Related papers: Machine learning aided parameter analysis in Perov…
Accurately predicting the physical and chemical properties of materials remains one of the most challenging tasks in material design, and one effective strategy is to construct a reliable data set and use it for training a machine learning…
Halide perovskites have emerged as one of the most interesting materials for optoelectronic applications due to their favorable properties, such as defect-tolerance and long charge carrier lifetimes, which are attributed to their dynamic…
In this paper, we present a machine learning based architecture for microwave characterization of inkjet printed components on flexible substrates. Our proposed architecture uses several machine learning algorithms and automatically selects…
We report the machine learning (ML)-based approach allowing thermoelectric generator (TEG) efficiency evaluation directly from 5 parameters: 2 physical properties - carriers density and energy gap, and 3 engineering parameters - external…
Metal-organic frameworks (MOFs) have emerged as promising materials for various applications due to their unique structural properties and versatile functionalities. This study presents a comprehensive investigation of machine learning…
The mechanical properties and long-term structural reliability of crystalline materials are strongly influenced by microstructural features such as grain size, morphology, and crystallographic texture. These characteristics not only…
To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based…
In this work, we present a ready-to-use symmetry invariant expansion form of the eight-band k.p Hamiltonian for inorganic and organic metal halide perovskites (CsPbX$_3$ and MAPbX$_3$ with $X = \{$Cl, Br, I$\}$). We use the k.p model to…
In recent times, the use of machine learning in materials design and discovery has aided to accelerate the discovery of innovative materials with extraordinary properties, which otherwise would have been driven by a laborious and…
Novel halide perovskites with improved stability and optoelectronic properties can be designed via composition engineering at cation and/or anion sites. Data-driven methods, especially high-throughput first principles computations and…
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either…
The physical and chemical characteristics of cathodes used in batteries are derived from the lithium-ion phosphate cathodes crystalline arrangement, which is pivotal to the overall battery performance. Therefore, the correct prediction of…
Screening combinatorial space for novel materials - such as perovskite-like ones for photovoltaics - has resulted in a high amount of simulated high-troughput data and analysis thereof. This study proposes a comprehensive comparison of…
Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use…
Defect tolerance is a critical enabling factor for efficient lead-halide perovskite materials, but the current understanding is primarily on band-edge (cold) carriers, with significant debate over whether hot carriers (HCs) can also exhibit…
Supervised machine learning models are trained with various molecular descriptors to predict infrared emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstrate that a feature importance analysis based on the random…
In this work, we employ a machine-learning-assisted high-throughput density functional theory framework to systematically investigate the stability, electronic structure, and magnetic ground states of 234 M$_4$X$_3$T$_x$ MXenes. The machine…
We present a physics based multiscale materials-to-systems model for polycrystalline $PbSe$ photodetectors that connects fundamental material properties to circuit level performance metrics. From experimentally observed film structures and…
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…
Halide perovskites have remarkable properties for relatively crudely processed semiconductors, including large optical absorption coefficients and long charge carrier lifetimes. Thanks to such properties, these materials are now competing…