Computational Chemist

Job Title: Computational Physical Chemist (Scientist/ Senior Scientist)

Job Summary: Alsym Energy is seeking an exceptional computational physical chemist to shape the scientific direction of electrolyte discovery and development. You will combine deep physical chemistry with machine learning and AI, supported by expertise in molecular dynamics (MD) or density functional theory (DFT), to propose new formulations and establish predictive design rules. Working within Discovery and Early Prototyping team, you will own research questions from hypothesis through experimental validation and translate molecular insight into improvements in battery performance, lifetime, and safety.

Industry Background: Energy storage is one of the fastest-growing sectors in the global energy economy, with annual stationary storage capacity additions projected to grow 23% per year from 2025 to 2035, driven by surging electricity demand from electrification, data centers, and AI infrastructure. Alsym Energy is the leading developer of non‑flammable, high‑performance, low‑cost sodium‑ion batteries for AI data centers, utilities, commercial real estate and anywhere else energy storage is needed.

• PhD in physical chemistry, computational chemistry, chemical engineering, electrochemistry, or a closely related field, with a record of original research and increasing scientific independence.
• Deep understanding of thermodynamics, statistical mechanics, chemical kinetics, and electrochemistry, with demonstrated application to liquid electrolytes, molecular liquids, or electrochemical interfaces.
• Research-level expertise in either MD or DFT, including sound choices of models and approximations, convergence or sampling checks, and interpretation of molecular mechanisms.
• Strong hands‑on ML and AI experience for scientific prediction, including model development, rigorous validation, uncertainty assessment, and recognition of data leakage and extrapolation limits.
• Strong Python and scientific software skills, experience with relevant simulation and ML packages, and the ability to build reproducible workflows on high‑performance computing systems.
• Evidence of exceptional scientific contributions through impactful publications, patents, research software, or industrial advances. Ability to initiate projects, collaborate with experimentalists, and explain how results change a development decision.
• Define and lead a computational electrolyte research agenda. Identify high‑value scientific questions, propose testable hypotheses, and recommend the formulations and experiments that should be pursued next.
• Use MD or DFT to understand solvent, salt, and additive interactions, including ion solvation and association, transport, reaction pathways, and electrode interfaces. Select methods appropriate to each question.
• Develop ML and AI models that combine simulation and experimental data to predict electrolyte properties and explore new composition spaces. Use active learning or Bayesian optimization where they improve candidate selection.
• Investigate chemical and electrochemical stability, interphase formation, and thermal decomposition. Work with diagnostics and cell‑safety colleagues to connect molecular mechanisms to experimentally validated degradation and thermal‑risk indicators.
• Turn results into predictive design rules and ranked candidates that balance transport, stability, interfacial compatibility, temperature range, cost, and formulation practicality. Test predictions on compositions beyond the training dataset.
• Partner with experimental electrolyte scientists to design discriminating experiments, interpret conflicting results, and update models. Define validation criteria and communicate uncertainty and limits…