Related papers: Machine learning potential as a guide for eutectic…
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between…
This study presents a machine learning approach to predict the Curie temperature in binary alloys, specifically focusing on the Fe-Pt, Fe-Ni, Fe-Pd, and Co-Pt compounds within a concentration range of 10 to 90 atomic percent. The optimal…
We used the superconductors in the SuperCon database to construct element vectors and then perform unsupervised learning of their critical temperatures (T$_c$). Only the chemical composition of superconductors was used in this procedure. No…
The enthalpy of mixing in the liquid phase is an important property for predicting phase formation in alloys. It can be estimated in a large compositional space from pair wise interactions between elements, for which machine learning has…
Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from…
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…
We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been…
Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space.…
Ultra-high temperature ceramics (UHTCs) represent a class of crystalline materials for extreme environments. They can withstand extremely high temperatures but are mechanically difficult to work with due to their inherent brittleness.…
Fracture toughness ($K_\mathrm{Ic}$) and fracture strength ($\sigma_\mathrm{f}$) are key criteria in the selection and design of reliable ceramics. However, their experimental characterization remains challenging -- especially for ceramic…
Single-phase body-centered cubic (BCC) refractory multi-principal element alloys (RMPEAs) offer potential for developing alloys with exceptional strength. However, the compositional design space is immense. Exhaustively mapping this space…
Ruthenium-based B2 intermetallics are promising for refractory superalloys but are limited by the trade-off between high thermodynamic stability and elastic precipitation strain. We present a physics-guided machine learning framework…
All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a…
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning…
The control and manipulation of quantum systems without excitation is challenging, due to the complexities in fully modeling such systems accurately and the difficulties in controlling these inherently fragile systems experimentally. For…
Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or…
Nanoalloys offer a unique opportunity to tailor chemical properties through changes in composition, shape, and size. However, this flexibility introduces complexity that challenges both experimental and conventional theoretical methods. In…
Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More…