Related papers: Machine-Learning Enabled Search for The Next-Gener…
Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reliance on renewable energy sources such as wind and solar,…
In this work, we examine metal electrode-ionomer electrolyte systems at high voltage / negative surface charge and at high pH to assess factors that influence hydrogen production efficiency. We simulate the hydrogen evolution electrode…
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable…
MXenes with versatile chemistry and superior electrical conductivity are prevalent candidate materials for energy storage and catalysts. Inspired by recent experiments of hybridizing MXenes with carbon materials, here we theoretically…
Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds. Catalyst discovery is fundamental yet challenging in…
A uniformly coated MoS2/Carbon-nanocomposite with three-dimensional hierarchical architecture based on carbonized bacterial cellulose (CBC) nanofibers is synthesized by a facile one-step hydrothermal method followed by thermal annealing at…
We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based…
Molybdenum disulfide, $MoS_2$, is a next-generation semiconductor and is frequently integrated into emergent optoelectronic technologies based on two-dimensional materials. Here, we present a method that provides direct optical feedback on…
With the increase in the complexity of the materials used in various sophisticated electronic devices, recycling of E-waste is becoming challenging. In the present study, we have converted thermoelectric (TE) waste into functional HER…
Magnesium (Mg) alloys have shown great prospects as both structural and biomedical materials, while poor corrosion resistance limits their further application. In this work, to avoid the time-consuming and laborious experiment trial, a…
Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning…
The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…
The hydro-mechanical behavior of clay-sulfate rocks, especially their swelling properties, poses significant challenges in geotechnical engineering. This study presents a hybrid constrained machine learning (ML) model developed using the…
The hydrogen oxidation reaction (HOR) and hydrogen evolution reaction (HER) play an important role in hydrogen based energy conversion. Recently, the frustrating performance in alkaline media raised debates on the relevant mechanism,…
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive…
Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…
Two-dimensional (2D) heterostructuring is a versatile methodology for designing nanoarchitecture catalytic systems that allow for reconstruction and modulation of interfaces and electronic structures. However, catalysts with such structures…
This short paper presents the potential of using machine learning to predict materials behaviour in the context of hydrogen interaction with steel. Effort has been made to understand the quality, and amount of data needed to get improved…
Heterostructures (HSs) formed by the transition-metal dichalcogenides (TMDCs) materials have shown great promise in next-generation optoelectronic and photonic applications. An artificially twisted HS, allows us to manipulate the optical,…
We present a response-augmented machine learning (ML) approach to the energetics of electrified metal surfaces. We leverage local descriptors to learn the work function as the first-order energy change to introduced bias charges and…