We recently introduced the Alchemical Integral Transform (AIT), enabling the prediction of energy differences, and guessed an ansatz to parameterize space r in some alchemical change λ. Here, we present a rigorous derivation of AIT's kernel K and discuss the parameterization r(λ) in n dimensions, i.e., necessary conditions, mathematical freedoms, and additional constraints when obtaining it. Analytical expressions for changes in energy spectra and densities are given for a number of systems. Examples include homogeneous potentials such as the quantum harmonic oscillator, hydrogen-like atom, and Dirac well, both for one- and multiparticle cases, and a multiparticle system beyond coordinate scaling for harmonic potentials.
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http://dx.doi.org/10.1063/5.0245863 | DOI Listing |
J Chem Phys
January 2025
Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.
We recently introduced the Alchemical Integral Transform (AIT), enabling the prediction of energy differences, and guessed an ansatz to parameterize space r in some alchemical change λ. Here, we present a rigorous derivation of AIT's kernel K and discuss the parameterization r(λ) in n dimensions, i.e.
View Article and Find Full Text PDFJ Phys Chem Lett
November 2024
Institute for Theoretical Physics, Heidelberg University, 69120 Heidelberg, Germany.
Thermodynamic integration (TI) offers a rigorous method for estimating free-energy differences by integrating over a sequence of interpolating conformational ensembles. However, TI calculations are computationally expensive and typically limited to coupling a small number of degrees of freedom due to the need to sample numerous intermediate ensembles with sufficient conformational-space overlap. In this work, we propose to perform TI along an alchemical pathway represented by a trainable neural network, which we term Neural TI.
View Article and Find Full Text PDFJ Phys Chem B
November 2024
Laboratoire de Biochimie Théorique UPR 9080, Université Paris Cité, CNRS, 75005 Paris, France.
Colvars is an open-source C++ library that provides a modular toolkit for collective-variable-based molecular simulations. It allows practitioners to easily create and implement descriptors that best fit a process of interest and to apply a wide range of biasing algorithms in collective variable space. This paper reviews several features and improvements to Colvars that were added since its original introduction.
View Article and Find Full Text PDFJ Chem Phys
October 2024
Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany.
Accurate quantum mechanics based predictions of property trends are so important for material design and discovery that even inexpensive approximate methods are valuable. We use the alchemical integral transform to study multi-electron atoms and to gain a better understanding of the approximately quadratic behavior of energy differences between iso-electronic atoms in their nuclear charges. Based on this, we arrive at the following simple analytical estimate of energy differences between any two iso-electronic atoms, ΔE≈-(1+2γNe-1)ΔZZ̄.
View Article and Find Full Text PDFChem Sci
October 2024
Department of Chemical and Biomolecular Engineering, University of Notre Dame IN 46556 USA
Adsorption is a fundamental process studied in materials science and engineering because it plays a critical role in various applications, including gas storage and separation. Understanding and predicting gas adsorption within porous materials demands comprehensive computational simulations that are often resource intensive, limiting the identification of promising materials. Active learning (AL) methods offer an effective strategy to reduce the computational burden by selectively acquiring critical data for model training.
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