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Accurate XC functionals for surface and materials chemistry

The XC functionals we develop are based on a combination of data science and physical constraints. Training data consists of experimental and quantum chemistry reference data. We thus devise functionals that are accurate for a number of relevant materials properties.

The metaGGA MCML [Brown, Maimaiti, Trepte, Bligaard, & Voss, J. Comput. Chem. 42, 2004 (2021)] is a multipurpose functional for materials science yielding good surface chemical as well as bulk structural predictions. Fᴏʀᴛʀᴀɴ source code implementing MCML can be downloaded from our github page. Our functionals are also included with libxc, which is interfaced to many DFT codes. Below is an example how to use MCML via libxc with the VASP code. A recent version of libxc is needed, which can be checked out from gitlab:
   git clone
   cd libxc
   autoreconf -i
   ./configure --prefix=libxcinstallationprefix --disable-fhc CC=gcc FC=gfortran
   make && make install

Replace libxcinstallationprefix with an installation path for libxc (and, e.g., gcc with icc and gfortran with ifort, respectively, if these compilers are going to be used to compile VASP instead of the GNU compilers). Following the instructions on how to compile VASP with libxc support, add the following (towards then end of) VASP's makefile.include before compilation:
   LIBXC_ROOT   = libxcinstallationprefix
   LLIBS       += -L$(LIBXC_ROOT)/lib -lxcf03 -lxc
   INCS        += -I$(LIBXC_ROOT)/include

MCML can then be employed in VASP simulations with the following INCAR settings (in addition to other useful settings for metaGGA simulations, such as LASPH=.TRUE. or switching to conjugate gradients with ALGO=A in cases of poor convergence)
   # parameters for MCML DOI:10.1002/jcc.26732

The metaGGA VCML-rVV10 [Trepte & Voss, J. Comput. Chem. 43, 1104 (2022)] consists of simultaneously trained semi-local and non-local interactions, yielding an accurate and balanced description of chemi- and physisorption on metal surfaces in addition to a good description of bulk structural properties. VCML-rVV10 can be employed in VASP simulations with the following INCAR settings:
   # parameters for VCML-rVV10 DOI:10.1002/jcc.26872
   BPARAM   = 15.35