Uncertainty-Aware ML Surrogates for DFT Strain–Bandgap Engineering in CsSnX3 (X = Cl, Br, I) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Uncertainty-Aware ML Surrogates for DFT Strain–Bandgap Engineering in CsSnX3 (X = Cl, Br, I) Md. Nurul Amin, Muhammad Mudassir Ahmad Alwi, Muhammad Jawad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8021864/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Strain is a powerful and reversible knob to tune the electronic structure of halide perovskites, but brute-force first-principles mapping of wide compressive/tensile windows is costly and can be numerically fragile near metallization. We develop uncertainty-aware machine-learning surrogates for the strain–band-gap relation 𝐸𝑔 (𝜀) in cubic CsSnX3 (X = Cl, Br, I) under isotropic and uniaxial-𝑐 loading. A curated, convergence-checked DFT dataset (PBE, ultrasoft pseudopotentials, 600 eV cutoff, 12×12×12 𝑘-mesh; SOC omitted by design) is used to train per-curve models selected via leave-one-out cross-validation across linear, kernel, tree, instance-based, and Gaussian-process families. Kernel surrogates dominate: Gaussian processes and kernel ridge achieve state-of-the-art accuracy, including a LOO RMSE ∼0.025 eV for CsSnBr3 under uniaxial-𝑐. A single “hard” case, CsSnCl3 under isotropic compression-shows local non-convexity near an incipient metallic window, for which a 𝑘NN model outperforms smooth kernels and the predictive uncertainty widens appropriately. Calibrated intervals (analytic 𝜎 for GPR; bootstrap 𝜎 otherwise) support principled, goal-optional acquisition of next-DFT points, concentrating effort where information gain is highest. Deformation-potential analysis at 𝜀=0% yields two robust trends: isotropic loading produces larger |𝑎𝑔| than uniaxial-𝑐 for all halides, and the isotropic sensitivity follows Cl > Br > I. The resulting surrogates provide fast, interpretable, and confidence-quantified maps for strain–band-gap engineering and a reproducible playbook to direct high-value calculations. All data, models, and scripts are released to enable reuse. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Physical sciences/Physics DFT halide perovskites strain engineering kernel methods (GPR/KRR) uncertainty quantiőcation deformation potentials Full Text Additional Declarations No competing interests reported. Supplementary Files SI.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers invited by journal 08 Nov, 2025 Editor invited by journal 06 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Submission checks completed at journal 04 Nov, 2025 First submitted to journal 03 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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