Spatial statistics for screening molecular structures
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910228293877760 |
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| author | Ray, Pranoy Kalidindi, Surya R. |
| author_facet | Ray, Pranoy Kalidindi, Surya R. |
| contents | The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message-passing graph networks and equivariant models, that require millions of DFT-labeled training structures and produce non-convex latent representations that complicate continuous optimization for inverse design. These architectures are impractical in data-scarce regimes, which is the typical case in molecular screening, and exhibit well-documented limitations in capturing chemically disordered configurations and chiral geometries. This review presents feature engineering based on spatial statistics as a physically rigorous and immediately deployable alternative. Molecular structures are encoded as voxelized scalar fields, and two-point auto- and cross-correlations are evaluated deterministically via Fast Fourier Transforms, explicitly transferring the burden of spatial pattern recognition from the learning algorithm to a closed-form, physics-informed operation. Principal component analysis of the resulting correlation maps yields low-dimensional, strictly convex representations that support lean neural networks (<100k trainable parameters) and non-parametric surrogate models, achieving sub-2% prediction error with as few as 10 training samples. Demonstrated across periodic crystals, chemically disordered high-entropy alloys, and non-periodic organic molecules, this framework enables Bayesian active learning and zero-shot extrapolation on commodity hardware, which current large-scale architectures cannot replicate at equivalent data budgets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17147 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spatial statistics for screening molecular structures Ray, Pranoy Kalidindi, Surya R. Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message-passing graph networks and equivariant models, that require millions of DFT-labeled training structures and produce non-convex latent representations that complicate continuous optimization for inverse design. These architectures are impractical in data-scarce regimes, which is the typical case in molecular screening, and exhibit well-documented limitations in capturing chemically disordered configurations and chiral geometries. This review presents feature engineering based on spatial statistics as a physically rigorous and immediately deployable alternative. Molecular structures are encoded as voxelized scalar fields, and two-point auto- and cross-correlations are evaluated deterministically via Fast Fourier Transforms, explicitly transferring the burden of spatial pattern recognition from the learning algorithm to a closed-form, physics-informed operation. Principal component analysis of the resulting correlation maps yields low-dimensional, strictly convex representations that support lean neural networks (<100k trainable parameters) and non-parametric surrogate models, achieving sub-2% prediction error with as few as 10 training samples. Demonstrated across periodic crystals, chemically disordered high-entropy alloys, and non-periodic organic molecules, this framework enables Bayesian active learning and zero-shot extrapolation on commodity hardware, which current large-scale architectures cannot replicate at equivalent data budgets. |
| title | Spatial statistics for screening molecular structures |
| topic | Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2605.17147 |