Agents for sequential learning using multiple-fidelity data.

Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values. The fidelities of data come from the results of DFT calculations as low fidelity and experimental results as high fidelity. We demonstrate performance gains of agents which incorporate multi-fidelity data in two contexts: either using a large body of low fidelity data as a prior knowledge base or acquiring low fidelity data in-tandem with experimental data. This advance provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. This may also serve as a reference point for those who are interested in practical strategies that can be used when multiple data sources are available for active or sequential learning campaigns.

OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

Rational Solid-State Synthesis Routes for Inorganic Materials.

The rational solid-state synthesis of inorganic compounds is formulated as catalytic nucleation on crystalline reactants, where contributions of reaction and interfacial energies to the nucleation barriers are approximated from high-throughput thermochemical data and structural and interfacial features of crystals, respectively. Favorable synthesis reactions are then identified by a Pareto analysis of relative nucleation barriers and phase selectivities of reactions leading to the target. We demonstrate the application of this approach in reaction planning for the solid-state synthesis of a range of compounds, including the widely studied oxides LiCoO2, BaTiO3, and YBa2Cu3O7, as well as other metal oxide, oxyfluoride, phosphate, and nitride targets. Pathways for enabling the retrosynthesis of inorganics are also discussed.

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure-property relationships for materials design.

Autonomous intelligent agents for accelerated materials discovery.

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

Network analysis of synthesizable materials discovery.

Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.

Discovery of a Ni-Ga catalyst for carbon dioxide reduction to methanol.

The use of methanol as a fuel and chemical feedstock could become very important in the development of a more sustainable society if methanol could be efficiently obtained from the direct reduction of CO2 using solar-generated hydrogen. If hydrogen production is to be decentralized, small-scale CO2 reduction devices are required that operate at low pressures. Here, we report the discovery of a Ni-Ga catalyst that reduces CO2 to methanol at ambient pressure. The catalyst was identified through a descriptor-based analysis of the process and the use of computational methods to identify Ni-Ga intermetallic compounds as stable candidates with good activity. We synthesized and tested a series of catalysts and found that Ni5Ga3 is particularly active and selective. Comparison with conventional Cu/ZnO/Al2O3 catalysts revealed the same or better methanol synthesis activity, as well as considerably lower production of CO. We suggest that this is a first step towards the development of small-scale low-pressure devices for CO2 reduction to methanol.

First principles investigation of zinc-anode dissolution in zinc-air batteries.

With surging interest in high energy density batteries, much attention has recently been devoted to metal-air batteries. The zinc-air battery has been known for more than a hundred years and is commercially available as a primary battery, but recharging has remained elusive, in part because the fundamental mechanisms still remain to be fully understood. Here, we present a density functional theory investigation of the zinc dissolution (oxidation) on the anode side in the zinc-air battery. Two models are envisaged, the most stable (0001) surface and a kink surface. The kink model proves to be more accurate as it brings about some important features of bulk dissolution and yields results in good agreement with experiments. From the adsorption energies of hydroxyl species and experimental values, we construct a free energy diagram and confirm that there is a small overpotential associated with the reaction. The applied methodology provides new insight into computational modelling and design of secondary metal-air batteries.

Indirect, reversible high-density hydrogen storage in compact metal ammine salts.

The indirect hydrogen storage capabilities of Mg(NH 3) 6Cl 2, Ca(NH 3) 8Cl 2, Mn(NH 3) 6Cl 2, and Ni(NH 3) 6Cl 2 are investigated. All four metal ammine chlorides can be compacted to solid tablets with densities of at least 95% of the crystal density. This gives very high indirect hydrogen densities both gravimetrically and volumetrically. Upon heating, NH 3 is released from the salts, and by employing an appropriate catalyst, H 2 can be released corresponding to up to 9.78 wt % H and 0.116 kg H/L for the Ca(NH 3) 8Cl 2 salt. The NH 3 release from all four salts is investigated using temperature-programmed desorption employing different heating rates. The desorption is found mainly to be limited by heat transfer, indicating that the desorption kinetics are extremely fast for all steps. During desorption from solid tablets of Mg(NH 3) 6Cl 2, Mn(NH 3) 6Cl 2, and Ni(NH 3) 6Cl 2, nanoporous structures develop, which facilitates desorption from the interior of large, compact tablets. Density functional theory calculations reproduce trends in desorption enthalpies for the systems studied, and a mechanism in which individual chains of the ammines are released from the surface of the crystal is proposed to explain the fast absorption/desorption processes.

Generation of nanopores during desorption of NH3 from Mg(NH3)6Cl2.

It is shown that nanopores are formed during desorption of NH3 from Mg(NH3)6Cl2, which has been proposed as a hydrogen storage material. The system of nanopores facilitates the transport of desorbed ammonia away from the interior of large volumes of compacted storage material. DFT calculations show that there exists a continuous path from the initial Mg(NH3)6Cl2 material to MgCl2 that does not involve large-scale material transport. Accordingly, ammonia desorption from this system is facile.