Continuous perception and adaptive metrics for scalable structure refinement
Vincenzo Barone
Abstract
We present a geometry-refinement workflow that adapts to the cost of an energy--gradient evaluation and the reliability of molecular topology. An initial Cartesian calculation and continuous bonding diagnostics determine whether to begin in projected Cartesian coordinates or in chemically adapted internal coordinates. Chemical perception generates local redundant measurements; sparse rank and conditioning tests combine them into nonredundant, symmetry-adapted coordinates; and a common optimizer uses the selected representation with resident or external calculators. For inexpensive gradients, the projected Cartesian route limits coordinate overhead; a single monitored internal-coordinate step can then provide chemical interpretability. When gradients are costly and topology is certified, symmetry-adapted internal coordinates reduce expensive energy--gradient calls. A quadraticized map improves localized anharmonic cases, while mixed Cartesian/internal charts cover uncertain or complex topologies. Local sparse operators and resident force-field and tight-binding calculators extend both routes to large systems. The same Cartesian calculator contract supports multilevel energy and gradient composition. The measured coordinate pipeline and recurrent resident tight-binding update show near-linear scaling over the tested size ranges; this statement applies to those qualified components, not to arbitrary electronic-structure calculations or cold starts. For certified, directly separable internal charts, the optimizer can use chemically designed Morse or Gaussian maps to quadraticize the local step problem. The quadraticized branch uses a short geometry-local history, while delocalized charts remain unchanged. This reduces iterations in difficult minima without changing the electronic potential or coordinate chart.
Create a lesson
Related papers
Spin Migration in the Argon Dimer
Stefanos Carlström, Serguei Patchkovskii, Misha Yu Ivanov
Accuracy of Vibrational Properties of Machine-Learning Force Fields for Molecular Crystal Transformations
Natalia Goncharova, Oskar Schweighofer, Johannes Hoja
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals
Manuel V. Klockow, Simon Wagner, Roman Remme et al.
Unveiling the Influence of Graphitic Nanocarbon Structures, Defects, and Surface Functional Groups on Supercapacitive Performance in LiTFSI Electrolyte
Phongphot Sakulaue, Bénédicte Réty, Takafumi Ishii et al.
Quantum dynamics of an ion in an RF quadrupole trap with weak octupolar anharmonicity
Sukanya Jana, Anushree Dey, Pintu Manadal et al.
Quantitative modeling of acid-base equilibria
Qianjun Xu, Simin Liu, Cesare Malosso et al.