Paper

Lithium-ion batteries (LIBs) are an essential building block for modern energy storage. The solid-electrolyte interface (SEI) is an important component of LIBs, which acts as a passivation layer and prevents electrode and electrolyte from further decomposition and, thus, from capacity loss. In this work, we investigated the first steps of SEI formation initiated from commonly used electrolyte com- pounds ethylene carbonate (EC), diethyl carbonate (DEC), vinylele carbonate (VC), and 1,3-propane sultone (PS). Ab initio molecular dynamics (AIMD) simulations based on density functional theory was used to discover chemical reactions without chemical intu- ition. In order to simulate the reductive potential at the electrode, electrons are added sequentially to the system, leading to electro- reductive decomposition of the compounds. It was observed that this progressive electron addition leads to the formation of various reaction products, which can act as further reactants in subsequent reactions. Further reaction products were observed, some of which were reactions known from the literature, but also other, ener- getically less favorable structures were discovered. The molecular structures found in the AIMD simulations agree closely with experi- mental findings, validating the accuracy and reliability of the herein presented approach of sequentially adding electrons in molecular simulations.

Cancer treatment is often hindered by the emergence of drug resistance, frequently driven by novel mutations in oncogenes or drug-targeted pathways. Predicting resistance mechanisms is critical for informing therapeutic strategies and improving patient outcomes. Here, we present a computational workflow that leverages high-performance computing (HPC) resources to systematically evaluate the impact of emerging mutations on drug efficacy. Our workflow integrates deep learning structure prediction, molecular dynamics simulations, molecular docking, and binding predictions of known compounds to predict resistance mechanisms and propose alternative therapeutic options. We also explore quantum chemical calculations as a tool to complement experimental validations to better understand the binding preferences between different protein forms.