Session B32: Material modeling and computational methods for Quantum science and advanced applications

Machine learning for combinatorial exploration of quantum materials

We have been applying machine learning (ML) to high-throughput experimentation in a variety of ways in order to discover new quantum materials [1]. The main focus of our exploration have been superconductors and topological insulators.  The combinatorial strategy can be used to uncover minute details of composition - structure – property relationships which reflect the physical origin of superconductivity [2]. ML can substantially enrich combinatorial experimentation in guiding the experiments as well as streamlining the massive amount of data which result from combinatorial libraries. For instance, we have developed ML models for superconducting critical temperature which can be used to predict possible new superconductors. Our recent emphasis is to develop autonomous combinatorial experimentation techniques based on Bayesian active learning which can further speed up the screening process by removing the necessity to test every composition on libraries. We have shown that we can reduce the number of experiments by an order of magnitude in this manner. I will discuss our recent live demonstration where autonomous interaction between experiment and theory is used to rapidly map thin-film phase diagrams of multinary systems in closed-loop cycles. This work is carried out in collaboration with A. G. Kusne, V. Stanev, H. Liang, H. Yu, J. Park, and J. Paglione.   

Charting the electronic structure of inorganic materials.

I'll first present our efforts towards verified, curated, and reproducible materials simulations, powered by AiiDA (https://www.aiida.net/) and disseminated on the Materials Cloud (https://www.materialscloud.org/), under a model of open-source software and open-access data with full provenance of every step in the simulation workflows. Brief mention will be made of the verification of pseudopotentials for density-functional theory calculations, the curation of protocols for targeted accuracies, and the deployment of robust direct-minimization approaches on accelerated architectures. Then, I will discuss our approach to capture the electronic structure fingerprints of inorganic materials in the form of atom-centered maximally localized Wannier functions spanning the valence and the lower part of the conduction manifolds, obtained through a novel approach to disentanglement and localization also aimed at full automation. Early applications to the exploration of the electronic-structure fingerprints will also be presented.   

Multi-armed quantum bandits: Exploration versus exploitation when learning properties of quantum states

We initiate the study of tradeoffs between exploration and exploitation in online learning of properties of quantum states. Given sequential oracle access to an unknown quantum state, in each round, we are tasked to choose an observable from a set of actions aiming to maximize its expectation value on the state (the reward). Information gained about the unknown state from previous rounds can be used to gradually improve the choice of action, thus reducing the gap between the reward and the maximal reward attainable with the given action set (the regret). We provide various information-theoretic lower bounds on the cumulative regret that an optimal learner must incur, and show that it scales at least as the square root of the number of rounds played. We also investigate the dependence of the cumulative regret on the number of available actions and the dimension of the underlying space. Moreover, we exhibit strategies that are optimal for bandits with a finite number of arms and general mixed states. If we have a promise that the state is pure and the action set is all rank-1 projectors the regret can be interpreted as the infidelity with the target state and we provide some support for the conjecture that measurement strategies with less regret exist for this case.   

2-D Materials Modelling: from Transistors to Majorana Fermions

Since the first experimental demonstration of a monolayer MoS₂ transistor in 2011, transition metal dichalcogenides (TMDs) have received a wide attention from the scientific community as potential replacement for Silicon FinFETs at the end of the semiconductor roadmap. As graphene, TMDs exhibit excellent electrostatic properties due to their 2-D nature, but contrary to it, they are characterized by large band gaps, while keeping acceptable mobilities. However, so far, no transistor based on a TMD channel could outperform the Si technology. While this limitation can be partly attributed to technical issues, the TMD bandstructure also explains this behavior: electrons/holes are not fast enough to allow for large ON-state currents. 

Through density functional theory (DFT), the existence of more than 1,800 2-D materials was recently predicted. Among them there might be components with better transport properties than TMDs. We therefore selected 100 monolayers out of this database of 2-D materials, combined DFT and quantum transport to simulate their “current vs. voltage” characteristics, and identified 13 candidates with both n- and p-type ON-state currents larger than what Si FinFETs are expected to deliver in the future. The results of these theoretical investigations will be presented in this talk, together with the possible application of conventional and exotic 2-D materials as hosts for Majorana Fermions.