A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards the understanding of interfacial systems. We describe the various algorithms and methods implemented in the package. Using several test cases, we demonstrate how the package enables high-throughput computational screening of advanced materials, directly contributing to the Materials Genome Initiative (MGI), which aims to accelerate the discovery, development, and deployment of new materials.

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The discovery of emergent phenomena in 2D materials has sparked substantial research efforts in the materials community. A significant experimental challenge for this field is exerting atomistic control over the structure and composition of the constituent 2D layers and understanding how the interactions between layers drive both structure and properties. While no segregation for single bilayers was observed, segregation of Pb to the surface of three bilayer thick PbSe-SnSe alloy layers was discovered within [(Pb Sn Se) ] (TiSe ) heterostructures using electron microscopy. This segregation is thermodynamically favored to occur when Pb Sn Se layers are interdigitated with TiSe monolayers. DFT calculations indicate that the observed segregation depends on what is adjacent to the Pb Sn Se layers. The interplay between interface- and volume-free energies controls both the structure and composition of the constituent layers, which can be tuned using layer thickness.

The need for high-capacity Li-ion battery cathodes has favored the increase of Ni content in commercial battery cells. However, at high states of charge (SOCs), Ni-rich materials undergo a phase transition and volume collapse with deleterious effects on battery performance. It is uncertain whether this drastic volume change is caused by the phase transition or not. To provide more insight into the volume-phase transition relationship in the high Ni cathode Li x NiO 2 , we performed density functional theory calculations, along with molecular dynamics simulations using machine learning potentials to calculate the temperature-and composition-dependent free energy differences between the suspected phases at high SOCs (x < 0.25). We find that the calculated free energy difference between the suspected phases containing different oxygen stacking sequences is small at room temperature. Furthermore, we find that the collapse of the layered LiNiO 2 c-lattice parameter at high SOCs is mainly due to the electronic depletion of the oxygen sublattice and the lack of screening from positive Li ions. The interactions between adjacent oxygen ions across an empty Li layer (NiO 2 ) are largely controlled by van der Waals interactions and are in fact similar regardless of the oxygen stacking, which explains the negligible free energy differences between O1 and O3 stacking in NiO 2 .

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The design of next-generation alloys through the Integrated Computational Materials Engineering (ICME) approach relies on multi-scale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as Density Functional Theory (DFT) and Molecular Dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies have emerged that mitigate this gap. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as Phase Field Method (PFM) and Calculation of Phase Diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist and Bayesian methodologies. Challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.

The discovery of emergent phenomena in 2D materials has sparked substantial researche fforts in the materials community.Asignificant experimental challenge for this field is exerting atomistic control over the structure and composition of the constituent 2D layers and understanding howt he interactions between layers drive both structure and properties.W hile no segregation for single bilayers was observed, segregation of Pb to the surface of three bilayer thickP bSe-SnSe alloyl ayers was discovered within [(Pb x Sn 1Àx Se) 1+d ] n (TiSe 2 ) 1 heterostructures using electron microscopy. This segregation is thermodynamically favored to occur when Pb x Sn 1Àx Se layers are interdigitated with TiSe 2 monolayers.D FT calculations indicate that the observed segregation depends on what is adjacent to the Pb x Sn 1Àx Se layers.T he interplay between interface-and volume-free energies controls both the structure and composition of the constituent layers,w hich can be tuned using layer thickness.

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