Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements S e.g. the length of body-ordered descriptors, such as the Smooth Overlap of Atomic Positions (SOAP) power spectrum (3-body) and the Atomic Cluster Expansion (ACE) (multiple body-orders), scales as (N S) ν where ν + 1 is the body-order and N is the number of radial basis functions used in the density expansion. We introduce two distinct approaches which can be used to overcome this scaling for the SOAP power spectrum. Firstly, we show that the power spectrum is amenable to lossless compression with respect to both S and N , so that the descriptor length can be reduced from O(N 2 S 2 ) to O (N S). Secondly, we introduce a generalized SOAP kernel, where compression is achieved through the use of the total, element agnostic density, in combination with radial projection. The ideas used in the generalized kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the Atom Centered Symmetry Functions (ACSF). Finally, both compression approaches are shown to offer comparable performance to the original descriptor across a variety of numerical tests.

Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements S e.g. the length of body-ordered descriptors, such as the SOAP power spectrum (3-body) and the (ACE) (multiple body-orders), scales as (NS)ν where ν + 1 is the body-order and N is the number of radial basis functions used in the density expansion. We introduce two distinct approaches which can be used to overcome this scaling for the SOAP power spectrum. Firstly, we show that the power spectrum is amenable to lossless compression with respect to both S and N, so that the descriptor length can be reduced from $${{{\mathcal{O}}}}({N}^{2}{S}^{2})$$ O ( N 2 S 2 ) to $${{{\mathcal{O}}}}\left(NS\right)$$ O N S . Secondly, we introduce a generalised SOAP kernel, where compression is achieved through the use of the total, element agnostic density, in combination with radial projection. The ideas used in the generalised kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the (ACSF).

Metal-organic frameworks (MOFs) have emerged as highly versatile materials with applications in gas storage and separation, solar light energy harvesting and photocatalysis. The design of new MOFs, however, has been hampered by the lack of computational methods for ab initio MOF structure prediction, which could be used to inspire and direct experimental synthesis. Here, we report the first ab initio method for the prediction of MOF structures and test it against a diverse set of known MOFs that were chosen for their differences in topology, metal coordination geometry, and ligand binding sites. In all cases, our calculations produced structures that match experiment using only the target composition and ligand molecular structure, proving the versatility of our procedure. The herein presented methodology utilizes the point group symmetry of ligands to enable, for the first time, prediction of MOF structures from first principles, without having to resort to empirical guidelines based on rigid connectivity of nodes and linkers, or to previously determined crystal structures and topologies of known MOFs. This advance provides the first tool to change MOF design from an empirically based process that is based on chemistʼs intuition rooted in literature-or database-established knowledge of node-and-linker connectivity to a more general and theory-driven materials development. This ab initio MOF structure prediction approach, which is here validated on a range of known MOF classes, provides a unique opportunity to explore the phase landscape of MOFs computationally and enables MOF research and development even in case of limited access to laboratory resources, as for example in case of a global pandemic.

Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability must be well-understood. Computational materials modelling can expedite this process by predicting novel materials, both in stand-alone theoretical calculations and in tandem with experiments. In this review, we describe a materials discovery framework based on density functional theory (DFT) to predict the properties of electrode and solid-electrolyte materials and validate these predictions experimentally. First, we discuss crystal structure prediction using the Ab initio random structure searching (AIRSS) method. Next, we describe how DFT results allow us to predict which phases form during electrode cycling, as well as the electrode voltage profile and maximum theoretical capacity. We go on to explain how DFT can be used to simulate experimentally measurable properties such as nuclear magnetic resonance (NMR) spectra and ionic conductivities. We illustrate the described workflow with multiple experimentally validated examples: materials for lithium-ion and sodium-ion anodes and lithium-ion solid electrolytes. These examples highlight the power of combining computation with experiment to advance battery materials research.

First-principles crystal structure prediction (CSP) is the most powerful approach for materials discovery, enabling the prediction and evaluation of properties of new solid phases based only on a diagram of their underlying components. Here, we present the first CSP-based discovery of metal–organic frameworks (MOFs), offering a broader alternative to conventional techniques, which rely on geometry, intuition, and experimental screening. Phase landscapes were calculated for three systems involving flexible Cu­(II) nodes, which could adopt a potentially limitless number of network topologies and are not amenable to conventional MOF design. The CSP procedure was validated experimentally through the synthesis of materials whose structures perfectly matched those found among the lowest-energy calculated structures and whose relevant properties, such as combustion energies, could immediately be evaluated from CSP-derived structures.

W e have recently become aware of some errors in the reporting of the Li−Sn structures in our paper: 1 (1) The space groups (SG) of certain Li−Sn structures are reported incorrectly in the following cases:(i) The SG of the Li 2 Sn 3 structure is given as P1̅ in six instances: in the Abstract, in the "Low Li-Content Structures" section, in the Discussion, in Table 1, in the image of Figure 2b, and in the caption of Figure 6. It should read P4/mmm. The structure's ball-and-stick depiction is correct in Figure 2b.(ii) The SG of the Li 5 Sn 3 structure is given as P4̅ 3m in the "Structures between 1 ≤ x ≤ 4.4 in Li x Sn" section and in the caption of Figure 2. It should read Im3̅ m.(iii) The SG of the Li 8 Sn 3 structure is given as C2/m in the caption of Figure 4. It should read R3̅ m.(iv) The SG of the Li 5 Sn 1 structure is given as Pmma in Table 1. It should read P6/mmm.(v) The SG of the Li 7 Sn 1 structure is given as Fmmm in the Abstract; it should read C2.(vi) In the "Lithium Antimonides" Results section, Li 1 Sb 1 is described as being "similar to Li 1 Sn 1 −P4/mmm"; this should read "similar to Li 1 Sn 1 −Pmm2".(2) The CIFs provided in the Supporting Information for the

The Smooth Overlap of Atomic Positions (SOAP) descriptor represents an increasingly common approach to encode local atomic environments in a form readily digestible to machine learning algorithms. The SOAP descriptor...

Metal-organic frameworks (MOFs) have emerged as highly versatile materials with applications in gas storage and separation, solar light energy harvesting and photocatalysis. The design of new MOFs, however, has been hampered by the lack of computational methods for <i>ab initio</i> crystal structure prediction, which could be used to direct experimental synthesis. Here we report the first <i>ab intio</i> method for MOF structure prediction, and test it against a diverse set of MOFs, with differences in topology, metal coordination geometry and ligand binding sites. In all cases our calculations produced structures which match experiment, proving the versatility of our procedure for MOF structure prediction. With our new methodology for <i>ab initio</i> structure prediction, current approaches to MOF design are set to change towards a more sustainable theory-driven materials development.<br>