Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations

Bleiziffer, Patrick; Schaller, Kay; Riniker, Sereina

doi:10.1021/acs.jcim.7b00663

Cited by 158 publications

(198 citation statements)

References 86 publications

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“…[256] A relatively new approach to charge assignment is Machine Learning. [286][287][288] Also note that the force-match procedure, to be discussed in Subsection 6.7, naturally includes the fit of partial charges.…”

Section: Point Chargesmentioning

confidence: 99%

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Dubbeldam

Walton

Vlugt

et al. 2019

Advcd Theory and Sims

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Molecular simulations are an excellent tool to study adsorption and diffusion in nanoporous materials. Examples of nanoporous materials are zeolites, carbon nanotubes, clays, metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and zeolitic imidazolate frameworks (ZIFs). The molecular confinement these materials offer has been exploited in adsorption and catalysis for almost 50 years. Molecular simulations have provided understanding of the underlying shape selectivity, and adsorption and diffusion effects. Much of the reliability of the modeling predictions depends on the accuracy and transferability of the force field. However, flexibility and the chemical and structural diversity of MOFs add significant challenges for engineering force fields that are able to reproduce experimentally observed structural and dynamic properties. Recent developments in design, parameterization, and implementation of force fields for MOFs and zeolites are reviewed.contain silicon and oxygen. They are readily available, very stable, and relatively cheap. The material should have the right combination of high adsorption selectivity, combined with adequate capacity for use in fixed-bed devices. Recently, new classes of nanoporous materials have been designed that have stability, high void volumes, and well defined tailorable cavities of uniform size. Example are metal-organic frameworks (MOFs), [1][2][3][4][5][6][7] covalent organic frameworks (COFs), [8,9] and zeolitic imidazolate frameworks (ZIFs). [10] These novel materials posses almost unlimited structural variety because of the many combinations of building blocks that can be imagined. The building blocks self-assembly during synthesis into crystalline materials that, after evacuation of the structure, can find applications in adsorption separations, air purification, gas storage, chemical sensing, and catalysis. [4,11] The judicious selection of building blocks allows the pore volume and functionality to be tailored in a rational manner. Because of the designprinciple underlying the synthesis, it is important to understand structure-properties relations, such as the mechanisms of gas separation as a function of shape and size of the pore system, in order to create "blueprints to MOF-design." Computer simulation is cost-effective, fast, and allows for rapid identification of important structural parameters affecting adsorption.Most of the published works on molecular simulation studying zeolites have used the Kiselev model. [12] In this model, zeolite atoms are fixed and interactions of guest atoms with silicon atoms is accounted for by an effective interaction only with the surrounding oxygen atoms. Interactions between sorbate molecules and the host are modeled by placing Lennard-Jones sites and partial charges on all framework atoms and sorbate molecules. The Kiselev model is attractive because of its simplicity and computational efficiency. This model has shown significant success, both in using the molecular dynamics method to compute, for example, the diffu...

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Section: Point Chargesmentioning

confidence: 99%

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Dubbeldam

Walton

Vlugt

et al. 2019

Advcd Theory and Sims

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“…[18][19][20] Applications of ML are becoming increasingly common in experimental and computational chemistry. Recent chemistry related work reports on ML models for chemical reactions 21,22 , potential energy surfaces [23][24][25][26][27] , forces [28][29][30] , atomization energies [31][32][33] , atomic partial charges 32,[34][35][36] , molecular dipoles 26,37,38 , materials discovery [39][40][41] , and protein-ligand complex scoring 42 .…”

Section: Introductionmentioning

confidence: 99%

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Smith¹,

Nebgen²,

Zubatyuk³

et al. 2019

Preprint

135

205

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Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and druglike molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology and chemistry, and billions of times faster than CCSD(T)/CBS calculations.

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“…[18][19][20] Applications of ML are becoming increasingly common in experimental and computational chemistry. Recent chemistry related work reports on ML models for chemical reactions 21,22 , potential energy surfaces [23][24][25][26][27] , forces [28][29][30] , atomization energies 31,32 , atomic partial charges [32][33][34][35] , molecular dipoles 26,36,37 , materials discovery [38][39][40] , and protein-ligand complex scoring 41 . Many of these studies represent important and continued progress toward ML models of quantum chemistry that are transferable (i.e.…”

Section: Main Text: Introductionmentioning

confidence: 99%

Outsmarting Quantum Chemistry Through Transfer Learning

Bt¹,

Zubatyuk²,

Lubbers³

et al. 2018

Preprint

111

178

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<div>Computer simulations are foundational to theoretical chemistry. Quantum-mechanical (QM) methods provide the highest accuracy for simulating molecules but have difficulty scaling to large systems. Empirical interatomic potentials (classical force fields) are scalable, but lack transferability to new systems and are hard to systematically improve. Automated, data-driven machine learning is close to achieving the best of both approaches. Here we use transfer learning to retrain a general purpose neural network potential, ANI-1x, on a dataset of gold standard QM calculations (CCSD(T)/CBS level) that is relatively small but designed to optimally span chemical space. The resulting potential, ANI-1ccx, approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. ANI-1ccx is broadly applicable to materials science, biology and chemistry, and billions of times faster than the parent CCSD(T)/CBS calculations.</div>

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Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations

Cited by 158 publications

References 86 publications

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Outsmarting Quantum Chemistry Through Transfer Learning

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