jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thirukumaran Periyasamy | Shakila Parveen Asrafali | Jaewoong Lee
Source: Micromachines (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Emerging Electrode Materials for Next-Generation Electrochemical Devices: A Comprehensive Review The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium's abundance, necessitates materials like hard carbon for the anode, as sodium's larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal-nitrogen-carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel-iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes-two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance-and Single-Atom Catalysts (SACs)-which maximize metal utilization-are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Electrochemical energy conversion ; Anode ; Catalysis ; Electrochemistry ; Supercapacitor ; Graphite ; Oxygen evolution ; Energy storage ; Fuel cells ; Electrocatalyst MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Ragnar Björnsson
Source: ChemRxiv (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

ASH: a multi-scale, multi-theory modelling program We introduce ASH, a multi-scale, multi-theory modelling program for quantum mechanics (QM), molecular mechanics (MM) and hybrid calculations, written in the Python programming language. ASH is written in response to the increasingly diverse computational chemistry software landscape that features more QM and MM programs than ever before and with machine learning interatomic potentials (MLIP) further changing the way modern computational chemistry is being performed. ASH is a Python library that intentionally separates computational chemistry jobs (geometry optimizations, frequency calculations, molecular dynamics, surface scans, reaction paths etc.) from the QM, MM or ML method (calculated by the specialized QM or MM programs or ML libraries). By keeping the jobs separate from the Hamiltonian, a highly flexible computational chemistry environment emerges, that can be used in workflows involving QM methods (using interfaces to many different QM-programs), classical MM methods with multiple forcefields (via an interface to the OpenMM library), machine-learning potentials or hybrid methods. ASH is especially powerful as a program for performing hybrid simulations: including QM/MM, QM/ML, ML/MM, QM+ML or ONIOM calculations for proteins, solvated molecules or molecular crystals. Molecular dynamics and enhanced sampling can be performed using any level of theory allowing for highly flexible freeenergy simulations (such as metadynamics) enabled by interfaces to algorithms in OpenMM and Plumed. There are flexible interfaces to many QM programs such as ORCA, xTB, pySCF, CP2K, MRCC, Turbomole, CFour and many others. Python (programming language) ; ONIOM ; Workflow ; Software ; Molecular dynamics ; Interface (matter) ; Computational mechanics ; Quantum chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Scientific Computing and Data Management
Authors: Bo Bo Han | Shobhit K. Patel | Ashish Baldania | Yogesh Sharma | Fahad Ahmed Alzahrani
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Smart graphene-enhanced ceramic material refractive index sensor simulation design developed for highly sensitive breast Cancer detection optimized with machine learning Ceramic ; Refractive index ; Breast cancer ; Chemical sensor Graphene research and applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: Oluwafemi Babatunde Olasilola | Adeola Ajoke Oni | Olawale Rukayat Abisola | Adeyinka G. Ologun | Amirlahi Ademola Fajingbesi | Kemi K. Oladapo | Francis T. Omigbodun
Source: Future Batteries (article, 2026-01-22)
Field: Materials Science — Biomaterials

Smart magnesium batteries: Using AI to power greener and more reliable desalination systems This study develops a focused AI-based optimisation framework to improve the performance of magnesium alloy batteries for renewable-powered desalination systems. The objective is to enhance voltage stability, reduce internal resistance, and extend cycle life through coordinated optimisation of material and operating parameters. An analytical–simulation methodology is adopted, combining electrochemical degradation models with machine learning prediction and genetic algorithm optimisation. Key variables include alloy composition, electrolyte type, operating temperature, and current density. Neural networks were trained using a literature-anchored dataset and validated through cross-validation, while genetic algorithms were used to identify optimal multi-objective operating conditions. The optimised Mg–Al configurations demonstrated a 25% reduction in voltage degradation, a 50% decrease in internal resistance , and a 20% increase in cycle life compared with baseline (non-optimised) conditions, achieving up to 220 stable cycles. The predictive models attained a 94.5% accuracy with a root mean square error of 0.015 V , indicating low prediction uncertainty and robust generalisation within the studied domain. These quantified improvements translate into higher energy efficiency and reduced maintenance demand for desalination applications. Overall, the results confirm that AI-assisted optimisation provides a reliable, resource-efficient pathway for designing sustainable magnesium-based energy storage systems aligned with circular economy objectives. Desalination ; Genetic algorithm ; Internal resistance ; Reduction (mathematics) ; Voltage ; Power (physics) ; Mean squared error ; Energy (signal processing) Magnesium Alloys: Properties and Applications ; Membrane-based Ion Separation Techniques ; Advanced battery technologies research
Authors: Kunpeng Xu | Ning Zhang | Zhen-Yu Yin | Kai-Qi Li
Source: International Journal of Mechanical Sciences (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Hierarchical physics-guided neural network for sparse-data heterogeneous material identification 202603 bcch Artificial neural network ; Identification (biology) ; Pattern recognition (psychology) ; System identification ; Deep neural networks Machine Learning in Materials Science ; Advanced Neural Network Applications ; Model Reduction and Neural Networks
Authors: Yaoxun Feng | Qing Xu | Changqing Li
Source: Sustainability (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

A Novel Intelligent Prediction Model for Higher Heating Value of Sustainable Solid Biomass Fuel Based on Bayesian Optimized Deep Neural Network Biomass energy is recognized as a clean and sustainable energy source and is leveraged as a key enabler for driving the low-carbon transition of the energy system and achieving sustainable development. The higher heating value of solid biomass fuels (HHV-SBF) is a key parameter in its catalytic conversion process, and HHV-SBF is of great significance for catalyst design and matching, as well as the selection of reaction process parameters. To address the limitations in accuracy and generalization capability of traditional prediction methods for estimating the HHV-SBF, a dataset is constructed in this study that correlates chemical elements, proximate analysis parameters, and biochemical components with the HHV-SBF. Key hyperparameters of the deep neural network (DNN) are optimized using the Bayesian optimization algorithm. A Bayesian optimization-based deep neural network (BO-DNN) model is developed for the intelligent prediction of the HHV-SBF. Results show that the coefficient of determination (R2) of the BO-DNN model reaches 92.6%. Compared to multiple mainstream deep learning algorithms, its performance is improved by approximately 11.61%, and the mean square error is significantly reduced. The BO-DNN model demonstrates excellent generalization capability and stability. The findings of this study provide a theoretical basis for the rapid and accurate prediction of the HHV-SBF. Artificial neural network ; Hyperparameter ; Key (lock) ; Generalization ; Bayesian optimization ; Bayesian probability ; Process (computing) ; Energy (signal processing) Machine Learning in Materials Science ; Thermochemical Biomass Conversion Processes ; Catalysis and Oxidation Reactions
Authors: Pengju Yin | Yumeng Xiao | Chenyao Feng | Xiaoyao Wu | Xinyuan Wang | Dmitriy Klyuyev | Zengrui Wu | Yiping Zhao | Bo Hu
Source: Analytical Chemistry (article, 2026-01-28)
Field: Chemistry — Analytical Chemistry

Decoding Complex Spectra with Machine Learning: Advances in Spectral Unmixing for Chemical Analysis Decoding methods ; Spectral analysis ; Spectral line ; Pattern recognition (psychology) ; Complex matrix ; Qualitative analysis Spectroscopy and Chemometric Analyses ; Molecular spectroscopy and chirality ; Machine Learning in Materials Science
Authors: Hang Fu | Qingshan Cai | Yifan Han | Zhikai Li | Tianhao Wu | Yunzhu Ma | Wensheng Liu
Source: Materials & Design (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Unlocking strength-ductility synergy in tungsten heavy alloys through interpretable machine learning Tungsten ; Artificial neural network Machine Learning in Materials Science ; Advanced materials and composites ; Titanium Alloys Microstructure and Properties
Authors: Anonymous | P. Liu | Zhuohang Xie | Wenhui Mi | Pengyue Gao | Hong Jian Zhao | Jian Lv | Yanchao Wang | Yanming Ma
Source: Physical review. B./Physical review. B (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures Bridging (networking) ; Compatibility (geochemistry) ; Mode (computer interface) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Elisa Herrera | Antonio Gil | Manuel Algarra
Source: Advances in Colloid and Interface Science (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Emerging synthetic strategies, tunable properties, and multifunctional applications of metal-doped carbon dots: A review Carbon fibers ; Metal ions in aqueous solution ; Metal ; Variety (cybernetics) ; Chemical sensor Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Matthew S. Johnson | Richard Henry West | Judit Zádor
Source: Faraday Discussions (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

An Interpretable Machine Learning Framework for Prediction of Adsorption Energies and Generative Design of Active Sites on Arbitrary Catalysts We present a highly interpretable and efficient machine learning framework for predictive and generative modeling of adsorption energies on surfaces using subgraph isomorphic decision trees (SIDTs). Extracting graph representations of... Generative grammar ; Adsorption ; Graph ; Generative model ; Decision tree ; Support vector machine Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: L. C. Zhang | Xiaoyu Sun | Jingqing Li | Lunyang Liu | Tao Liao | Ying Lu | Wenbo Yuan | Hongyuan Chen | Hongfei Li | Yongfeng Men | Shichun Jiang
Source: Polymer (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Machine learning prediction of mechanical properties of polyolefins from stress-strain curves Artificial neural network ; Extreme learning machine ; Ultimate tensile strength ; Random forest ; Polymer ; Polypropylene ; Boosting (machine learning) ; Experimental data Machine Learning in Materials Science ; Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies
Authors: Samar M. Fawzy | Mohammed K. M. Ali | Nageh K. Allam
Source: ACS Sustainable Chemistry & Engineering (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Driven Materials Design for Next-Generation Sustainable Energy Technologies The increasing complexity of materials discovery necessitates a shift from traditional trial-and-error approaches to fully integrated, AI-driven workflows. In this Perspective, we introduce a constraint-aware, AI-guided framework designed to systematically explore vast chemical and structural spaces and generate novel materials for sustainable energy applications. Our workflow integrates high-throughput, energy-informed computations with machine learning (ML), physics-informed generative models, experimental feedback, and uncertainty quantification, all aligned with sustainability objectives. Central to this approach are advanced ML techniques and generative models that ensure that the proposed materials are both chemically feasible and functionally optimized. We highlight the transformative potential of closed-loop AI-driven discovery to accelerate development across key energy technologies, including batteries, catalysts, photovoltaics, and thermoelectrics. By positioning AI not merely as a predictive tool but as an autonomous research partner, this perspective provides a roadmap for rapidly designing, validating, and deploying next-generation sustainable energy materials. Workflow ; Transformative learning ; Sustainability ; Key (lock) ; Generative grammar ; Efficient energy use ; Sustainable energy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Ze Li | Xin Qian | Tao Liu | Yi Wang | Xiaoliang Huang | Hongchang Duan | Zhao Zhang | Zongzhuang Sun | Hao Yan | Xiaobo Chen | Chaohe Yang | Lianying Wu | Xin Zhou
Source: Fuel (article, 2026-01-07)
Field: Chemistry — Analytical Chemistry

Enhancing engineering strategies for direct catalytic cracking of crude oil via Molecular-Level kinetic reaction Networks: A Machine Learning-Enabled Multi-Objective optimization framework Fluid catalytic cracking ; Product distribution ; Cracking ; Catalysis ; Raw material ; Process (computing) ; Crude oil ; Olefin fiber Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Heat transfer and supercritical fluids
Authors: Jun Deng | Xuejun Zhu | Yuanchuan Ren | Cheng Wang | Nanqi Ren
Source: Nano Materials Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Site surgery in single atom catalysis: machine learning assisted precise regulation of coordination environment and decoding of structure-activity relationships Decoding methods ; Atom (system on chip) ; Pattern recognition (psychology) ; Perspective (graphical) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Yuichi Kamata | Quoc Hoan Tran | Yasuhiro Endo | Hirotaka Oshima
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum Transformer for Molecular Learning: Multi-Configuration Ground-State Energy Prediction The Transformer model, renowned for its powerful attention mechanism, has achieved state-of-the-art performance in various artificial intelligence tasks but faces challenges with quantum data. With a growing focus on leveraging quantum machine learning for quantum data, particularly in quantum chemistry, we propose the Molecular Quantum Transformer (MQT) for modeling interactions in molecular quantum systems. By utilizing quantum circuits to implement the attention mechanism on the molecular configurations, MQT can efficiently calculate ground-state energies for all configurations. Numerical demonstrations show that in calculating ground-state energies for H2, LiH, BeH2, and H4, MQT outperforms the classical Transformer, highlighting the promise of quantum effects in Transformer structures. Furthermore, its pretraining capability on diverse molecular data facilitates the efficient learning of new molecules, extending its applicability to complex molecular systems with minimal additional effort. Our method offers an alternative to existing quantum algorithms for estimating ground-state energies, opening new avenues in quantum chemistry and materials science. Quantum ; Quantum computer ; Quantum machine learning ; Transformer ; Quantum algorithm ; Quantum annealing ; Quantum sensor ; Quantum information Various Chemistry Research Topics
Authors: Chen Qian | Valdas Vitartas | James R. Kermode | Reinhard J. Maurer
Source: npj Computational Materials (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Equivariant electronic Hamiltonian prediction with many-body message passing Abstract Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant O (3) irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to f orbital matrix interaction blocks. We demonstrate the model’s accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening. Message passing ; Locality ; Eigenvalues and eigenvectors ; Generalization ; Artificial neural network ; Distance matrix ; Hamiltonian (control theory) ; Benchmark (surveying) ; Computation Machine Learning in Materials Science ; Quantum many-body systems ; Topological Materials and Phenomena
Authors: Shaojing Dong | Weihan Sun | Xiuli SHEN
Source: Materials Today Communications (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Prediction method for crystal plasticity constitutive parameters in single-crystal superalloys via two-stage tiered mapping neural network based on nanoindentation Nanoindentation ; Superalloy ; Crystal plasticity ; Plasticity ; Artificial neural network ; Constitutive equation Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Metallurgy and Material Forming
Authors: Qing Li | Haikuan Dong | Penghua Ying | Zheyong Fan
Source: International Journal of Heat and Mass Transfer (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Anisotropic and isotropic elasticity and thermal transport in monolayer C24 networks from machine-learning molecular dynamics Two-dimensional fullerene networks have recently attracted increasing interest due to their diverse bonding topologies and mechanically robust architectures. In this work, we develop an accurate machine-learned potential NEP-C 24 for both the quasi-hexagonal phase (qHP) and the quasi-tetragonal phase (qTP) C 24 monolayers, based on the neuroevolution potential (NEP) framework. Using this NEP-C 24 model, we systematically investigate the elastic and thermal transport properties. Compared with C 60 monolayers, both C 24 phases exhibit markedly enhanced stiffness, arising from the combination of reduced molecular size and an increased number density of inter-fullerene covalent bonds (approximately 14.1 bonds/ nm 2 for qHP C 24 versus 5.6 bonds/ nm 2 for qHP C 60 . The qTP C 24 monolayer shows nearly isotropic elastic properties and thermal conductivities along its two principal axes owing to its four-fold symmetry, whereas the chain-like, misaligned bonding topology of the qHP C 24 monolayer leads to pronounced in-plane anisotropy. Homogeneous nonequilibrium molecular dynamics and spectral decomposition analyses reveal that low-frequency ( < 5 THz) acoustic phonons dominate heat transport, with directional variations in phonon group velocity and mean free path governing the anisotropic response in qHP C 24 . Real-space heat flow visualizations further show that, in these fullerene networks, phonon transport is dominated by strong inter-fullerene covalent bonds rather than weak van der Waals interactions. These findings establish a direct link between intermolecular bonding topology and phonon-mediated heat transport, providing guidance for the rational design of fullerene-based two-dimensional materials with tunable mechanical and thermal properties. • Accurate NEP model is developed for qHP and qTP C 24 monolayers. • qHP C 24 shows thermal transport anisotropy while qTP C 24 is isotropic. • C 24 networks outperform C 60 in stiffness and thermal conductivity. • Low-frequency acoustic phonons dominate heat conduction. • High bond density enables superior thermal transport in C 24 . Phonon ; Isotropy ; Anisotropy ; van der Waals force ; Molecular dynamics ; Monolayer ; Intermolecular force ; Covalent bond ; Elasticity (physics) Thermal properties of materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research