We focus on the development of heterogenous catalyst for electrosynthesis of important base chemicals, such as ammonia, alcohols and olefins. This includes both independent computational studies and collaborations with internationally renowned experimental groups. We will continue exploring the unique capacity of boron doped silicon and germanium compounds, e.g. B-silicene and B-germanene, to selectively bind N2, CO and NO. In particular, diatom catalysts based on boron doped silicene have been shown to function as effective four atom catalysts and promote C-C cross coupling resulting in high valued products. We will continue the development of solid catalysts of metals ligated with NCN units for electrocatalytic reduction of nitrate and nitrite to ammonia. In particular, we will explore high entropy catalysts based on such systems. There is also new project supported by Energimyndigheten for developing heterogenous catalysts for conversion of acetic acid to ketene where Prof Weissenrieder Applied Physics (KTH) contribute with surface characterization using synchrotron measurements. Electrocatalytic modeling is highly challenging and requires resource-demanding periodic DFT computations using VASP. To model the reaction mechanism and free energy surface of a single reaction for a single catalyst typically requires the computation of structure, phonon frequencies and free energy of more than 25 reaction intermediates and products. Highly demanding transition state calculations using the nudge elastic band (NEB) approach are needed to determine the kinetics of key chemical and electrochemical steps. NEB calculations parallelize well and the performance is good using 16 nodes or more on Dardel. Electrochemical reactions take place in aqueous solution and implicit solvation using Vaspsol is included. In some cases resource demanding DFT MD simulations with explicit water molecules are needed. Long MD simulations are used, e.g. to estimate catalyst stability. Furthermore, the development of new catalysts requires structure prediction when crystal structures are not available, e.g. our collaborators are developing catalysts based on solid solutions of ligated transition metals in varying ratio. Structure prediction is highly demanding as a large number of DFT structure optimizations have to be run in parallel but the task scales well. Analysis of high entropy materials is particularly demanding and requires structure prediction followed by high temperature MD simulations on multiple structures. In additions to conventional DFT computations we will continue to develop the molecular surface property approach (MSPA). It is computationally efficient as it allows all the catalytic sites and their associated activities to be estimated from local surface properties (MSPs) obtained from a single DFT computation of the bare catalyst. The MSPs are used in the analysis and development of catalysts and for the development of machine learning models for catalyst design. We have python codes for automated computation and analysis of MSPs that work with a variety of DFT codes, e.g. CP2K and VASP (gitlab.com/chliu2018/cqcg). A new Fortran program is under development that will allow for real time analysis of local minima and maxima as well as descriptors related to the statistical distribution of MSPs.