AlxGa1−xAs crystals with direct 2 eV band gaps from computational alchemy

Abstract

We use alchemical first order derivatives for the rapid yet robust prediction of band structures. The power of the approach is demonstrated for the design challenge of finding AlxGa1−xAs semiconductor alloys with large direct band gap using computational alchemy within a genetic algorithm. Dozens of crystal polymorphs are identified for x > 2 3 with direct band gaps larger than 2 eV according to HSE approximated density functional theory. Based on a single generalized gradient approximated density functional theory band structure calculation of pure GaAs we observe convergence after visiting only ∼800 crystal candidates. The general applicability of alchemical gradients is demonstrated for band structure estimates in III-V and IV-IV crystals as well as for H2 uptake in Sr and Ca-alanate crystals.

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

“…Hence, we believe that it is warranted, and without loss of generality, to focus on constitutional isomers in rigid lattices for which thermal or geometrical distortions can be neglected. Note that relative offsets in total energies due to differences in stoichiometry are straightforward to estimate, and typically occur on different orders of magnitude, and that subsequent inclusion of configurational degrees of freedom within alchemical predictions is possible, as already demonstrated for small molecules (41,42) and ionic, metallic, and semiconducting solids (43)(44)(45). Hence, the focus of the following applications lies on breaking down the combinatorially scaling problem of energy predictions throughout colored chemical bond connectivity graphs.…”

Deep elastic strain engineering of bandgap through machine learning

“…This method of tweaking the material property is conceptually based on chemical alloying, whereby the chemical composition is tuned in an alloy melt to produce desirable strength or ductility. Invoking this approach, conventional bandgap engineering resorted to chemical alloying such as GaAl1−xAsx or Ga1−xInxAs (17). However, we have demonstrated here that the stress-free situation is usually not the optimal state for a figure of merit, and elastic strains allow the bandgap to exhibit many more possible values so that each pure material candidate should occupy a much larger hyperspace enabled through the achievable 6D strain space.…”

Quantum algorithm for alchemical optimization in material design

“…On the other hand, despite a long tradition of ML methods in pharmaceutical applications [5][6][7][8][9] and many successful applications as filters applied to large molecular libraries 10 , the overall usefulness of ML for molecular design is still controversial 11,12 . Of different nature are the alchemical perturbative approaches [13][14][15] and the more recent ML techniques trained across the CCS and used to predict, among others, reorganization energies 16 , chemical reactivity 17 , and crystal properties 18,19 . The automatic generation of ML models for classical and quantum observables has only recently been accomplished within the rigorous realm of physical chemistry 20 .…”