jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenyang Cao | Liyi Bai | Shuo Cao | Ye Su | Yanzhou Wang | Zheyong Fan | Ping Qian
Source: ArXiv.org (preprint, 2025-03-26)
Field: Materials Science — Materials Chemistry

Structural and transport properties of LiTFSI/G3 electrolyte with machine-learned molecular dynamics The lithium bis(trifluoromethylsulfonyl)azanide-triglyme electrolyte plays a critical role in the performance of lithium-ion batteries. However, its solvation structure and transport properties at the atomic scale remain incompletely understood. In this study, we develop an efficient and accurate neuroevolution potential (NEP) model by integrating bootstrap and active learning strategies. Using machine-learned NEP-driven molecular dynamics simulations, we explore the structural and diffusion properties of LiTFSI/G3 across a wide range of the solute-to-solvent ratios, systematically analyzing electrolyte density, ion coordination, viscosity, and lithium self-diffusion. The computed densities show excellent agreement with experimental data, and pair correlation analysis reveals significant interactions between lithium ions and surrounding oxygen atoms, which strongly impacts Li$^+$ mobility. Viscosity and diffusion calculations further demonstrate that increasing LiTFSI concentration enhances Li-O interactions, resulting in higher viscosity and reduced lithium diffusion. Additionally, machine learning-based path integral molecular dynamics (PIMD) simulations confirm the negligible impact of quantum effects on Li$^+$ transport. The electrolyte-specific protocol developed in this work provides a systematic framework for constructing high-fidelity machine learning potentials for complex systems. Electrolyte ; Solvation ; Molecular dynamics ; Lithium (medication) ; Viscosity ; Diffusion ; Work (physics) ; Ion Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Guan-Ming Chen | Thijs Stuyver
Source: ChemRxiv (preprint, 2025-06-12)
Field: Chemistry — Spectroscopy

Harnessing Surrogate Models for Data-efficient Predictive Chemistry: Descriptors vs. Learned Hidden Representations Predictive chemistry often faces data scarcity, limiting the performance of machine learning (ML) models. This is particularly the case for specialized tasks such as reaction rate or selectivity prediction. A common solution is to use quantum mechanical (QM) descriptors—physically meaningful features derived from electronic structure calculations—to enhance model robustness in low-data regimes. However, computing these descriptors is costly. Surrogate models address this by predicting QM descriptors directly from molecular structure, enabling fast and scalable input generation for data-efficient downstream ML models. In this study, we compare two strategies for using surrogate models: one that feeds predicted QM descriptors into downstream models, and another that leverages the surrogate’s internal hidden representations instead. Across a diverse set of chemical prediction tasks, we find that hidden representations often outperform QM descriptors, particularly when descriptor selection is not tightly aligned with the downstream task. Only for extremely small datasets or when using carefully selected, task-specific descriptors do the predicted values yield better performance. Our findings highlight that the hidden space of surrogate models captures rich, transferable chemical information, offering a robust and efficient alternative to explicit descriptor use. We recommend this strategy for building data-efficient models in predictive chemistry, especially when feature importance analysis is not a primary goal. Computer science ; Artificial intelligence ; Machine learning ; Chemistry Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Guilherme M. Arantes (8072546) | Jan Řezáč (1279317)
Source: Figshare (article, 2025-06-30)
Field: Chemistry — Inorganic Chemistry

Benchmark of ApproximateQuantum Chemical and MachineLearning Potentials for Biochemical Proton Transfer Reactions Proton transfer reactions are among the most common chemical transformations and are central to enzymatic catalysis and bioenergetic processes. Their mechanisms are often investigated using DFT or approximate quantum chemical methods, whose accuracy directly impacts the reliability of the simulations. Here, a comprehensive set of semiempirical molecular orbital and tight-binding DFT approaches, along with recently developed machine learning (ML) potentials, are benchmarked against high-level MP2 reference data for a curated set of proton transfer reactions representative of biochemical systems. Relative energies, geometries, and dipole moments are evaluated for isolated reactions. Microsolvated reactions are also simulated using a hybrid QM/MM partition. Traditional DFT methods offer high accuracy in general but show markedly larger deviations for proton transfers involving nitrogen-containing groups. Among approximate models, RM1, PM6, PM7, DFTB2-NH, DFTB3, and GFN2-xTB show reasonable accuracy across properties, though their performance varies by chemical group. The ML-corrected (Δ-learning) model PM6-ML improves accuracy for all properties and chemical groups and transfers well to QM/MM simulations. Conversely, standalone ML potentials perform poorly for most reactions. These results provide a basis for evaluating approximate methods and selecting potentials for proton transfer simulations in complex environments. Proton ; Quantum chemical ; Benchmark (surveying) ; Set (abstract data type) ; Transfer (computing) ; Dipole ; Basis set ; Reliability (semiconductor) Metal-Catalyzed Oxygenation Mechanisms ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Jae-Geun Ha | Ho Sung Jang | Sunmi Shin | Chang‐Soo Park | SeMin Park
Source: Journal of Magnetics (article, 2025-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting Grain Size of Fe Si Alloys Steel Sheet through Magnetic Properties and Machine Learning with XAI Measuring the grain size of electrical steel is important for understanding its properties, but until now studies have attempted this using experimental equations or machine learning (ML), resulting in limited models. This study applied three ML and Explainable-AI (XAI) to analyze magnetic properties and identify key the factors most relevant to grain size prediction. Among the three tree-based models, B4_Br/Bm, B10_P<sub>cm</sub> were identified as key variables. It was also found that variables under B1, B4, B10 conditions accounted for 60% of the model explanation ratio. Final model was constructed using these variables. These findings demonstrate the potential of ML with XAI to predict grain size with fewer magnetic measurement. Grain size ; Electrical steel ; Key (lock) ; Experimental data Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Longhui Zhang | Zhouran Zhang | Yiming Zhang | Lihe Chen | Shun Li | Li’an Zhu | Yicong Ye | Rong Chen | Shuxin Bai
Source: Defence Technology (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Machine learning driven impact fracture understanding and end-to-end design of energetic multi-principal-element alloys Fracture (geology) ; Machine design High-Velocity Impact and Material Behavior ; Machine Learning in Materials Science ; High Temperature Alloys and Creep
Authors: Divya Bajpai Tripathy | Subhalaxmi Pradhan | Pooja Agarwal | Rishabha Malviya
Source: Tissue Engineering Part B Reviews (article, 2025-12-04)
Field: Materials Science — Biomaterials

Inorganic Chiral Nanomaterials in Tissue Engineering Applications: Mini Review All inorganic nanomaterials such as gold, silica, and cobalt oxide nanoparticles are transforming tissue engineering by providing enantioselective properties with unique characteristics that are mimicking the chirality of biological systems, allowing the precise modulation of cellular behaviors like differentiation and alignment. It is essential for the regeneration of complex tissues such as bone, cartilage, and neural networks, but their clinical application is being obstructed by considerable challenges such as the inability to sustain consistent chirality during synthesis. There are limited means to characterize their molecular structure, the high cost of their production, which constrains their scalability, and the long-term biocompatibility. There are different concerns of these materials in physiological environments, which call for novel solutions such as machine learning-aided synthesis, bioinspired mineralization, and interfacing with cutting-edge technologies such as 3D and 4D bioprinting to design biomimetic scaffolds that facilitate enhanced tissue regeneration. The personalized strategies that are modifying nanomaterial properties to match the distinct requirements of individual patients have the promise of enhancing therapeutic outcomes, and collaborations among materials science, bioengineering, and clinical expertise are needed to standardize protocols, overcome regulatory barriers, and tap the full potential of these nanomaterials. This review is hence a critical appraisal of their revolutionary potential, present limitations, and future promise in enhancing regenerative medicines. [Figure: see text]. Interfacing ; Tissue engineering ; Nanomaterials ; Regenerative medicine ; Regeneration (biology) ; Nanomedicine ; Biomimetics ; Scaffold Supramolecular Self-Assembly in Materials ; Diatoms and Algae Research ; Bone Tissue Engineering Materials
Authors: Lukas Volkmer | Leonardo Medrano Sandonas | Philip Grimm | Julia Kristin Hufenbach | Gianaurelio Cuniberti
Source: Acta Materialia (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions The development of resilient and lightweight Aluminum alloys is central to advancing structural materials for energy-efficient engineering applications. To address this challenge, in this study, we explore the elastic properties of Al-Mg-Zr solid solutions by integrating advanced machine learning (ML) techniques with quantum-mechanical (QM) atomistic simulations. For this purpose, we develop accurate and transferable machine-learned interatomic potentials (MLIPs) using two complementary approaches: (i) an on-the-fly learning scheme combined with Bayesian linear regression during ab initio molecular dynamics simulations, and (ii) the equivariant neural network architecture MACE. Both MLIPs facilitate the prediction of composition-dependent elastic properties while drastically reducing the computational cost compared to conventional QM methods. Comparison with ultrasonic measurements shows that the deviation between simulation and experiment remains within a few GPa across all Al-Mg-Zr systems investigated. These potentials also enable the systematic exploration of the Al-Mg-Zr solid solution phase space and provide insights into the elastic behavior as a function of alloying element concentration. Hence, our findings demonstrate the reliability and transferability of the parameterized on-the-fly MLIPs, making them valuable for accelerating the design of Al alloys with tailored physicomechanical properties in complex compositional spaces. While the present study focuses on homogeneous phases, it establishes a foundation for future multiscale simulations that include microstructural features such as precipitates and grain boundaries. Interatomic potential ; Ab initio ; Artificial neural network ; Phase (matter) ; Molecular dynamics ; Kriging ; Reliability (semiconductor) ; Transferability ; Parameterized complexity Machine Learning in Materials Science ; Microstructure and mechanical properties ; Model Reduction and Neural Networks
Authors: Deshpande, Aaryesh
Source: arXiv (Cornell University) (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm The convergence of statistical learning and molecular physics is transforming our approach to modeling biomolecular systems. Physics-informed machine learning (PIML) offers a systematic framework that integrates data-driven inference with physical constraints, resulting in models that are accurate, mechanistic, generalizable, and able to extrapolate beyond observed domains. This review surveys recent advances in physics-informed neural networks and operator learning, differentiable molecular simulation, and hybrid physics-ML potentials, with emphasis on long-timescale kinetics, rare events, and free-energy estimation. We frame these approaches as solutions to the "biomolecular closure problem", recovering unresolved interactions beyond classical force fields while preserving thermodynamic consistency and mechanistic interpretability. We examine theoretical foundations, tools and frameworks, computational trade-offs, and unresolved issues, including model expressiveness and stability. We outline prospective research avenues at the intersection of machine learning, statistical physics, and computational chemistry, contending that future advancements will depend on mechanistic inductive biases, and integrated differentiable physical learning frameworks for biomolecular simulation and discovery. Differentiable function ; Artificial neural network ; Intersection (aeronautics) ; Inference ; Consistency (knowledge bases) ; Frame (networking) ; Convergence (economics) ; Deep learning ; Computational learning theory Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Ahmed El Alouani | Mohamed Al Khalfioui | Adrien Michon | S. Vézian | P. Boucaud | Minh Tuan Dau
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Defect formation energy of impurities in 2D materials: how data engineering shapes machine learning model selection? Energy (signal processing) ; Impurity ; Experimental data ; Data processing ; Field (mathematics) ; Data modeling ; Process (computing) Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Advancements in Semiconductor Devices and Circuit Design
Authors: Marko Petković | J. A. Luna | Elīza Beate Dinne | Vlado Menkovski | Sofı́a Calero
Source: ArXiv.org (preprint, 2025-03-26)
Field: Materials Science — Materials Chemistry

Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites Accurately predicting adsorption properties in nanoporous materials using Deep Learning models remains a challenging task. This challenge becomes even more pronounced when attempting to generalize to structures that were not part of the training data.. In this work, we introduce SymGNN, a graph neural network architecture that leverages material symmetries to improve adsorption property prediction. By incorporating symmetry operations into the message-passing mechanism, our model enhances parameter sharing across different zeolite topologies, leading to improved generalization. We evaluate SymGNN on both interpolation and generalization tasks, demonstrating that it successfully captures key adsorption trends, including the influence of both the framework and aluminium distribution on CO$_2$ adsorption. Furthermore, we apply our model to the characterization of experimental adsorption isotherms, using a genetic algorithm to infer likely aluminium distributions. Our results highlight the effectiveness of machine learning models trained on simulations for studying real materials and suggest promising directions for fine-tuning with experimental data and generative approaches for the inverse design of multifunctional nanomaterials. Adsorption ; Generalization ; Artificial neural network ; Nanoporous ; Graph ; Zeolite ; Key (lock) ; Inverse Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Sudip Chakraborty | Praval Pratap Singh | Jasmeen Kaur | Mohit Jakhar | Shihan VC | Chandraniv Dey | Kavita Thakran
Source: ChemRxiv (article, 2025-12-29)
Field: Chemistry — Organic Chemistry

Hybrid ChemBERTa and DFT Machine Learning Framework for Predicting Enantioselectivity in Organosilanes Mediated Carbonyl Reduction Reactions Interpretability ; Selectivity ; Construct (python library) ; Reduction (mathematics) ; Quantum chemical ; Training set ; Predictive power Organoboron and organosilicon chemistry ; Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: Van-Tung Nguyen | Dung Nguyen | Giang Bui | Thien Binh Nguyen | Thy Thi Anh Ho
Source: Energy and Buildings (article, 2025-11-06)
Field: Materials Science — Polymers and Plastics

Predicting energy usage in fixed-area office buildings: A comprehensive ANN-based study with electrochromic glazing Glazing ; Parametric statistics ; Field (mathematics) ; Deep learning ; Energy consumption ; Energy (signal processing) ; Parametric design ; Building science ; Building energy simulation Transition Metal Oxide Nanomaterials ; Building Energy and Comfort Optimization ; Urban Heat Island Mitigation
Authors: Michael Scherbela | Nicholas Gao | Philipp Grohs | Stephan Günnemann
Source: ArXiv.org (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, FiRE reduces the asymptotic complexity of neural-network variational Monte Carlo (NN-VMC) by $\sim n_\text{el}$, the number of electrons. By restricting electron-electron interactions within the neural network, FiRE accelerates all key operations -- sampling, pseudopotentials, and Laplacian computations -- resulting in a real-world $10\times$ acceleration in now-feasible 180-electron calculations. We validate our method's accuracy on various challenging systems, including biochemical compounds, conjugated hydrocarbons, and organometallic compounds. On these systems, FiRE's energies are consistently within chemical accuracy of the most reliable data, including experiments, even in cases where high-accuracy methods such as CCSD(T), AFQMC, or contemporary NN-VMC fall short. With these improvements in both runtime and accuracy, FiRE represents a new `gold-standard' method for fast and accurate large-scale ab-initio calculations, potentially enabling new computational studies in fields like quantum chemistry, solid-state physics, and material design. Ansatz ; Electronic structure ; Wave function ; Computation ; Monte Carlo method ; Key (lock) ; Acceleration Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Ihsan ulhaq Toor
Source: International Journal of Electrochemical Science (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Artificial intelligence and machine learning in corrosion inhibitor design &amp; development: Advances, challenges, and future perspectives The emerging field of artificial intelligence (AI) and machine learning (ML) has opened new frontiers in corrosion science, particularly in the design, screening and performance prediction of corrosion inhibitors. Traditional experimental and quantum chemical approaches, while reliable, are often time-consuming and limited by empirical correlations. AI and ML driven models now offer a data-intensive alternative capable of predicting inhibitor efficiency, adsorption behavior, and electrochemical response with remarkable precision. Here in this study, recent progress in applying AI and ML algorithms such as artificial neural networks, support vector machines, decision trees, and deep learning frameworks to predict corrosion inhibition efficiency, adsorption mechanisms, and electrochemical parameters derived from potentiodynamic and impedance measurements are critically examined. The study reviews the data foundation essential for AI workflows including quantum, electrochemical, and image-based descriptors along with classical (SVR, RF, ANN), deep-learning (3L-DMPNN, ChemBERTa), and hybrid quantum ML architectures for inhibition efficiency prediction. Emerging generative models like MoIGPT have demonstrated the ability to design molecules conditioned on factors such as performance and toxicity. Meanwhile, integrated AI Electrochemistry pipelines connect machine learning predictions directly to experimental validation through electrochemical impedance spectroscopy and potentiodynamic polarization techniques. Despite remarkable advances, challenges remain in data standardization, model interpretability, scalability, and sustainability. Addressing these bottlenecks through FAIR data infrastructure, explainable and trustworthy AI, and green computational practices, will be critical for realizing the long-term vision of fully autonomous, eco-conscious, and self-optimizing corrosion-management ecosystems. Corrosion ; Artificial neural network ; Dielectric spectroscopy ; Deep learning ; Support vector machine ; Corrosion inhibitor ; Trustworthiness Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Dengpeng; id_orcid 0000-0002-9160-4533 Huang | Mark Tankink | Anke Blume
Source: University of Twente Research Information (article, 2025-03-05)
Field: Materials Science — Materials Chemistry

Prediction of elastomer properties using Convolutional Neural Networks (CNN) To predict the elastomer properties from its microstructures, material models in different scales have been developed ranging from the molecular level to the macro level. However, the application of these models is limited by either the computational costs or their accuracy. An efficient but accurate modelling is still missing. Due to its promising performances in prediction, machine learning provides a suitable alternative to traditional methods. In this study, the tensile properties of elastomers are predicted from the expanded TEM images using the Convolutional Neural Networks (CNN). The CNN model shows good accuracy and efficiency in prediction. Elastomer ; Convolutional neural network ; Ranging ; Artificial neural network ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Elasticity and Material Modeling
Authors: Wanli Jia | Pengcheng Ma | Qi Guan | Lin Zhang | Xinmei Wang | Ruofan Shen | Wei Wang
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Accelerated exploration of direct band gap transition metal dichalcogenide monolayers through machine learning-driven discovery Monolayer ; Transition metal ; Transition (genetics) ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Catalysis 2D Materials and Applications ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties