Modern ab initio methods have rapidly increased our understanding of solid state materials properties, chemical reactions, and the quantum interactions between atoms. However, poor scaling often renders direct ab initio calculations intractable for large or complex systems. There are two obvious avenues through which to remedy this problem: (i) develop new, less expensive methods to calculate system properties, or (ii) make existing methods faster. This paper describes an open source framework designed to pursue both of these avenues. PROPhet (short for PROPerty Prophet) utilizes machine learning techniques to find complex, non-linear mappings between sets of material or system properties. The result is a single code capable of learning analytical potentials, non-linear density functionals, and other structure-property or property-property relationships. These capabilities enable highly accurate mesoscopic simulations, facilitate computation of expensive properties, and enable the development of predictive models for systematic materials design and optimization. This work explores the coupling of machine learning to ab initio methods through means both familiar (e.g., the creation of various potentials and energy functionals) and less familiar (e.g., the creation of density functionals for arbitrary properties), serving both to demonstrate PROPhet’s ability to create exciting post-processing analysis tools and to open the door to improving ab initio methods themselves with these powerful machine learning techniques.

Density functional theory (DFT) has become the standard for studying periodic systems for a wide range of materials properties. Originally proposed in the 1960s [1,2], this formalism states that material properties can be described as a pure functional of the charge density, eliminating the need to directly treat all the electrons in the system. This finding drastically reduced the computational costs associated with simulating realistic systems to study their unique properties.While DFT is promising for a variety of reasons, there are several shortcomings of the theory. One of the most significant shortcomings of DFT is that large systems are difficult to study. This is because some of the terms within DFT formalism are not known as a pure functional of the charge density. One example of this is the kinetic energy term, requiring explicit treatment of the Kohn-sham orbitals, drastically increasing the computational cost of DFT methods. Work in orbital-free DFT [3] has been proposed to decrease this reliance and is a promising way to achieve simulations of tens of thousands of atoms within DFT.Arising from the lack of an exact functional, a second area of concern is in predicting certain materials properties accurately. While DFT is useful for predicting formation energies, potential energy surfaces, total energies, and ground state atomic structures, it performs poorly for predicting properties that are inherently dependent on excited states. Commonly used DFT exchange-correlation approximations, such as the local density approximation (LDA) [2] and generalized gradient approximation (GGA) [4], are notoriously inaccurate at predicting key properties such as the band gap. This is generally thought to be caused by the inaccurate treatment of the self-interaction term of the electrons [5]. Higher-order methods such as the GW approximation (GWA) [6] are capable

Layered transition metal phosphates and phosphites (TMPs) are a class of materials composed of layers of 2D sheets bound together via van der Waals interactions and/or hydrogen bonds. Explored primarily for use in proton transfer, their unique chemical tunability also makes TMPs of interest for forming large-scale hybrid materials. Further, unlike many layered materials, TMPs can readily be solution exfoliated to form single 2D sheets or bilayers, making them exciting candidates for a variety of applications. However, the electronic properties of TMPs have largely been unstudied to date. In this work, we use first-principles computations to investigate the atomic and electronic structure of TMPs with a variety of stoichiometries. We demonstrate that there exists a strong linear relationship between the band gap and the ionic radius of the transition metal cation in these materials, and show that this relationship, which opens opportunities for engineering new compositions with a wide range of band gaps, arises from constraints imposed by the phosphorus-oxygen bond geometry. In addition, we find that the energies of the valence and conduction band edges can be systematically tuned over a range of ∼3 eV via modification of the functional group extending from the phosphorus. Based on the Hammett constant of this functional group, we identify a simple, predictive relationship for the ionization potential and electron affinity of layered TMPs. Our results thus provide guidelines for systematic design of TMP-derived functional materials, which may enable new approaches for optimizing charge transfer in electronics, photovoltaics, electrocatalysts, and other applications.

The performance of bulk organic and hybrid organic-inorganic heterojunction photovoltaics is often limited by high carrier recombination arising from strongly bound excitons and low carrier mobility. Structuring materials to minimize the length scales required for exciton separation and carrier collection is therefore a promising approach for improving efficiency. In this work, first-principles computations are employed to design and characterize a new class of photovoltaic materials composed of layered transition metal phosphates (TMPs) covalently bound to organic absorber molecules to form nanostructured superlattices. Using a combination of transition metal substitution and organic functionalization, the electronic structure of these materials is systematically tuned to design a new hybrid photovoltaic material predicted to exhibit very low recombination due to the presence of a local electric field and spatially isolated, high mobility, two-dimensional electron and hole conducting channels. Furthermore, this material is predicted to have a large open-circuit voltage of 1.7 V. This work suggests that hybrid TMPs constitute an interesting class of materials for further investigation in the search for achieving high efficiency, high power, and low cost photovoltaics.

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