chemical space; density functional theory; perturbation theory; computational alchemy
von Rudorff Guido Falk, von Lilienfeld O. Anatole (2021), Simplifying inverse materials design problems for fixed lattices with alchemical chirality, in
Science Advances, 7(21), eabf1173-eabf1173.
von Rudorff Guido Falk, Heinen Stefan N, Bragato Marco, von Lilienfeld O Anatole (2020), Thousands of reactants and transition states for competing E2 and S$_\mathrm{N}$2 reactions, in
Machine Learning: Science and Technology, 1(4), 045026-045026.
Bragato Marco, von Rudorff Guido Falk, von Lilienfeld O. Anatole (2020), Data enhanced Hammett-equation: reaction barriers in chemical space, in
Chemical Science, 11(43), 11859-11868.
Käser Silvan, Koner Debasish, Christensen Anders S., von Lilienfeld O. Anatole, Meuwly Markus (2020), Machine Learning Models of Vibrating H 2 CO: Comparing Reproducing Kernels, FCHL, and PhysNet, in
The Journal of Physical Chemistry A, 124(42), 8853-8865.
Domenichini Giorgio, von Rudorff Guido Falk, von Lilienfeld O. Anatole (2020), Effects of perturbation order and basis set on alchemical predictions, in
The Journal of Chemical Physics, 153(14), 144118-144118.
Heinen Stefan, Schwilk Max, von Rudorff Guido Falk, von Lilienfeld O Anatole (2020), Machine learning the computational cost of quantum chemistry, in
Machine Learning: Science and Technology, 1(2), 025002-025002.
von Rudorff Guido Falk, von Lilienfeld O. Anatole (2020), Alchemical perturbation density functional theory, in
Physical Review Research, 2(2), 023220-023220.
von Rudorff Guido Falk, von Lilienfeld O. Anatole (2020), Rapid and accurate molecular deprotonation energies from quantum alchemy, in
Physical Chemistry Chemical Physics, 22(19), 10519-10525.
von Rudorff Guido Falk, von Lilienfeld O. Anatole (2019), Atoms in Molecules from Alchemical Perturbation Density Functional Theory, in
The Journal of Physical Chemistry B, 123(47), 10073-10082.
Zaspel Peter, Huang Bing, Harbrecht Helmut, von Lilienfeld O. Anatole (2019), Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited, in
Journal of Chemical Theory and Computation, 15(3), 1546-1559.
Fias Stijn, Chang K. Y. Samuel, von Lilienfeld O. Anatole (2018), Alchemical Normal Modes Unify Chemical Space, in
The Journal of Physical Chemistry Letters, 10(1), 30-39.
Chang K. Y. Samuel, von Lilienfeld O. Anatole (2018), AlxGa1−xAs crystals with direct 2 eV band gaps from computational alchemy, in
Physical Review Materials, 2(7), 073802-073802.
von Lilienfeld O. Anatole (2018),
Quantum Machine Learning in Chemical Compound Space, 57(16), 4164-4169, Wiley, Germany 57(16), 4164-4169.
Much research is devoted to the study of the relationship between the properties of materials and their chemical composition and structure. Rigorously rooted in quantum mechanics (QM), statistical mechanics, and heavy computing, employed methods nowadays permit to routinely predict relevant properties for novel combinations of atomic configurations and composition, effectively sampling chemical compound space (CCS). The task of using QM for the discovery of novel compounds with improved properties is therefore equivalent to a combinatorial optimization problem in CCS. Albeit straight-forward, tackling this task through QM based high-throughput screening is the least efficient way. This proposal is about the advancement of methods which enable the exploration and sampling of CCS in dramatically more efficient ways, without loss of accuracy. In particular, we plan to use ``alchemical'' perturbation theory which leverages differential, response-like, QM calculations of reference molecules and materials. Results for these perturbation theoretical calculations can subsequently be usedto accurately estimate properties of thousands, if not millions, new molecules and materials ``close by'' in CCS with negligible, or constant, computational overhead. Building on recent contributions, we propose three sub projects: (1) to investigate alchemical second and higher order perturbations to afford potential energy estimates of molecules with unprecedented speed and accuracy. (2) to use alchemical estimates for the tailoring of electronic properties in novel materials (important for renewable energy applications),and (3) to make accurate alchemical estimates of chemical reaction energy profiles (important for improved reaction conditions or catalysts).