jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: B. S. Sindu | Jan Hamaekers
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers Epoxy polymers are widely used due to their multifunctional properties, but machine learning (ML) applications remain limited owing to their complex 3D molecular structure, multi-component nature, and lack of curated datasets. Existing ML studies are largely restricted to simulation data, specific properties, or narrow constituent ranges. To address these limitations, we developed an informed Gaussian Process Regression-based Knowledge Distillation (GPR-KD) framework for predicting multiple physical (glass transition temperature, density) and mechanical properties (elastic modulus, tensile strength, compressive strength, flexural strength, fracture energy, adhesive strength) of thermoset epoxy polymers. The model was trained on experimental literature data covering diverse monomer classes (9 resins, 40 hardeners). Individual GPR models serve as teacher models capturing nonlinear feature-property relationships, while a unified neural network student model learns distilled knowledge across all properties simultaneously. By encoding the target property as an input feature, the student model leverages cross-property correlations. Molecular-level descriptors extracted from SMILES representations using RDKit create a physics-informed model. The framework combines GPR interpretability and robustness with deep learning scalability and generalization. Comparative analysis demonstrates superior prediction accuracy over conventional ML models. Simultaneous multi-property prediction further improves accuracy through information sharing across correlated properties. The proposed framework enables accelerated design of novel epoxy polymers with tailored properties. Epoxy ; Interpretability ; Scalability ; Robustness (evolution) ; Gaussian process ; Artificial neural network ; Nonlinear system ; Deep learning ; Multiscale modeling Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Polymer crystallization and properties
Authors: Gaopeng Ren | Yijie Yang | Jiajun Zhou | Kim E. Jelfs
Source: ArXiv.org (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

PolyMon: A Unified Framework for Polymer Property Prediction Accurate prediction of polymer properties is essential for materials design, but remains challenging due to data scarcity, diverse polymer representations, and the lack of systematic evaluation across modelling choices. Here, we present PolyMon, a unified and accessible framework that integrates multiple polymer representations, machine learning methods, and training strategies within a single, accessible platform. PolyMon supports various descriptors and graph construction strategies for polymer representations, and includes a wide range of models, from tabular models to graph neural networks, along with flexible training strategies including multi-fidelity learning, Δ-learning, active learning, and ensemble learning. Using five key polymer properties as benchmarks, we perform systematic evaluations to assess how representations and models affect predictive performance. These case studies further illustrate how different training strategies can be applied within a consistent workflow to leverage limited data and incorporate physical model derived information. Overall, PolyMon provides a comprehensive and extensible foundation for benchmarking and advancing machine learning-based polymer property prediction. The code is available at github.com/fate1997/polymon. Workflow ; Leverage (statistics) ; Benchmarking ; Training set ; Property (philosophy) ; Graph ; Predictive modelling ; Extensibility Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: A. N. Dobrynin | Yury Khaydukov | Mark A. Gubbins
Source: Chemical Physics Reviews (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Modern machine learning in thin film device development Recent advances in deep learning and scientific machine learning (SML) offer a wide spectrum of novel tools with the potential to radically transform various areas of academic research, industrial research and development (R&amp;D), and manufacturing. These include inverse device design, data-driven equation discovery, rapid approximate partial solutions to differential equations for exploring the design space, automated metrology and manufacturing process control, new materials design, and the creation of fast and realistic digital twins for virtual testing and design optimization. The purpose of this review is to assist R&amp;D practitioners, who are not specialists in artificial intelligence, in navigating this complex and dynamic landscape, enabling them to adopt modern machine learning (ML) methods in their work. We particularly emphasize the potential advantages of deep learning methods for the field of thin film device development, highlighting the main approaches and points of their applications in R&amp;D design and process. The review is organized into several sections. First, we provide a brief overview of machine learning and deep learning, introducing basic neural network architectures and describing their possible use cases relevant to industrial R&amp;D. In Sec. III, we introduce examples of ML approaches enabling the reduction of dependency on the amount of input data and improving generalization capabilities of neural networks through introduction of realistic inductive biases in the form of symmetries, conservation laws, physics equations, etc. Then we review some of the most successful large-scale SML models, including foundational materials simulation and generation models. Finally, we discuss existing and prospective applications of ML models in different aspects of thin film devices development. Artificial neural network ; Deep learning ; Field (mathematics) ; Generalization ; Process (computing) ; Development (topology) ; Industrial design ; Metrology Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Dhrutik Patel | Amit Kumar Rai | Govind Singh | Rimjhim Singh
Source: AIP conference proceedings (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Artificial neural network-based prediction of mechanical properties of steel alloys Artificial neural network ; Deformation (meteorology) ; Welding ; Weldability ; Compression (physics) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Ying Zhang | Rui Guo | Yu Shen | Tianmin He
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Recent advances in low-temperature ceramic fuel cells: material design and applications Ceramic fuel cells (CFCs) are highly efficient and clean electrochemical energy conversion devices, featuring a wide range of available fuels (hydrogen, methane, ethanol and biomass gas) and the absence of the need for precious metal catalysts. They will play an important role in the future development of sustainable energy. Compared with high-temperature CFCs, low-temperature CFCs (LT-CFCs) have the advantages of a broader selection of materials, lower material cost, shorter start-up time, and enhanced thermal cycling durability. However, as the operating temperature decreases, the ionic conductivity of the electrolyte and the catalytic activity of the electrodes (cathode and anode) also significantly decrease, leading to a sharp decline in CFC performance. To address these issues, researchers have made significant efforts in the design and development of LT-CFC materials, including the design concept, crystal structure and properties, composition, microstructure and performance optimization of the materials. In this review, we systematically summarize the research progress in the design and development of key materials (electrodes and electrolytes) for LT-CFCs over the last decade, especially focusing on the new materials designed by various strategies, the rationale behind chosen solutions and the applications of these materials in LT-CFCs, specifically including machine learning, density functional theory calculations, high-entropy strategies, defect engineering, mechanical mixing, impregnation strategy, self-assembly, and surface reconstruction. Some potential challenges and prospects of key materials for LT-CFCs in the future are suggested. Ceramic ; Fuel cells ; Energy transformation ; Material Design ; Biomass (ecology) ; Range (aeronautics) ; Electrochemical energy conversion ; Alcohol fuel ; Electrochemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Taiba Jamal Ashraf Ansari | Gulafsha Ansari | Tahreem Ashraf Momin | Sara Abdul Salam Momin
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-17)
Field: Materials Science — Materials Chemistry

Role of Artificial Intelligence in Heterogeneous Catalyst Heterogeneous catalysis plays a vital role in present-day chemical industries and supports numerous large-scale operations related to energy production, petrochemical processing, and environmental remediation. Despite its industrial importance, the systematic design of efficient catalysts is still difficult because catalytic reactions are governed by several interconnected factors operating at different length and time scales, including electronic interactions, surface processes, reaction pathways, and mass and heat transport at the reactor level. When combined with first-principles simulations and experimental results, ML(Machine Learning) can lower computational costs and allow researchers to explore a wider range of catalysts. Heterogeneous catalysis, ML has been used for tasks like discovering new catalysts quickly, predicting adsorption energies, modeling reaction rates, evaluating uncertainties, and supporting automated experiments. Artificial intelligence (AI) is increasingly influencing heterogeneous catalysis by accelerating atomistic simulations, kinetic modeling, and experimental analysis. A persistent challenge is the connection between intrinsic reaction kinetics and experimentally accessible observables, due to the multiscale nature of catalytic systems and the indirect character of measurements. Recent progress in machine-learned force fields, micro kinetic modeling, reactor simulations, and operand techniques enables rapid exploration of large chemical and parameter spaces. Ambiguity persists as multiple mechanisms fit the same data, while AI-driven self-driving models enable rapid catalyst prediction and optimization. Heterogeneous catalysis ; Catalysis ; Petrochemical ; Range (aeronautics) ; Chemical energy ; Chemical reaction engineering ; Chemical reactor ; Chemical kinetics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Ahmad Alruhaily | Basil Aldhiabi | Osama Alqarafi | Mohamed Shaban | Asad A. Zaidi | Sohaib Z. Khan | Hamad AlMohamadi | Essam R.I. Mahmoud | Asim Laeeq Khan
Source: Separation and Purification Technology (article, 2026-03-30)
Field: Chemistry — Inorganic Chemistry

Data-driven analysis of a PET-derived aluminium MOF for Congo red removal: Circular-economy synthesis, mechanistic insights, and explainable machine learning Adsorption ; Congo red ; Aqueous solution ; Polyethylene terephthalate ; Aluminium ; Response surface methodology ; Wastewater ; Precipitation Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Cheng Zeng | Harry W. Sullivan | Thomas Egg | Maya M. Martirossyan | Philipp Höllmer | Jirui Jin | Richard G. Hennig | Adrian Roitberg | Stefano Martiniani | Ellad B. Tadmor | Mingjie Liu
Source: Open MIND (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids, and metal-organic frameworks, extending such approaches to fully periodic molecular crystals is still elusive. Here, we present MolCrystalFlow, a flow-based generative model for molecular crystal structure prediction. The framework disentangles intramolecular complexity from intermolecular packing by embedding molecules as rigid bodies and jointly learning the lattice matrix, molecular orientations, and centroid positions. Centroids and orientations are represented on their native Riemannian manifolds, allowing geodesic flow construction and graph neural network operations that respects geometric symmetries. We benchmark our model against a state-of-the-art generative model (MOFFlow) for large-size periodic crystals and a rule-based structure generation method (Genarris) on two open-source molecular crystal datasets. MolCrystalFlow outperforms MOFFlow while achieving competitive performance against Genarris. We also demonstrate an integration of MolCrystalFlow model with universal machine learning potential to accelerate molecular crystal structure prediction, paving the way for data-driven generative discovery of molecular crystals. Intermolecular force ; Intramolecular force ; Crystal (programming language) ; Crystal structure ; Molecular graph ; Crystal structure prediction ; Molecule ; Generative grammar ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Supramolecular Chemistry and Complexes
Authors: Zhengyu Liang | Xin Tan | Jian Wang | Qiao Yang | Zhanqing He | Hui Qi
Source: Langmuir (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Different Termination Diamond Yb Vacancy Color Centers on the (110) Surface: <i>Ab Initio</i> and Machine-Learning Molecular Dynamics Yb vacancy color centers on diamond (110) surfaces are promising candidates for long-coherence solid-state qubits and scalable quantum networks. However, their performance is strongly affected by surface termination, where the mechanism of charge redistribution and its impact on defect dynamics remain unclear. Here, we combine first-principles calculations and machine-learning molecular dynamics (MLMD) to systematically study F-, H-, N-, and O-terminated surfaces. The results reveal that the electronegativity of the terminating atom governs interfacial charge rearrangement, which, in turn, dictates the key quantum properties of Yb centers. The highly electronegative F termination induces the strongest surface dipole and the most significant charge transfer from the lattice, resulting in superior electronic and structural stability. Electron localization and COHP analyses show that strong C-F bonds effectively passivate dangling bonds and eliminate near-Fermi defect states. Furthermore, F termination suppresses electron-phonon coupling-induced decoherence and exhibits minimal atomic fluctuation in MLMD simulations. This synergy between electronic and dynamic stability identifies F termination as the most favorable configuration for engineering robust, high-performance Yb color centers in diamond. Dangling bond ; Vacancy defect ; Quantum tunnelling ; Quantum decoherence ; Diamond ; Molecular dynamics ; Dipole ; Relaxation (psychology) ; Electronegativity ; Charge (physics) Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Topological Materials and Phenomena
Authors: Yelizaveta A. Morkhova | Vladislav T. Osipov | Mikhail I. Smolkov | Artem A. Kabanov
Source: Journal of Solid State Chemistry (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Identification of novel Mg2+- &amp; Zn2+-ion conducting halides via high-throughput screening of the inorganic crystal structure database Crystal structure ; Halide ; Identification (biology) ; X-ray crystallography ; Crystal (programming language) ; Inorganic Chemical Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Thermal Expansion and Ionic Conductivity
Authors: J E Umaña | N A Zawicki | Matthew A. Gebbie | Victor M Zavala | Rose K. Cersonsky
Source: ChemRxiv (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Identifying the Impact of Chemical Functional Groups on Ionic Liquid Conductivity Ionic liquids are non-flammable, electrochemically stable electrolytes with promise as next-generation battery electrolytes. However, strong ion correlations and deviations from classical ion transport prevent the predictive design of application-specific ionic liquid electrolytes. While machine learning models can help address such limitations, standard cheminformatics tools are not well-suited to incorporate electrostatic interactions and thus do not account for the strong intermolecular interactions within ionic liquid materials. In this work, we present a molecular fragment representation that reflects charge-carrier resonance using SMARTS molecular substructure searching to explicitly capture the electrostatic contributions to conductivity of ionic liquid compounds. We find that this representation simplifies structure-conductivity relationships and improves predictive performance in low-data regimes. Additionally, we report how learned contributions of molecular structure impact ion transport and map the ionic liquid chemical design space using principal covariates regression to visualize the underlying structure-property relationships. We find that charge delocalization and anion flexibility are the most influential molecular features in improving ion transport in single-component ionic liquids. Further, we find that polar, alkyl, and fluorinated cation or anion functionalization decrease ion transport by introducing additional intermolecular interactions. Finally, many cation charge centers similarly enhance ion transport, suggesting that ionic liquid anions and ion functionalization are key for tuning intermolecular interactions and ion mobility. This work provides an interpretable approach for building structure-property relationships for currently available ionic liquid structures and opens new perspectives on how to design ionic liquids for desirable transport behavior. All data and models are shared as opensource code. Ionic liquid ; Ion ; Intermolecular force ; Ionic bonding ; Ionic conductivity ; Ionic potential ; Cheminformatics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Ionic liquids properties and applications
Authors: Lizhong Yang | Xinyu You | Fei Xiao | Long Gao | Alessandro Romagnoli
Source:  (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Machine Learning-driven Melting Point Prediction for Heat Recovery Materials Point (geometry) ; Work (physics) ; Heat recovery ventilation ; Temperature measurement Machine Learning in Materials Science ; Phase Change Materials Research ; Thermal properties of materials
Authors: Zhilong Wang | Fengqi You
Source: Journal of Materials Informatics (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

The synergy of geometric tolerance factor and machine learning in discovering stable materials Assessing stability remains a fundamental prerequisite for deploying materials across a wide range of applications, including batteries, catalysts, and photovoltaics. However, first-principles stability checks such as phonon dispersion and energy above hull calculations typically require days to weeks of computing time per composition, creating a critical bottleneck for truly high-throughput discovery. In this Perspective, we highlight the underutilized potential of geometric tolerance factors (T&lt;sub&gt;f&lt;/sub&gt;) as lightweight yet informative indicators for rapid stability assessment. First, we review the T&lt;sub&gt;f&lt;/sub&gt; developed for representative materials systems, including perovskites, spinels, and garnets, and analyze recent cases where such indicators have been integrated into AI-driven materials discovery. Then, we identify key open challenges in designing T&lt;sub&gt;f&lt;/sub&gt; that are both accurate and generalizable, as well as in effectively incorporating them into AI frameworks. The potential solutions, including active learning for multi-composition structure, electron density profile-based learning for ionic radii estimation, and diffusion model for thermodynamic and kinetic stability, are proposed to address these challenges. The synergy between T&lt;sub&gt;f&lt;/sub&gt;-based heuristics and advanced AI models has the potential to triage vast compositional spaces before committing to expensive first-principles stability validation, thereby enabling broader innovations in materials design and deployment. Stability (learning theory) ; Bottleneck ; Heuristics ; Key (lock) ; Range (aeronautics) ; Boundary (topology) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Perovskite Materials and Applications