Related papers: Interpretable machine learning-guided design of Fe…
The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine…
Microstructural heterogeneity affects the macro-scale behavior of materials. Conversely, load distribution at the macro-scale changes the microstructural response. These up-scaling and down-scaling relations are often modeled using…
We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the…
Predicting the Curie temperature ($T_\mathrm{C}$) of magnetic materials is crucial for advancing applications in data storage, spintronics, and sensors. We present a machine learning (ML) framework to predict $T_{\mathrm{C}}$ using a…
Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for…
First-principles alloy theory, formulated within the exact muffin-tin orbitals method in combination with the coherent-potential approximation, is used to study the mechanical properties of ferromagnetic body-centered cubic (bcc)…
The development of quantitative models for radiation damage effects in iron, iron alloys and steels, particularly for the high temperature properties of the alloys, requires understanding of magnetic interactions, which control the phase…
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…
A Magnetic Cluster Expansion (MCE) model for ternary face-centered cubic Fe-Ni-Cr alloys has been developed using DFT data spanning binary and ternary alloy configurations. Using this MCE model Hamiltonian, we perform Monte Carlo…
With the intention to reveal the effect of the substitution, Ti-doped InSb alloy, we accomplished a first-principles prediction within the FPLAPW+lo method. We used GGA-PBEsol scheme attached with the improved TB-mBJ approach to predict…
Rare earth (RE)-free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard…
New hard magnetic materials with zero or low rare earth content are in demand due to the high prices of the rare earth metals. Among the candidates for such materials, we consider MnB, FeB and their alloys, because previous experiments…
Efficiently designing lightweight alloys with combined high corrosion resistance and mechanical properties remains an enduring topic in materials engineering. To this end, machine learning (ML) coupled ab-initio calculations is proposed…
First-principles calculations are used to gauge different levels of approximation to calculate the magnetocrystalline anisotropy energies (MAE) of five $L1_0$ FeMe alloys (Me=Co, Cu, Pd, Pt, Au). We find that a second-order perturbation…
Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional…
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the…
The elementary excitations in metallic glasses (MGs), i.e., $\beta$ processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those…
The development of supercapacitors is impeded by the unclear relationships between nanoporous electrode structures and electrochemical performance, primarily due to challenges in decoupling the complex interdependencies of various…
We employ a combination of machine learning and first-principles calculations to predict magnetic properties of rare-earth lean magnets. For this purpose, based on training set constructed out of experimental data, the machine is trained to…
We describe an integrated modelling approach to accelerate the search for novel, single-phase, multicomponent materials with high magnetocrystalline anisotropy (MCA). For a given system we predict the nature of atomic ordering, its…