Preface

George H. Bootha and Ali Alavib
aKing’s College London, London, UK
bMax Planck Institute for Solid State Research, Stuttgart, Germany

If one aims to numerically predict chemical phenomena without any empiricism or experimental input, then starting from the electronic structure and the quantum many-body problem it entails is inescapable. We are lucky in one sense that the equations governing this problem are well known, and from it the properties of molecules, materials and their reactions are in principle obtainable. However, these equations are unfortunately insoluble in general, and their approximate, yet accurate and scalable numerical solution has long been sought. Progress in this field holds the promise of widespread impact in the predictive computational determination of molecular properties, unique insight into reaction pathways and intermediates, the inverse design of materials, and much more. Innovative and emerging methods for the numerical solution to these equations have long outstripped the ability of brute force approaches, with a diversification and combination of approaches key to developments in the field. These have allowed numerical experiments to model ever larger systems, with increasingly reliable accuracies and agreement with their analogous physical experiments.

The motivation for this Faraday Discussion followed from Faraday Discussion 224 (2020) on ‘New horizons in Density Functional Theory’. Density functional theory is undoubtedly the most widespread approach to this electronic problem, but it is also clear that many of the fundamental approximations and limitations of the approach have remained the same for a long time. There is an alternative, and there was interest in exploring it further in the context of this Faraday Discussion—correlated electronic structure which relies on other quantum variables; Green’s functions, wave functions and others. These offer a more rigorous and systematic inclusion of the correlated electron physics. However, it is important to reflect on where the field is, and the grand challenges which remain to be tackled, in order to extend the applicability of the field to wider communities, and the scope of the problems which can be currently addressed. Where do these ‘post-mean-field’ theories sit in the hierarchy of viable approaches, and what are the limitations of existing methods? What are the promising new ideas and research directions which will enable progress in the field in the upcoming years? This is particularly relevant with the disruptive technologies of machine learning and quantum computing entering the field, with electronic structure in particular often touted as an application of near-term quantum computers. We hope that this collection of papers serves as a snapshot into the state-of-the-art in a diverse selection of electronic structure methods and their applicability, presenting in particular novel approaches for strong correlation, excited state calculations and bridging the length- and time-scales for their ever-increasing scope.

Acknowledgements

No part of this discussion would have been possible without the fantastic support of the Royal Society of Chemistry staff. In particular, Stuart Govan, Claire Springett, Sarah Latham and Vikki Pritchard who have done such a lot of work in putting this event on, and Irene Sanchez Molina, Rini Prakash and Charlotte Pugsley on the publishing side. We also owe a debt to the scientific committee, Peter Knowles, Dominika Zgid, Gustavo Scuseria and Katarzyna Pernal for (amongst other tasks) their reviewing and chairing of the papers. The speakers all deserve particular thanks, in particular the opening and closing lectures delivered by Garnet Chan and Francesco Evangelista respectively. We also thank the poster prize committee, Brenda Rubenstein, Pierre-Francois Loos and Francesco Evangelista, as well as all those who prepared the excellent selection of posters. Finally, and most importantly, we sincerely thank all speakers and participants for making this such an exciting and stimulating meeting to support this field. Thank you.

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