jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Waqas Ahmad | Muhammad Zubair Nawaz | Jamal Kazmi | Umer Younis | Yu. Yu. Illarionov | Zhiming M. Wang
Source: Advanced Optical Materials (article, 2025-06-21)
Field: Materials Science — Materials Chemistry

Two‐Dimensional Indium Chalcogenides: From Fundamental Properties to Next‐Generation Optoelectronic Devices Abstract 2D indium chalcogenides, such as InSe and In 2 Se 3 , represent a significant class of functional materials characterized by tunable band gaps, exceptional carrier mobility, and robust light–matter interactions. This review thoroughly examines synthesis techniques, including chemical vapor deposition, molecular beam epitaxy, mechanical, and liquid exfoliation methods. It further highlights cutting‐edge characterization methodologies such as atomic force microscopy, scanning electron microscopy, and Raman spectroscopy, providing essential insights into the optoelectronic properties of these materials. The remarkable features of 2D indium chalcogenides facilitate their implementation in diverse optoelectronic applications, notably high‐performance photodetectors, polarization‐sensitive devices, and imaging systems. These materials also offer broadband spectral responsiveness and compatibility with van der Waals heterostructures, enhancing device performance. However, critical challenges such as scalability, environmental stability, and interface quality still hinder their widespread technological adoption. The review addresses these issues by recommending refined fabrication approaches, sophisticated interface engineering, and innovative encapsulation techniques. Finally, it outlines promising future directions, emphasizing scalable synthesis, hybrid device engineering, and the integration of machine learning, thereby paving the way for next‐generation photodetection technologies. This comprehensive review underscores the transformative potential of 2D indium chalcogenides and establishes a clear roadmap for overcoming existing challenges to realize their capabilities thoroughly. Materials science ; Nanotechnology ; Indium ; Optoelectronics ; Heterojunction ; Photonics ; Engineering physics ; Physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Guoliang Zhu | Jagadeesh Suriyaprakash | Guangri Chen | Dapeng Hong | Jiajun Gu | Yanyang Zhang | Lianwei Shan | Huanyan Xu | Xiaojing Li | Haitao Wu | Limin Dong | Shuyan Qi | Xinxin Jin | Gang Liu
Source: Chemical Engineering Journal (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Multi-dimensional decoupling local electron spin and inner Helmholtz layer roles in MoS2/MoSe2 superlattice photocatalysts Decoupling (probability) ; Superlattice ; Electron ; Layer (electronics) ; Photocatalysis ; Materials science ; Helmholtz free energy ; Condensed matter physics ; Spin (aerodynamics) ; Chemistry ; Nanotechnology ; Optoelectronics ; Physics ; Engineering ; Quantum mechanics ; Thermodynamics ; Organic chemistry ; Catalysis 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Advanced Photocatalysis Techniques
Authors: Jingzhi Yang | Junsen Zhao | Xin Guo | Yami Ran | Zhongheng Fu | Hongchang Qian | Lingwei Ma | Patrick Keil | J.M.C. Mol | Dawei Zhang
Source: Corrosion Science (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Combinatorial discovery and investigation of the synergism of green amino acid corrosion inhibitors: Integrating high-throughput experiments and interpretable machine learning approach Corrosion ; Throughput ; Drug discovery ; High-throughput screening ; Computer science ; Corrosion inhibitor ; Machine learning ; Artificial intelligence ; Combinatorial chemistry ; Biochemical engineering ; Nanotechnology ; Materials science ; Chemistry ; Engineering ; Metallurgy ; Biochemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ranbir Singh | N. Nagabhooshanam | Y. P. Thakur | Deepak Nathiya | M. D. Anto Praveena | Umesh Kumar | A. Rajaram
Source: Plasmonics (article, 2025-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-driven discovery of alloyed plasmonic nanodisks for broadband solar absorption and charge carrier multiplication Plasmon ; Broadband ; Absorption (acoustics) ; Materials science ; Optoelectronics ; Multiplication (music) ; Multiple exciton generation ; Charge (physics) ; Charge carrier ; Solar cell ; Telecommunications ; Physics ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Plasmonic and Surface Plasmon Research
Authors: Aiman Tarıq | Büşra Uzun | Babür Deliktaş | Mustafa Özgür Yaylı
Source: Engineering Applications of Artificial Intelligence (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Bending analysis of cantilever microbeams with three porosity distributions using physics-informed neural network and modified couple stress theory This study explores the use of a Physics-Informed Neural Network (PINN) framework to investigate the bending behavior of a cantilever microbeam made of porous material. PINN is a powerful approach that combines machine learning with physics principles to address the challenges of limited training data and enforce domain knowledge into the learning process, making them effective surrogate solvers for Partial Differential Equations (PDEs). In this work, a cantilever microbeam subjected to a uniformly distributed transverse load is examined, considering three different pore distributions including homogeneous, symmetric, and non-symmetric. The bending analysis incorporates the size effect by integrating the modified couple stress theory with the Euler-Bernoulli beam theory. First, the bending equation based on the modified couple stress theory is extended to include porous material properties. The governing equation is then solved using the Laplace transform. The PINN model is trained to approximate the solution by minimizing a loss function that accounts for residual errors at collocation points, as well as initial and boundary conditions. To enhance computational efficiency, the optimal hyperparameters of the PINN model are determined using a combination of Taguchi design of experiments and the Grey Relational Method. Taguchi-Grey approach effectively captures the trade-off between these objectives by normalizing and aggregating them into a single value to reflect the overall performance. The results are validated against analytical solutions based on Laplace transform, and the influence of key parameters such as microbeam length, length scale parameter, and porosity is systematically investigated. Cantilever ; Computer science ; Bending ; Artificial neural network ; Porosity ; Stress (linguistics) ; Composite material ; Artificial intelligence ; Materials science Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Force Microscopy Techniques and Applications
Authors: Kai Zhu | Enrico Trizio | Jintu Zhang | Renling Hu | Linlong Jiang | Tingjun Hou | Luigi Bonati
Source: Chemical Reviews (review, 2025-10-22)
Field: Materials Science — Materials Chemistry

Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling. Focus (optics) ; Reinforcement learning ; Generative grammar ; Sampling (signal processing) ; Phase (matter) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Anomaly Detection Techniques and Applications
Authors: Wu‐Xing Zhou | Chengwei Wu | Hongyang Cao | Yu‐Jia Zeng | Guofeng Xie | Gang Zhang
Source: Materials Today Physics (article, 2025-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Abnormal thermal conductivity increase in β-Ga2O3 by an unconventional bonding mechanism using machine-learning potential Materials science ; Mechanism (biology) ; Thermal conductivity ; Thermal ; Chemical physics ; Engineering physics ; Composite material ; Thermodynamics Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties
Authors: Xingyue Ma | Hongying Chen | Ri He | Zhanbo Yu | Sergei Prokhorenko | Zheng Wen | Zhicheng Zhong | Jorge Íñiguez | L. Bellaïche | Di Wu | Yurong Yang
Source: npj Computational Materials (article, 2025-03-13)
Field: Materials Science — Materials Chemistry

Active learning of effective Hamiltonian for super-large-scale atomic structures Abstract The first-principles-based effective Hamiltonian scheme provides one of the most accurate modeling techniques for large-scale structures, especially for ferroelectrics. However, the parameterization of the effective Hamiltonian is complicated and can be difficult for some complex systems such as high-entropy perovskites. Here, we propose a general form of effective Hamiltonian and develop an active machine-learning approach to parameterize the effective Hamiltonian based on Bayesian linear regression. The parameterization is employed in molecular dynamics simulations with the prediction of energy, forces, stress and their uncertainties at each step, which decides whether first-principles calculations are executed to retrain the parameters. Structures of BaTiO 3 , PbTiO 3 , Pb(Zr 0.75 Ti 0.25 )O 3 , and (Pb,Sr)TiO 3 system are taken as examples to show the accuracy of this approach, as compared with conventional parametrization method and experiments. This machine-learning approach provides a universal and automatic way to compute the effective Hamiltonian parameters for any considered complex systems with super-large-scale (more than 10 7 atoms) atomic structures. Atomic units ; Hamiltonian (control theory) ; Scale (ratio) ; Computer science ; Physics ; Mathematics ; Mathematical optimization ; Quantum mechanics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Umme Javeria | Sung Jin Kim
Source: Journal of Materials Research and Technology (review, 2025-07-30)
Field: Materials Science — Metals and Alloys

Investigation of hydrogen embrittlement in steel alloys: mechanism, factors, advanced methods and materials, applications, challenges, and future directions: A review Hydrogen embrittlement (HE) remains a critical challenge in structural metals, particularly steels, compromising their mechanical integrity in hydrogen-containing environments. The process typically initiates with crack nucleation and progresses to fracture due to a reduction in ductility and an increase in brittleness. This poses serious reliability concerns for industries, such as the automotive, oil and gas, and construction industries, that heavily rely on steel structures. To effectively address this issue, it is essential to first understand the underlying mechanisms and influencing factors of HE, which inform the development of appropriate mitigation strategies. Researchers have explored various mitigation methods, including the application of metallic or ceramic coatings, microstructural optimization, and alloying approaches. This review provides a comprehensive overview of i) the mechanisms of HE in both surface and bulk regions of steel, ii) the factors influencing the severity of HE, iii) advanced analytical approaches–such as thermal desorption spectroscopy (TDS), density functional theory (DFT), machine learning (ML) atom probe tomography (APT), and others–for elucidating HE mechanisms, iv) effective methods to prevent or minimize HE, and v) practical applications in automotive, oil and gas, and marine structures. Lastly, this review critically discusses the major challenges currently faced by the steel industry and outlines potential solutions and future research directions. Materials science ; Hydrogen embrittlement ; Embrittlement ; Metallurgy ; Mechanism (biology) ; Hydrogen ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Welding Techniques and Residual Stresses
Authors: Philip Loche | Kevin K. Huguenin-Dumittan | Melika Honarmand | Qianjun Xu | Egor Rumiantsev | Wei Bin How | Marcel F. Langer | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Fast and flexible long-range models for atomistic machine learning Most atomistic machine learning (ML) models rely on a locality ansatz and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physical effects-most notably electrostatics. Many approaches have been proposed to overcome these limitations, but efforts to make them efficient and widely available are hampered by the need to incorporate an ad hoc implementation of methods to treat long-range interactions. We develop a framework aiming to bring some of the established algorithms to evaluate non-bonded interactions-including Ewald summation, classical particle-mesh Ewald, and particle-particle/particle-mesh Ewald-into atomistic ML. We provide a reference implementation for PyTorch as well as an experimental one for JAX. Beyond Coulomb and more general long-range potentials, we introduce purified descriptors that disregard the immediate neighborhood of each atom and are more suitable for general long-range ML applications. Our implementations are fast, feature-rich, and modular: They provide an accurate evaluation of physical long-range forces that can be used in the construction of (semi)empirical baseline potentials; they exploit the availability of automatic differentiation to seamlessly combine long-range models with conventional, local ML schemes; and they are sufficiently flexible to implement more complex architectures that use physical interactions as building blocks. We benchmark and demonstrate our torch-pme and jax-pme libraries to perform molecular dynamics simulations, train ML potentials, and evaluate long-range equivariant descriptors of atomic structures. Range (aeronautics) ; Computer science ; Benchmark (surveying) ; Ewald summation ; Exploit ; Modular design ; Electrostatics ; Artificial intelligence ; Statistical physics ; Molecular dynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Mona Lisa Moura de Oliveira | Camila Maria Aguiar da Costa Alves | Carla Freitas de Andrade | Diana C. S. Azevedo | Fernanda Leite Lobo | A. Fuerte | Paloma Ferreira-Aparicio | C. Caravaca | Rita X. Valenzuela
Source: ACS Omega (review, 2025-01-21)
Field: Materials Science — Materials Chemistry

Recent Progress and Perspectives on Functional Materials and Technologies for Renewable Hydrogen Production Scientists worldwide have been inspecting hydrogen production routes and showing the importance of developing new functional materials in this domain. Numerous research articles have been published in the past few years, which require records and analysis for a comprehensive bibliometric and bibliographic review of low-carbon hydrogen production. Hence, a data set of 297 publications was selected after filtering journal papers published since 2010. The search keywords in the Scopus Database were "<i>green hydrogen</i>" and "<i>low carbon hydrogen production and materials</i>". The data were analyzed using the R Bibliometrix package. This analysis made it possible to determine the total annual publication rate and to segregate it by country, author, journal, and research institution. With a general upward trend in the total number of publications, China was identified as the leading country in research on the subject, followed by Germany and Korea. Keyword analysis and the chronological evolution of several important publications related to the topic showed the focus was on water splitting for low-carbon H<sub>2</sub> production. Finally, this review provides future directions for technologies and functional materials for low-carbon hydrogen production. Hydrogen technologies ; Production (economics) ; Hydrogen production ; Renewable energy ; Hydrogen economy ; Hydrogen ; Engineering ; Economics ; Chemistry Hydrogen Storage and Materials ; Advancements in Solid Oxide Fuel Cells ; Hybrid Renewable Energy Systems
Authors: Xiang Chen | Mingkang Liu | Shun‐Gao Yin | Yuchen Gao | Nan Yao | Qiang Zhang
Source: Angewandte Chemie International Edition (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Uni‐Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries Electrolytes are an essential part of rechargeable batteries, such as lithium batteries. However, electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (>10<sup>60</sup> for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni-Electrolyte, for designing advanced electrolyte molecules, which mainly includes three parts, i.e., EMolCurator, EMolForger, and EMolNetKnittor. New molecules can be designed by combining high-throughput screening and generative AI models from more than 100 million alternative molecules in the EMolCurator module. The molecular properties, including frontier molecular orbital information, formation energy, binding energy with a Li ion, viscosity, and dielectric constant, can be adopted as the screening parameters. The EMolForger and EMolNetKnittor modules can predict the retrosynthesis pathway and solid electrolyte interphase (SEI) formation mechanism for a given molecule, respectively. With the assistance of advanced AI methods, the Uni-Electrolyte is strongly supposed to discover new electrolyte molecules and chemical principles, promoting the practical application of next-generation rechargeable batteries. Electrolyte ; Molecule ; Nanotechnology ; Materials science ; Chemistry ; Electrode ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Chemistry and Chemical Engineering
Authors: Zongpeng Ma | Pablo Solís‐Fernández | Kaito Hirata | Yung‐Chang Lin | Keisuke Shinokita | Mina Maruyama | Kota Honda | Tatsuki Kato | Aika Uchida | Hiroto Ogura | Tomohiro Otsuka | Masahiro Hara | Kazunari Matsuda | Kazu Suenaga | Susumu Okada | Toshiaki Kato | Yasufumi Takahashi | Hiroki Ago
Source: Science Advances (article, 2025-01-08)
Field: Materials Science — Materials Chemistry

Lattice-guided growth of dense arrays of aligned transition metal dichalcogenide nanoribbons with high catalytic reactivity Transition metal dichalcogenides (TMDs) exhibit unique properties and potential applications when reduced to one-dimensional (1D) nanoribbons (NRs), owing to quantum confinement and high edge densities. However, effective growth methods for self-aligned TMD NRs are still lacking. We demonstrate a versatile approach for lattice-guided growth of dense, aligned MoS<sub>2</sub> NR arrays via chemical vapor deposition (CVD) on anisotropic sapphire substrates, without tailored surface steps. This method enables the synthesis of NRs with widths below 10 nanometers and longitudinal axis parallel to the zigzag direction, being also extensible to the growth of WS<sub>2</sub> NRs and MoS<sub>2</sub>-WS<sub>2</sub> heteronanoribbons. Growth is influenced by both substrate and CVD temperature, indicating the role of anisotropic precursor diffusion and substrate interaction. The 1D nature of the NRs was asserted by the observation of Coulomb blockade at low temperatures. Pronounced catalytic activity was observed at the edges of the NRs, indicating their promise for efficient catalysis. Chemical vapor deposition ; Materials science ; Transition metal ; Sapphire ; Zigzag ; Chemical physics ; Catalysis ; Nanotechnology ; Anisotropy ; Substrate (aquarium) ; Graphene ; Lattice (music) ; Chemical engineering ; Condensed matter physics ; Optoelectronics ; Chemistry ; Optics 2D Materials and Applications ; Graphene research and applications ; Advanced Photocatalysis Techniques
Authors: Sandra Mathew | Kalathiparambil Rajendra Pai Sunajadevi | Dephan Pinheiro
Source: Materials Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Transition metal oxide/chalcogenide-integrated MXene heterostructures: emerging materials for supercapacitors and water splitting The growing global demand for sustainable energy solutions necessitates advancements in energy storage and conversion technologies, aligning with the United Nations’ sustainable development goals. Supercapacitor ; Water splitting ; Sustainable energy ; Transition metal ; Energy transformation ; Energy transition ; Energy storage MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advanced Memory and Neural Computing
Authors: Bradley J. Rhodes | Sander Vandenhaute | Vaidotas Šimkus | James Gin | Jonathan Godwin | Tim Duignan | M. Neumann
Source: ArXiv.org (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

Orb-v3: atomistic simulation at scale We introduce Orb-v3, the next generation of the Orb family of universal interatomic potentials. Models in this family expand the performance-speed-memory Pareto frontier, offering near SoTA performance across a range of evaluations with a &gt;10x reduction in latency and &gt; 8x reduction in memory. Our experiments systematically traverse this frontier, charting the trade-off induced by roto-equivariance, conservatism and graph sparsity. Contrary to recent literature, we find that non-equivariant, non-conservative architectures can accurately model physical properties, including those which require higher-order derivatives of the potential energy surface. This model release is guided by the principle that the most valuable foundation models for atomic simulation will excel on all fronts: accuracy, latency and system size scalability. The reward for doing so is a new era of computational chemistry driven by high-throughput and mesoscale all-atom simulations. Traverse ; Mesoscale meteorology ; Interatomic potential ; Scale (ratio) ; Reduction (mathematics) ; Graph ; Pareto principle Machine Learning in Materials Science