Defect modeling and control in structurally and compositionally complex materials
Conventional computational approaches for modeling defects face difficulties when applied
to complex materials, mainly due to the vast configurational space of defects. In this …
to complex materials, mainly due to the vast configurational space of defects. In this …
Robust data-driven approach for predicting the configurational energy of high entropy alloys
High entropy alloys (HEAs) are promising next-generation materials due to their various
excellent properties. To understand these properties, it's necessary to characterize the …
excellent properties. To understand these properties, it's necessary to characterize the …
[HTML][HTML] Analytical models of short-range order in FCC and BCC alloys
A statistical-mechanics analysis is used to create analytic estimates for the short-range order
(SRO) parameters in FCC and BCC solid solution alloys as a function of composition and …
(SRO) parameters in FCC and BCC solid solution alloys as a function of composition and …
Progress in computational and machine‐learning methods for heterogeneous small‐molecule activation
The chemical conversion of small molecules such as H2, H2O, O2, N2, CO2, and CH4 to
energy and chemicals is critical for a sustainable energy future. However, the high chemical …
energy and chemicals is critical for a sustainable energy future. However, the high chemical …
Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide
Artificial neural network (ANN) potentials enable the efficient large-scale atomistic modeling
of complex materials with near first-principles accuracy. For molecular dynamics simulations …
of complex materials with near first-principles accuracy. For molecular dynamics simulations …
Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications
The high entropy alloys have become the most intensely researched materials in recent
times. They offer the flexibility to choose a large array of metallic elements in the periodic …
times. They offer the flexibility to choose a large array of metallic elements in the periodic …
A hybrid prediction frame for HEAs based on empirical knowledge and machine learning
S Hou, M Sun, M Bai, D Lin, Y Li, W Liu - Acta Materialia, 2022 - Elsevier
Phase formation plays key role in the properties of high-entropy alloys (HEAs). If the phases
of HEAs can be accurately predicted, the number of experiments can be greatly reduced …
of HEAs can be accurately predicted, the number of experiments can be greatly reduced …
Effects of vanadium concentration on mechanical properties of VxNbMoTa refractory high-entropy alloys
M Wang, ZL Ma, ZQ Xu, XW Cheng - Materials Science and Engineering: A, 2021 - Elsevier
Mechanical properties and strengthening, deformation, and/or fracture mechanisms of
refractory high-entropy alloys V x NbMoTa are investigated. At room temperature, both the …
refractory high-entropy alloys V x NbMoTa are investigated. At room temperature, both the …
Accurate machine learning force fields via experimental and simulation data fusion
S Röcken, J Zavadlav - npj Computational Materials, 2024 - nature.com
Abstract Machine Learning (ML)-based force fields are attracting ever-increasing interest
due to their capacity to span spatiotemporal scales of classical interatomic potentials at …
due to their capacity to span spatiotemporal scales of classical interatomic potentials at …
Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials
X Yuan, Y Zhou, Q Peng, Y Yang, Y Li… - npj Computational …, 2023 - nature.com
Chemical-disordered materials have a wide range of applications whereas the
determination of their structures or configurations is one of the most important and …
determination of their structures or configurations is one of the most important and …