Molecular Modeler

Job Description

You will be part of the Multi Scale modelling team based in India. This is bps core team for advanced scientific modelling capabilities - computational fluid dynamics, finite-element structural analysis, and general multi-physics modelling - supporting all bps current and future businesses. The team solves complex problems through science-based modelling from molecular level to system scale for accelerating technology development, improving component reliability and lowering cost. Molecular modelling and computational simulations are indispensable tools for gaining a deeper understanding of the underlying mechanisms, predicting chemical behaviour, and accelerating the discovery of new materials and processes. Techniques, ranging from quantum mechanics to molecular dynamics simulations, allow us to investigate the structural, electronic, and kinetic aspects of reactions. Accountabilities: Work with multi-functional teams to understand scientific problems, and develop and implement computational models and algorithms to solve them. Analyze and interpret large-scale data sets. Stay updated with the latest advancements in computational science and related subject areas. Structure optimization: Determining the most stable arrangement of atoms in a molecule or complex system, considering factors such as bond lengths, angles, and dihedral angles. Methods: QM (e.g., DFT), MM, MD. Free energy calculations: Evaluating the free energy profiles of chemical reactions involved in electrochemical processes, such as oxidation-reduction reactions or electrode-electrolyte interactions. Methods: QM, thermodynamic integration, enhanced sampling (umbrella sampling, meta-dynamics). Electron transfer: Investigating the mechanisms and rates of electron transfer reactions, including electron transfer pathways, transition states, and activation energies. Methods: Time-dependent DFT, transition state theory. Solvation effects: Studying the influence of solvents on electrochemical reactions by considering solvent molecules interactions with the solute species and their impact on reaction kinetics and thermodynamics. Methods: Implicit, explicit solvent models, MD, QM/MM Redox properties: Predicting and analysing the redox potentials and electron affinities of molecules, ions, or complexes, which are crucial for understanding their electrochemical behaviour. Methods: QM Adsorption and surface phenomena: Simulating the adsorption of molecules or ions on electrode surfaces, examining the structure and stability of adsorbates, and evaluating their impact on electrochemical processes. Methods: QM, MD, Monte-Carlo. Design of electrochemical materials: Guiding the development of new materials for electrochemical applications by screening and optimizing molecular structures or complexes with desired electrochemical properties. Methods: high-throughput screening, machine learning, QSPR. Experience / Capabilities: PhD or equivalent experience in computational science, physics, mathematics, computer science or related field, or MS/BS/MEng/BEng with sufficient experience. Proven experience as a computational scientist or similar role. Knowledge of high-performance computing, data analysis, and scientific programming. Strong understanding of mathematical and statistical models.