Machine learning elastic constants of multi-component alloys
V Revi, S Kasodariya, A Talapatra, G Pilania… - Computational Materials …, 2021 - Elsevier
The present manuscript explores application of machine learning methods for determining
elastic constants and other derived mechanical properties of multi-component alloys. A …
elastic constants and other derived mechanical properties of multi-component alloys. A …
Inverse design of materials that exhibit the magnetocaloric effect by text-mining of the scientific literature and generative deep learning
Magnetic materials play an important role in a wide variety of everyday applications, and
they are critical components in many devices used for energy conversion. However, there …
they are critical components in many devices used for energy conversion. However, there …
Insights into cation ordering of double perovskite oxides from machine learning and causal relations
A Ghosh, G Palanichamy, DP Trujillo… - Chemistry of …, 2022 - ACS Publications
This work investigates origins of cation ordering in double perovskites using first-principles
theory computations combined with machine learning (ML) and causal relations. We have …
theory computations combined with machine learning (ML) and causal relations. We have …
Designing workflows for materials characterization
Experimental science is enabled by the combination of synthesis, imaging, and functional
characterization organized into evolving discovery loop. Synthesis of new material is …
characterization organized into evolving discovery loop. Synthesis of new material is …
Plutonium aging: From fundamental mechanisms to material properties
S Su, L Shen, Y Zhao, A Yin, B Su, T Fa - Materials Science and …, 2024 - Elsevier
Plutonium and its derivatives have been demonstrated with a wide range of research and
applications in nuclear energy, nuclear devices, radioactive waste storage, basic science …
applications in nuclear energy, nuclear devices, radioactive waste storage, basic science …
Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides
The computational design of suitable multiferroic double perovskite oxides requires finding
materials that exhibit sizable polarization, magnetization, and coupling between them …
materials that exhibit sizable polarization, magnetization, and coupling between them …
Bridging microscopy with molecular dynamics and quantum simulations: An atomAI based pipeline
Recent advances in (scanning) transmission electron microscopy have enabled a routine
generation of large volumes of high-veracity structural data on 2D and 3D materials …
generation of large volumes of high-veracity structural data on 2D and 3D materials …
Towards physics-informed explainable machine learning and causal models for materials research
A Ghosh - Computational Materials Science, 2024 - Elsevier
From emergent material descriptions to estimation of properties stemming from structures to
optimization of process parameters for achieving best performance–all key facets of …
optimization of process parameters for achieving best performance–all key facets of …
Giant magnetocaloric effect driven by first-order magnetostructural transition in cosubstituted Ni-Mn-Sb Heusler compounds: Predictions from ab initio and Monte …
Using density functional theory and a thermodynamic model [V. Sokolovskiy Phys. Rev. B
86, 134418 (2012) PRBMDO 1098-0121 10.1103/PhysRevB. 86.134418] in this paper we …
86, 134418 (2012) PRBMDO 1098-0121 10.1103/PhysRevB. 86.134418] in this paper we …
[HTML][HTML] Identification of novel organic polar materials: A machine learning study with importance sampling
A Ghosh, DP Trujillo, S Hazarika, E Schiesser… - APL Machine …, 2023 - pubs.aip.org
Recent advances in the synthesis of polar molecular materials have produced practical
alternatives to ferroelectric ceramics, opening up exciting new avenues for their …
alternatives to ferroelectric ceramics, opening up exciting new avenues for their …