Lila Sciences·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Technical screen at Lila Sciences for a Research Engineer role. The whole conversation basically lived inside one question about DFT and how your choice of XC functional ripples through to MLIP quality. Dense material, and I wasn't fully warmed up for how deep they wanted to go on the cost-accuracy tradeoffs.

Questions Asked (1)

When generating DFT training data for machine-learning interatomic potentials, how does the choice of exchange-correlation functional (LDA, GGA/PBE, meta-GGA, hybrid) affect data quality and consistency, and how do those tradeoffs in cost versus accuracy carry forward into the downstream MLIP?

Technical Trade-offsSystem Design

Author's notes

This one took me a minute to frame properly.

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AI HintsAI Generated

Suggested Approach

Frame the answer around the accuracy-cost tradeoff ladder from LDA to hybrids, emphasizing that the functional choice must be consistent across all training data to avoid spurious energy/force discontinuities. Then connect this to downstream MLIP performance: higher-level functionals improve transferability and property prediction but increase data generation cost, requiring strategic sampling and validation.

Pro tip: Mention that mixing functionals in a single training set is a common pitfall that leads to inconsistent reference energies and poor MLIP generalization; always document the functional and use it consistently, and consider using a lower-level functional for pre-screening and a higher-level one for final data.

1. Define the functional hierarchy and tradeoffs

Briefly describe LDA, GGA/PBE, meta-GGA, and hybrids in terms of accuracy and computational cost, noting that accuracy generally increases with cost but not uniformly for all properties.

2. Assess impact on data quality and consistency

Explain how the functional affects energies, forces, and stresses, and why consistency is critical: mixing functionals introduces noise and biases that ML models cannot disentangle.

3. Connect to MLIP training and downstream performance

Discuss how functional choice propagates to MLIP accuracy, transferability, and physical fidelity, and how errors in reference data become systematic errors in predictions.

4. Propose a cost-aware strategy

Suggest practical approaches like using GGA/PBE for large-scale data generation, meta-GGA for refinement, and hybrids for validation or small critical datasets, with active learning to minimize high-cost calculations.

5. Highlight validation and uncertainty quantification

Emphasize the need to validate MLIPs against higher-level reference data and to quantify uncertainties arising from functional choice, ensuring robustness for the target application.

Key Points to Mention

LDA tends to overbind and is rarely used for MLIP training due to poor accuracy.
GGA/PBE is the workhorse for MLIPs, offering a good balance of cost and accuracy for many systems.
Meta-GGAs (e.g., SCAN) improve accuracy for diverse bonding but are more expensive and can be numerically sensitive.
Hybrid functionals (e.g., HSE) provide higher accuracy for properties like band gaps and reaction barriers but are prohibitively expensive for large-scale data generation.
Consistency in functional choice is crucial; mixing functionals leads to inconsistent reference data and degraded MLIP performance.
Cost-accuracy tradeoffs can be managed via active learning, transfer learning, or multi-fidelity approaches.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.