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Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. 

Low-cost, non-empirical corrections to semi-local density functional theory are essential for accurately modeling transition-metal chemistry. Here, we demonstrate the judiciously modified density functional theory (jmDFT) approach with non-empirical U and J parameters obtained directly from frontier orbital energetics on a series of transition-metal complexes. We curate a set of nine representative Ti(III) and V(IV) d 1 transition-metal complexes and evaluate their flat-plane errors along the fractional spin and charge lines. We demonstrate that while jmDFT improves upon both DFT+U and semi-local DFT with the standard atomic orbital projectors (AOPs), it does so inefficiently. We rationalize these inefficiencies by quantifying hybridization in the relevant frontier orbitals. To overcome these limitations, we introduce a procedure for computing a molecular orbital projector (MOP) basis for use with jmDFT. We demonstrate this single set of d 1 MOPs to be suitable for nearly eliminating all energetic delocalization and static correlation errors. In all cases, MOP jmDFT outperforms AOP jmDFT, and it eliminates most flat-plane errors at non-empirical values. Unlike DFT+U or hybrid functionals, jmDFT nearly eliminates energetic delocalization and static correlation errors within a non-empirical framework. 

Doped graphitic single-atom catalysts (SACs) with isolated iron sites have similarities to natural enzymes and molecular biomimetics that can convert methane to methanol via a radical rebound mechanism with high-valent Fe( iv )O intermediates. To understand the relationship of SACs to these homogeneous analogues, we use range-separated hybrid density functional theory (DFT) to compare the energetics and structure of the direct metal-coordinating environment in the presence of 2p ( i.e. , N or O) and 3p ( i.e. , P or S) dopants and with increasing finite graphene model flake size to mimic differences in local rigidity. While metal–ligand bond lengths in SACs are significantly shorter than those in transition-metal complexes, they remain longer than SAC mimic macrocyclic complexes. In SACs or the macrocyclic complexes, this compressed metal–ligand environment induces metal distortion out of the plane, especially when reactive species are bound to iron. As a result of this modified metal-coordination environment, we observe SACs to simultaneously favor the formation of the metal–oxo while also allowing for methanol release. This reactivity is different from what has been observed for large sets of square planar model homogeneous catalysts. Overall, our calculations recommend broader consideration of dopants ( e.g. , P or S) and processing conditions that allow for local distortion around the metal site in graphitic SACs. 

We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.