The role of uncertainty quantification and propagation in accelerating the discovery of electrochemical functional mater
- PDF / 3,499,070 Bytes
- 10 Pages / 585 x 783 pts Page_size
- 22 Downloads / 165 Views
troduction The tremendous progress in computational approaches within materials science has led to the potential for accelerating material discovery for a wide range of applications, including in energy1 and biological sciences.2 High-throughput computational studies have aided in materials discovery in thermoelectrics,3 electrocatalysis,4 hydrogen storage,5 topological insulators,6 magnetic materials,7 and solar materials.8 While computational techniques such as density functional theory (DFT) simulations9–13 have shown tremendous success in rapidly eliminating bad material candidates and guiding design choice,14,15 a set of promising candidates often emerges that is indistinguishable without additional resources, including experimental testing and validation. In parallel, an emerging frontier is the development of uncertainty quantification and propagation that has the potential to dramatically reduce the number of false positive candidates
and can accelerate material discovery by reducing the number of candidates tested. This introduces an additional dimension to the predictions from computational methods by associating a quantitative metric for the robustness of every prediction or qualitatively, such as the likelihood of success of the candidate. Uncertainty quantification and propagation can serve to break the commonly held notion that “computational results are believed by no one, except the person who generated them.” Uncertainty quantification coupled with high fidelity simulations will play a major role in enhancing trust in computationally aided material discovery by more rapidly and robustly eliminating candidates that have a low potential for success. In this article, we demonstrate a systematic approach for uncertainty quantification and propagation utilizing DFT calculations through two case studies. The first case study is an example from electrocatalysis, where the goal was to more
Gregory Houchins, Carnegie Mellon University, USA; [email protected] Dilip Krishnamurthy, Carnegie Mellon University, USA; [email protected] Venkatasubramanian Viswanathan, Carnegie Mellon University, USA; [email protected] *Denotes equal contribution. doi:10.1557/mrs.2019.45
204
• VOLUME 44 • MARCH 2019 • www.mrs.org/bulletin © available 2019 Materials Downloaded MRS fromBULLETIN https://www.cambridge.org/core. East Carolina University, on 12 Mar 2019 at 01:56:45, subject to the Cambridge Core terms of use, at https://www.cambridge.org/core/terms. https://doi.org/10.1557/mrs.2019.45
Research Society
Uncertainty quantification to accelerate materials Discovery
robustly identify candidate materials that can be more active and selective for oxygen reduction reaction. The second case study is an example from Li-ion batteries, where the goal was to identify a novel cathode material based on Ni-Mn-Co (NMC) oxides. In both cases, we find that uncertainty quantification allows the identification of regimes where predictions are highly certain within the employed level of computational complexity, in addition to
Data Loading...
