CREST—A program for the exploration of low-energy molecular chemical space

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(11)

Published: March 21, 2024

Conformer–rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Phys. 22, 7169 (2020)] as driver calculations at extended tight-binding level (xTB), it offers a variety molecular- metadynamics simulations, geometry optimization, structure analysis capabilities. Implemented algorithms include procedures conformational sampling, explicit solvation studies, calculation absolute entropy, identification protonation deprotonation sites. Calculations are set up to run concurrently, providing single-node parallelization. CREST designed require minimal user input comes with implementation GFNn-xTB Hamiltonians GFN-FF force-field. Furthermore, interfaces any quantum chemistry force-field software can easily be created. In this article, we present recent developments code show selection applications most important features program. An novelty refactored backend, which provides significant speed-up small or medium-sized drug molecules allows more sophisticated setups, example, mechanics/molecular mechanics minimum energy crossing point calculations.

Language: Английский

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A focus on delocalization error poisoning the density-functional many-body expansion

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Many-body expansions of quantum chemistry data are used for multiscale modeling, but density functional approximations with less than 50% exact Hartree–Fock exchange can bring significant errors (Broderick et al. , Chem. Sci. 2024, https://doi.org/10.1039/D4SC05955G).

Language: Английский

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From Ab Initio to Instrumentation: A Field Guide to Characterizing Multivalent Liquid Electrolytes

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

In this field guide, we outline empirical and theory-based approaches to characterize the fundamental properties of liquid multivalent-ion battery electrolytes, including (i) structure chemistry, (ii) transport, (iii) electrochemical properties. When detailed molecular-scale understanding multivalent electrolyte behavior is insufficient use examples from well-studied lithium-ion electrolytes. recognition that coupling techniques highly effective, but often nontrivial, also highlight recent characterization efforts uncover a more comprehensive nuanced underlying structures, processes, reactions drive performance system-level behavior. We hope insights these discussions will guide design future studies, accelerate development next-generation batteries through modeling with experiments, help avoid pitfalls ensure reproducibility results.

Language: Английский

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An atom-in-molecule adaptive polarized valence single-ζ atomic orbital basis for electronic structure calculations

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 159(16)

Published: Oct. 25, 2023

Many low-cost or semiempirical quantum mechanical-based electronic structure methods suffer from the use of unpolarized minimal atomic orbital (AO) basis sets. In this work, we overcome limitation by a fully DFT variationally optimized, adaptive set consistently available for elements up to radon (Z = 86). The new key feature is make linear coefficients primitive Gaussians in contracted AO dependent on effective charge atom molecule, i.e., each symmetry-unique obtains its "own" specifically adapted functions. way, physically important "breathing" AOs molecule with (a) (expansion/contraction anionic/cationic states) and (b) number close-lying bonded neighbor atoms accounted for. required charges are obtained specially developed extended Hückel type Hamiltonian coordination numbers geometry. Proper analytical derivatives resulting functions can easily be derived. Moreover, electric field-dependent, thus improving description of, e.g., dipole moments polarizabilities. termed q-vSZP (charge valence single-ζ, polarized) thoroughly benchmarked atomic/molecular thermochemical properties compared standard double-ζ sets at level accurate ωB97X-D4 functional. It shown that clearly superior existing sets, often reaching quality even better results. We expect it optimal choice future mechanical methods.

Language: Английский

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Modern semiempirical electronic structure methods

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(4)

Published: Jan. 24, 2024

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Extended Conductor-like Polarizable Continuum Solvation Model (CPCM-X) for Semiempirical Methods

The Journal of Physical Chemistry A, Journal Year: 2023, Volume and Issue: 127(33), P. 7036 - 7043

Published: Aug. 11, 2023

We have developed a new method to accurately account for solvation effects in semiempirical quantum mechanics based on polarizable continuum model (PCM). The extended conductor-like (CPCM-X) incorporates computationally efficient domain decomposition screening (ddCOSMO) tight binding (xTB) methods and uses post-processing approach established models, like the real solvents (COSMO-RS) universal solvent solute electron density (SMD). According various benchmarks, performs well across broad range of systems applications, including hydration free energies, non-aqueous large supramolecular association reactions neutral charged species. Our computing energies is much more accurate than current xtb program package. It improves accuracy by up 40% larger match even higher-level DFT-based models COSMO-RS SMD while being 2 orders magnitude faster. proposed underlying ddCOSMO are readily available wide variety accessible use computational applications.

Language: Английский

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Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models

The Journal of Physical Chemistry A, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Infrared (IR) spectroscopy is a potent tool for identifying molecular structures and studying the chemical properties of compounds, hence, various theoretical approaches have been developed to simulate predict IR spectra. However, based on quantum calculations suffer from high computational cost (e.g., density functional theory, DFT) or insufficient accuracy semiempirical methods orders magnitude faster than DFT). Here, we introduce new approach, universal machine learning (ML) models AIQM series targeting CCSD(T)/CBS level, that can deliver spectra with close DFT (compared experiment) speed GFN2-xTB method. This approach harmonic oscillator approximation frequency scaling factors fitted experimental data. While benchmarks reported here are focused spectra, our implementation supports anharmonic simulations via dynamics VPT2. These implementations available in MLatom as described https://github.com/dralgroup/mlatom be performed online web browser.

Language: Английский

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Zero-point energies from bond orders and populations relationships

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(8)

Published: Feb. 24, 2025

We report two analytical quantum mechanics (QM) models for approximating appropriately scaled harmonic zero-point energies (ZPEs) without Hessian calculations. Following our earlier bond from orders and populations model that takes a similar form as an extended Hückel but uses well-conditioned orbital populations, this work demonstrates proof of concept ZPEs, important component in thermochemistry calculations, while eschewing unfavorably scaling algorithms involving matrices. The ZPE-BOP1 Mulliken hybrid Kohn–Sham density functional theory calculations within Hückel-type defines vibrational energy terms using atom-pairwise parameters are fit to reproduce ZPEs B3LYP more accurate ZPE-BOP2 Hartree–Fock different includes short-range anharmonic term coupled three-body oscillator with seven parameters. Both predict molecules first row elements, outperforms strained long-chain provides competitive those semi-empirical QM methods (e.g., AM1, PM6, PM7, XTB-2) compute This shows progress outlook toward computational use efficiently useful physicochemical properties. It also opportunities approximate would shift traditional bottlenecks away costly such others focus on reliable populations.

Language: Английский

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dxtb—An efficient and fully differentiable framework for extended tight-binding

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(6)

Published: Aug. 9, 2024

Automatic differentiation (AD) emerged as an integral part of machine learning, accelerating model development by enabling gradient-based optimization without explicit analytical derivatives. Recently, the benefits AD and computing arbitrary-order derivatives with respect to any variable were also recognized in field quantum chemistry. In this work, we present dxtb—an open-source, fully differentiable framework for semiempirical extended tight-binding (xTB) methods. Developed entirely Python leveraging PyTorch array operations, dxtb facilitates extensibility rapid prototyping while maintaining computational efficiency. Through comprehensive code vectorization optimization, essentially reach speed compiled xTB programs high-throughput calculations small molecules. The excellent performance scales large systems, batch operability yields additional execution on parallel hardware. particular, energy evaluations are par existing programs, whereas automatically differentiated nuclear is only 2 5 times slower compared their counterparts. We showcase utility calculating various molecular spectroscopic properties, highlighting its capacity enhance simplify such evaluations. Furthermore, streamlines tasks offers seamless integration chemistry paving way physics-inspired end-to-end models. Ultimately, aims further advance capabilities methods, providing extensible foundation future developments hybrid learning applications. accessible at https://github.com/grimme-lab/dxtb.

Language: Английский

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Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target the computational investigation of materials. Building on previous comprehensive assessments efficient methods infrared (IR) spectroscopy, this study investigates predictive accuracy efficiency gas-phase IR spectra calculations, accessible through combination modern semiempirical quantum mechanical transferable machine learning potentials. A composite approach prediction based double-harmonic approximation, utilizing harmonic vibrational frequencies in squared derivatives dipole moment, employed. This allows methodical flexibility calculation intensities from dipoles corresponding modes. Various are systematically tested to suggest suitable protocol with emphasis efficiency. Among these methods, extended tight-binding (xTB) models, classical charge equilibrium potentials trained moment assessed across diverse data set organic molecules. We particularly focus recently reported foundational potential MACE-OFF23 address limitations conventional low-cost force-field methods. aims establish standard spectra, facilitating rapid reliable identification unknown compounds advancing automated high-throughput analytical workflows chemistry.

Language: Английский

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