yield Prediction in Cyclohexene Epoxidation: Toward a Digital Catalyst Concept Cyclohexene ; Yield (engineering) ; Catalysis ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Materials science ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Norah Salem Alsaiari | Bilal Siddique | Ejaz Hussain | Muhammad Faizan Nazar | Talal M. Althagafi | A. Badshah | M.S. Al‐Buriahi
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Structure-activity landscapes and synthetic accessibility in machine learning-guided organic semiconductor discovery Polymer ; Similarity (geometry) ; Organic semiconductor ; Absorption (acoustics) ; Property value ; Chemical space ; Cluster (spacecraft) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Shuhao Zhang | Michael Chigaev | Olexandr Isayev | Richard A. Messerly | Nicholas Lubbers
Source: ChemRxiv (preprint, 2025-04-25)
Field: Materials Science — Materials Chemistry

Including physics-informed atomization constraints in neural networks for reactive chemistry Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and relatively low computational cost. However, a common and unaddressed challenge with many current neural network MLIP models forms is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered neural network architecture to account for the energies of isolated atoms. The result produces a consistent prediction of atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture we call HIP-NN-AE, an AE-constrained version of the Hierarchically Interacting Particle Neural Network (HIP-NN), as well as ANI-AE, the AE-constrained version of the Accurate Neural Network Engine for Molecular Energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to an unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks, and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks. Artificial neural network ; Chemistry ; Nanotechnology ; Computer science ; Artificial intelligence ; Materials science Machine Learning in Materials Science
Authors: Yuqing He | Pierre-Paul De Breuck | Hongming Weng | Matteo Giantomassi | Gian‐Marco Rignanese
Source: npj Computational Materials (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Machine learning on multiple topological materials datasets A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different sub-datasets, differences are identified between the original datasets in terms of topological types, chemical elements, unknown magnetic compounds, and feature space coverage. Their impact on model performance is analyzed. Turning to the simpler binary classification between trivial insulators and nontrivial topological materials, three different approaches are also tested. Key characteristics influencing material topology are identified, with the maximum packing efficiency and the fraction of p valence electrons being highlighted as critical features. Computer science ; Topology (electrical circuits) ; Artificial intelligence ; Machine learning ; Mathematics ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Graph Neural Networks
Authors: Kerrin Janssen | Jan M. Wollschläger | Jonny Proppe | Andreas H. Göller
Source: ChemRxiv (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

When Machine Learning Models Learn Chemistry I: Quantifying Explainability with Matched Molecular Pairs Explainability methods in machine learning-driven research are increasingly being used, but it remains challenging to assess their reliability without deeply investigating the specific problem at hand. In this work, we present a Python-based Workflow for Interpretability Scoring using matched molecular Pairs (WISP). This workflow can be applied to assess the performance of explainability methods on any given dataset containing SMILES and is model-agnostic, making it compatible with any machine learning model. Evaluation on two physics-based datasets demonstrates that the explanations reliably capture the predictions of the respective machine learning models. Furthermore, our workflow reveals that explainability methods can only meaningfully reflect the property of interest when the underlying models achieve high predictive accuracy. Therefore, the explainability performance on a test set can function as a quality measure of the underlying model. To ensure compatibility with any model type, we developed an atom attributor, which generates atom-level attributions for any model using any descriptor that can be obtained using SMILES representations. This method can also be applied as a standalone explainability tool, independently of WISP. WISP enables users to interpret a wide range of machine learning models in the chemical domain and gain valuable insights into how these models operate and the extent to which they capture underlying chemical principles. Interpretability ; Workflow ; Applicability domain ; Reliability (semiconductor) ; Set (abstract data type) ; Test set ; Training set ; Property (philosophy) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Clara G. Hernández | Raúl Rivera-Blas | Chinnapat Panwisawas | Emmanuel Zenén Rivera Blas | Luis Armando Flores-Herrera
Source:  (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for Discovering Titanium Alloys with a Low Young’s Modulus This research proposes the use of machine learning (ML) to predict and optimise titanium alloys with low Young's modulus for applications in additive manufacturing of bone prostheses. From a database of 3,235 alloys, a predictive model integrating atomic and electronic descriptors was developed. The Artificial Neural Network achieved the best performance (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.8448), predicting a minimum modulus of 36.75 GPa. This approach allows to reduce time and resources in costly experimental tests. Artificial neural network ; Titanium alloy ; Modulus ; Titanium ; Elastic modulus ; Young's modulus ; Bulk modulus Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes
Authors: Filipp Gusev | Sang Hun Chung | Matthew Verber | Marcus H. Reis | Frank A. Leibfarth | Olexandr Isayev | Nicholas G. Taylor | Yueh Z. Lee
Source: UNC Libraries (article, 2025-06-07)
Field: Materials Science — Materials Chemistry

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of <sup>19</sup>F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring &lt;0.9% of the overall compositional space, lead to the identification of &gt;10 copolymer compositions that outperformed state-of-the-art materials. Copolymer ; Context (archaeology) ; Polymer ; Characterization (materials science) ; Monomer ; Identification (biology) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Tymofii Yu. Nikolaienko | Harshil Patel | A. Panda | Shashikant Joshi | Stanislav Jašo | Kaushic Kalyanaraman
Source: Advanced Theory and Simulations (article, 2025-06-27)
Field: Materials Science — Materials Chemistry

Making Physics‐Informed Neural Networks Theory‐Compliant: The Case of Fischer‐Tropsch Catalyst Modeling Abstract Physics‐Informed Neural Networks (PINNs) accelerate equation solving by merging physics‐based theories with machine learning. Yet, their application in multi‐staged computational workflows unveils reliability issues. This is demonstrated for Fischer‐Tropsch synthesis modeling, by leveraging PINNs for source terms evaluation in the finite‐difference method solving the coupled reaction‐diffusion equations. Subtle inaccuracies of PINNs approximating the functions in close vicinity of their input variables' ranges boundaries, while not captured by traditional neural network assessment methods, are shown to induce unphysical ultimate solutions and convergence failures. A problem‐specific PINN architecture is proposed that has a correct asymptotic behavior and resolves the revealed issues. Combined with a tailored initial guess generation scheme, the proposed modifications are shown to recover the overall stability of the simulations while preserving the speed‐up brought by PINNs as the workflow component. The possible applications of the proposed hybrid solver are discussed in the context of chemical reactor simulations. Fischer–Tropsch process ; Catalysis ; Artificial neural network ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications