T. Verstraelen

Accurately Determining the Phase Transition Temperature of CsPbI3 via Random-Phase Approximation Calculations and Phase-Transferable Machine Learning Potentials

T. Braeckevelt, R. Goeminne, S. Vandenhaute, S. Borgmans, T. Verstraelen, J.A. Steele, M. Roeffaers, J. Hofkens, S.M.J. Rogge, V. Van Speybroeck
Chemistry of Materials
34, 19, 8561–8576
2022
A1

Abstract 

While metal halide perovskites (MHPs) have shown great potential for various optoelectronic applications, their widespread adoption in commercial photovoltaic cells or photosensors is currently restricted, given that MHPs such as CsPbI3 and FAPbI3 spontaneously transition to an optically inactive nonperovskite phase at ambient conditions. Herein, we put forward an accurate first-principles procedure to obtain fundamental insight into this phase stability conundrum. To this end, we computationally predict the Helmholtz free energy, composed of the electronic ground state energy and thermal corrections, as this is the fundamental quantity describing the phase stability in polymorphic materials. By adopting the random phase approximation method as a wave function-based method that intrinsically accounts for many-body electron correlation effects as a benchmark for the ground state energy, we validate the performance of different exchange-correlation functionals and dispersion methods. The thermal corrections, accessed through the vibrational density of states, are accessed through molecular dynamics simulations, using a phase-transferable machine learning potential to accurately account for the MHPs’ anharmonicity and mitigate size effects. The here proposed procedure is critically validated on CsPbI3, which is a challenging material as its phase stability changes slowly with varying temperature. We demonstrate that our procedure is essential to reproduce the experimental transition temperature, as choosing an inadequate functional can easily miss the transition temperature by more than 100 K. These results demonstrate that the here validated methodology is ideally suited to understand how factors such as strain engineering, surface functionalization, or compositional engineering could help to phase-stabilize MHPs for targeted applications.

Open Access version available at UGent repository
Gold Open Access

DOI 

https://doi.org/10.1021/acs.chemmater.2c01508

Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach

S. Vandenhaute, M. Cools-Ceuppens, S. DeKeyser, T. Verstraelen, V. Van Speybroeck
npj Computational Materials
9, 1, 19
2023
A1

Abstract 

Computational modeling of physical processes in metal-organic frameworks (MOFs) is highly challenging due to the presence of spatial heterogeneities and complex operating conditions which affect their behavior. Density functional theory (DFT) may describe interatomic interactions at the quantum mechanical level, but is computationally too expensive for systems beyond the nanometer and picosecond range. Herein, we propose an incremental learning scheme to construct accurate and data-efficient machine learning potentials for MOFs. The scheme builds on the power of equivariant neural network potentials in combination with parallelized enhanced sampling and on-the-fly training to simultaneously explore and learn the phase space in an iterative manner. With only a few hundred single-point DFT evaluations per material, accurate and transferable potentials are obtained, even for flexible frameworks with multiple structurally different phases. The incremental learning scheme is universally applicable and may pave the way to model framework materials in larger spatiotemporal windows with higher accuracy.

 

A flexible and scalable implementation of the methodology is available in Psiflow.

Gold Open Access

DOI 

http://dx.doi.org/10.1038/s41524-023-00969-x

Fanpy: A Python Library for Prototyping Multideterminant Methods in Ab Initio Quantum Chemistry

T. D. Kim, M. Richer, G. Sánchez-Díaz, F. Heidar-Zadeh, T. Verstraelen, R.A. Miranda-Quintana, P.W. Ayers
Journal of Computational Chemistry
44, 5, 697-709
2022
A1

Abstract 

Fanpy is a free and open-source Python library for developing and testing multideterminant wavefunctions and related ab initio methods in electronic structure theory. The main use of Fanpy is to quickly prototype new methods by making it easier to transfer the mathematical conception of a new wavefunction ans¨atze to a working implementation. Fanpy uses the framework of our recently introduced Flexible Ansatz for N-electron Configuration Interaction (FANCI), where multideterminant wavefunctions are represented by their overlaps with Slater determinants of orthonormal spin-orbitals. In the simplest case, a new wavefunction ansatz can be implemented by simply writing a function for evaluating its overlap with an arbitrary Slater determinant. Fanpy is modular in both implementation and theory: the wavefunction model, the system’s Hamiltonian, and the choice of objective function are all independent modules. This modular structure makes it easy for users to mix and match different methods and for developers to quickly try new ideas. Fanpy is written purely in Python with standard dependencies, making it accessible for most operating systems; it adheres to principles of modern software development, including comprehensive documentation, extensive testing, and continuous integration and delivery protocols. This article is considered to be the official release notes for the Fanpy library.

DOI 

https://doi.org/10.1002/jcc.27034

GloMPO (Globally Managed Parallel Optimization): a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations

M. Freitas Gustavo, T. Verstraelen
Journal of Cheminformatics
14, 7
2022
A1

Abstract 

In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager.

Gold Open Access

DOI 

http://dx.doi.org/10.1186/s13321-022-00581-z

Zeo-1: A computational data set of zeolite structures

L. Komissarov, T. Verstraelen
Scientific Data
9, 61
2022
A1

Abstract 

Fast, empirical potentials are gaining increased popularity in the computational fields of materials science, physics and chemistry. With it, there is a rising demand for high-quality reference data for the training and validation of such models. In contrast to research that is mainly focused on small organic molecules, this work presents a data set of geometry-optimized bulk phase zeolite structures. Covering a majority of framework types from the Database of Zeolite Structures, this set includes over thirty thousand geometries. Calculated properties include system energies, nuclear gradients and stress tensors at each point, making the data suitable for model development, validation or referencing applications focused on periodic silica systems.

Gold Open Access

DOI 

http://dx.doi.org/10.1038/s41597-022-01160-5

Improving the Silicon Interactions of GFN-xTB

L. Komissarov, T. Verstraelen
Journal of Chemical Information and Modeling (JCIM)
61, 12, 5931–5937
2021
A1

Abstract 

A general-purpose density functional tight binding method, the GFN-xTB model is gaining increased popularity in accurate simulations that are out of scope for conventional ab initio formalisms. We show that in its original GFN1-xTB parametrization, organosilicon compounds are described poorly. This issue is addressed by re-fitting the model’s silicon parameters to a data set of 10 000 reference compounds, geometry-optimized with the revPBE functional. The resulting GFN1(Si)-xTB parametrization shows improved accuracy in the prediction of system energies, nuclear forces, and geometries and should be considered for all applications of the GFN-xTB Hamiltonian to systems that contain silicon.

DOI 

http://dx.doi.org/10.1021/acs.jcim.1c01170

ParAMS: Parameter Optimization for Atomistic and Molecular Simulations

L. Komissarov, R. Rüger, M. Hellström, T. Verstraelen
Journal of Chemical Information and Modeling (JCIM)
61, 8, 3737–3743
2021
A1

Abstract 

This work introduces ParAMS—a versatile Python package that aims to make parametrization workflows in computational chemistry and physics more accessible, transparent, and reproducible. We demonstrate how ParAMS facilitates the parameter optimization for potential energy surface (PES) models, which can otherwise be a tedious specialist task. Because of the package’s modular structure, various functionality can be easily combined to implement a diversity of parameter optimization protocols. For example, the choice of PES model and the parameter optimization algorithm can be selected independently. An illustration of ParAMS’ strengths is provided in two case studies: (i) a density functional-based tight binding (DFTB) repulsive potential for the inorganic ionic crystal ZnO and (ii) a ReaxFF force field for the simulation of organic disulfides.

DOI 

http://dx.doi.org/10.1021/acs.jcim.1c00333

Structure-aided optimization of non-nucleoside M. tuberculosis thymidylate kinase inhibitors

L. Song, R. Merceron, F. Hulpia, A. Lucia, B. Gracia, Y. Jian, M. Risseeuw, T. Verstraelen, P. Cos, J. Ainsa, H. Boshoff, H. Munier-Lehmann, S.N. Savvides, S. Van Calenbergh
European Journal of Medicinal Chemistry
Volume 225, Article Number 113784
2021
A1

Abstract 

Mycobacterium tuberculosis thymidylate kinase (MtTMPK) has emerged as an attractive target for rational drug design. We recently investigated new families of non-nucleoside MtTMPK inhibitors in an effort to diversify MtTMPK inhibitor chemical space. We here report a new series of MtTMPK inhibitors by combining the Topliss scheme with rational drug design approaches, fueled by two co-crystal structures of MtTMPK in complex with developed inhibitors. These efforts furnished the most potent MtTMPK inhibitors in our assay, with two analogues displaying low micromolar MIC values against H37Rv Mtb. Prepared inhibitors address new sub-sites in the MtTMPK nucleotide binding pocket, thereby offering new insights into its druggability. We studied the role of efflux pumps as well as the impact of cell wall permeabilizers for selected compounds to potentially provide an explanation for the lack of correlation between potent enzyme inhibition and whole-cell activity. (C) 2021 Elsevier Masson SAS. All rights reserved.

DOI 

http://dx.doi.org/10.1016/j.ejmech.2021.113784

Super-ions of sodium cations with hydrated hydroxide anions: inorganic structure-directing agents in zeolite synthesis

K. Asselman, N. Pellens, S. Radhakrishnan, C. V. Chandran, J.A. Martens, F. Taulelle, T. Verstraelen, M. Hellstrom, E. Breynaert, C. Kirschhock
Materials Horizons
Volume 8, Issue 9, Pages 2576-2583
2021
A1

Abstract 

In inorganic zeolite formation, a direct correspondence between liquid state species in the synthesis and the supramolecular decoration of the pores in the as-made final zeolite has never been reported. In this paper, a direct link between the sodium speciation in the synthesis mixture and the pore structure and content of the final zeolite is demonstrated in the example of hydroxysodalite. Super-ions with 4 sodium cations bound by mono- and bihydrated hydroxide are identified as structure-directing agents for the formation of this zeolite. This documentation of inorganic solution species acting as a templating agent in zeolite formation opens new horizons for zeolite synthesis by design.

DOI 

http://dx.doi.org/10.1039/d1mh00733e

Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential

M. Cools-Ceuppens, J. Dambre, T. Verstraelen
Journal of Chemical Theory and Computation (JCTC)
18 (3), 1672–1691
2022
A1

Abstract 

Explicit-electron force fields introduce electrons or electron pairs as semiclassical particles in force fields or empirical potentials, which are suitable for molecular dynamics simulations. Even though semiclassical electrons are a drastic simplification compared to a quantum-mechanical electronic wave function, they still retain a relatively detailed electronic model compared to conventional polarizable and reactive force fields. The ability of explicit-electron models to describe chemical reactions and electronic response properties has already been demonstrated, yet the description of short-range interactions for a broad range of chemical systems remains challenging. In this work, we present the electron machine learning potential (eMLP), a new explicit electron force field in which the short-range interactions are modeled with machine learning. The electron pair particles will be located at well-defined positions, derived from localized molecular orbitals or Wannier centers, naturally imposing the correct dielectric and piezoelectric behavior of the system. The eMLP is benchmarked on two newly constructed data sets: eQM7, an extension of the QM7 data set for small molecules, and a data set for the crystalline β-glycine. It is shown that the eMLP can predict dipole moments, polarizabilities, and IR-spectra of unseen molecules with high precision. Furthermore, a variety of response properties, for example, stiffness or piezoelectric constants, can be accurately reproduced.

DOI 

https://doi.org/10.1021/acs.jctc.1c00978

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