• Corpus ID: 221006034

Solving the inverse materials design problem with alchemical chirality

  title={Solving the inverse materials design problem with alchemical chirality},
  author={Guido Falk von Rudorff and O. Anatole von Lilienfeld},
  journal={arXiv: Chemical Physics},
Massive brute-force compute campaigns relying on demanding ab initio calculations routinely search for novel materials in chemical compound space, the vast virtual set of all conceivable stable combinations of elements and structural configurations which form matter. Here we demonstrate that 4-dimensional chirality, due to an `alchemical' reflection plane in the nuclear charge space of the electronic Hamiltonian, dissects that space, defining approximate ranks among sub-sets which effectively… 

Figures from this paper

Simplifying inverse materials design problems for fixed lattices with alchemical chirality
It is demonstrated that four-dimensional chirality arising from antisymmetry of alchemical perturbations dissects CCS and defines approximate ranks, which reduce its formal dimensionality and break down its combinatorial scaling.
Ab Initio Machine Learning in Chemical Compound Space
While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.
Quantum Machine Learning in Chemical Compound Space
The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.


Alchemical Normal Modes Unify Chemical Space.
This work presents a unification of coordinate and composition space in terms of alchemical normal modes (ANMs) which result from second order perturbation theory and promises to generally enable unbiased compound exploration campaigns at reduced computational cost.
Quantum mechanical treatment of variable molecular composition: from 'alchemical' changes of state functions to rational compound design.
To demonstrate the predictive power of alchemical first order derivatives (Hellmann-Feynman) the covalent bond potential of hydrogen fluoride and hydrogen chloride is investigated, as well as the hydrogen bond in the water-water and water-hydrogen fluoride dimer, respectively.
Exploring Chemical Space with Alchemical Derivatives: BN-Simultaneous Substitution Patterns in C60.
The present study fully exploits the computational advantages of the alchemical derivatives in larger three-dimensional systems and offers an interesting venue to study BN substitution patterns in higher fullerenes and graphene and paves the way for more efficient exploration of the Chemical Space.
Alchemical screening of ionic crystals.
Alchemical perturbations are introduced as a rapid and accurate tool to estimate fundamental structural and energetic properties in pure and mixed ionic crystals and mean absolute errors are on par with the density functional theory level of accuracy for energies and bulk moduli.
Guiding ab initio calculations by alchemical derivatives.
It is suggested that alchemical transformations could be meaningful for enhanced sampling in the context of virtual high-throughput materials screening projects.
The inverse band-structure problem of finding an atomic configuration with given electronic properties
Modern crystal-growth techniques, such as molecular beam epitaxy or metal–organic chemical-vapour deposition, are capable of producing prescribed crystal structures, sometimes even in defiance of
First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties
A well-defined notion of chemical compound space (CCS) is essential for gaining rigorous control of properties through variation of elemental composition and atomic configurations. Here, we give an
Alchemical derivatives of reaction energetics.
Alchemical derivatives are shown to be predictive for integer changes in atomic numbers for oxygen binding to a 79 atom palladium nanoparticle, illustrating their potential use in gradient-based optimization algorithms for the rational design of catalysts.
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.
This work introduces a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries, and derives a tensor kernel adapted to rotational symmetry.
Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds
The largest available database of potentially exfoliable 2D materials has been obtained via high-throughput calculations using van der Waals density functional theory.