First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning

First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to predict properties of heterostructures incorporating fabrication-dependent variability. Machine-learning (ML) approaches are increasingly being used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited ML techniques to characterize relationships between local atomic structures and global electronic transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of fabricated silicon/germanium heterostructures, matching measured data. We demonstrate application of ML approaches to extract important physics that determines electronic transport in semiconductor heterostructures, and bridge the gap between ab initio accessible models and fabricated systems. We anticipate that ETI framework would have broad applicability to diverse materials classes.

DCore: Integrated DMFT software for correlated electrons

We present a new open-source program, DCore, that implements dynamical mean-field theory (DMFT). DCore features a user-friendly interface based on text and HDF5 files. It allows DMFT calculations of tight-binding models to be performed on predefined lattices as well as ab initio models constructed by external density functional theory codes through the Wannier90 package. Furthermore, DCore provides interfaces to many advanced quantum impurity solvers such as quantum Monte Carlo and exact diagonalization solvers. This paper details the structure and usage of DCore and shows some applications.

A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

Accurate protein structure prediction with hydroxyl radical protein footprinting data

Hydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure.