BIOVIA Materials Studio 2019

BIOVIA Materials Studio 2019 is the latest release of BIOVIA’s predictive science tools for chemistry and materials science research. Materials Studio empowers researchers to understand the relationships between a material’s ato-mic and molecular structure and properties in order to make more informed decisions about material research and development. Using Materials Studio 2019 scientists can simulate more materials and more properties than ever before - for example in the design of new catalysts or new battery materials.

MORE SCIENCE – REACTION KINETICS A new module, Kinetix, has been made available. Kinetix provides predictions for the spacial distribution of reactants and products on reactive surfaces, such as catalyst surfaces, as a function of time using a Kinetic Monte Carlo approach. Tasks for examining the sensitivity of reaction mechanisms to individual reactions and for studying temperature programmed desorption have also been added to Materials Studio Cantera, to add yet more to the extensive suite of chemical engineering tools available in Materials Studio.DENSITY FUNCTIONAL THEORY FOR 1000S OF ATOMSThe features of ONETEP available through Materials Studio ONETEP UI has been significantly enhanced by adding capabilities for electron and spin transport, time dependent DFT for electronic excitation energies, optimization of the total spin of metallic systems and extension of solvation models to geometry optimization, transition state search and dynamics tasks.MORE MATERIAL PARAMETERSA new DFTB+ library, LIB 2019, designed for simulations of electrolytes in Li-ion batteries, has been added. The library contains parameters for the elements Li, C, H, O, N, F, P and will allow Li-ion diffusion to be measured as a function of electrolyte formulation. New GULP libraries for MEAM and ReaxFF RDX are also made available which can be applied to battery and high energy materials respectively.PERFORMANCE IMPROVEMENTSHybrid OpenMP/MPI parallelization has been made available for ONETEP and CASTEP calculations that will improve performance on compute clusters. Defaults have also been adjusted in ONETEP and Mesocite to improve performance without sacrificing accuracy. DMol3 geometry optimization in delocalized internal coordinates can also lead to substantial performance increases on highly parallel computers and/or for large systems, now that checkpoint information is no longer written to disk.Low-memory BFGS method with preconditioning (LBFGS) has been implemented in CASTEP geometry optimizer. LBFGS calculations can reduce the number of optimization steps significantly, especially for systems with large number of degrees of freedom.MORE PROPERTIESMulliken population analysis can now be applied in CASTEP for calculations with spin-orbit coupling. Additionally, prediction of chemical shifts are now possible for highly correlated systems where the use of DFT+U approach is required (for example, transition metal oxides or systems containing f-elements