Prasanna V. Balachandran

Accelerated search for materials with targeted properties by adaptive design

Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

Effect of interfacial octahedral behavior in ultrathin manganite films.

We investigate structural coupling of the MnO6 octahedra across a film/substrate interface and the resultant changes of the physical properties of ultrathin La2/3Sr1/3MnO3 (LSMO) films. In order to isolate the effect of interfacial MnO6 octahedral behavior from that of epitaxial strain, LSMO films are grown on substrates with different symmetry and similar lattice parameters. Ultrathin LSMO films show an increased magnetization and electrical conductivity on cubic (LaAlO3)0.3(Sr2AlTaO6)0.7 (LSAT) compared to those grown on orthorhombic NdGaO3 (NGO) substrates, an effect that subsides as the thickness of the films is increased. This study demonstrates that interfacial structural coupling can play a critical role in the functional properties of oxide heterostructures.

Adaptive Strategies for Materials Design using Uncertainties

We compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.

Materials prediction via classification learning

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturally uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. Our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle.

Thickness-dependent crossover from charge- to strain-mediated magnetoelectric coupling in ferromagnetic/piezoelectric oxide heterostructures.

Magnetoelectric oxide heterostructures are proposed active layers for spintronic memory and logic devices, where information is conveyed through spin transport in the solid state. Incomplete theories of the coupling between local strain, charge, and magnetic order have limited their deployment into new information and communication technologies. In this study, we report direct, local measurements of strain- and charge-mediated magnetization changes in the La0.7Sr0.3MnO3/PbZr0.2Ti0.8O3 system using spatially resolved characterization techniques in both real and reciprocal space. Polarized neutron reflectometry reveals a graded magnetization that results from both local structural distortions and interfacial screening of bound surface charge from the adjacent ferroelectric. Density functional theory calculations support the experimental observation that strain locally suppresses the magnetization through a change in the Mn-eg orbital polarization. We suggest that this local coupling and magnetization suppression may be tuned by controlling the manganite and ferroelectric layer thicknesses, with direct implications for device applications.

Interplay of octahedral rotations and breathing distortions in charge-ordering perovskite oxides

We investigate the structure--property relationships in

Massive band gap variation in layered oxides through cation ordering

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Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.

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Finding new perovskite halides via machine learning

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Classification of ABO3 perovskite solids: a machine learning study.

perovskites exhibiting octahedral rotations and cooperative octahedral breathing distortions (CBD) using group theoretical methods. Rotations of octahedra are ubiquitous in the perovskite family, while the appearance of breathing distortions -- oxygen displacement patterns that lead to approximately uniform dilation and contraction of the