Exchange Spin Coupling from Gaussian Process Regression

Bahlke, Marc Philipp; Mogos, Natnael; Proppe, Jonny; Herrmann, Carmen

doi:10.1021/acs.jpca.0c05983

Cited by 21 publications

(19 citation statements)

References 169 publications

(315 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gaussian process regression makes use of the "kernel trick" 32 to flexibly interpolate data for a wealth of applications. There has been a large amount of recent work using GPR for molecular and materials chemistry 33 including the use of GPR to predict molecular crystal energies, 34,35 MP2 and CCSD energies, 36 potential energy surfaces, 37 exchange spin coupling, 38 and for autonomous materials discovery. 39 Recently, GPR was used in a finite size correction for the coupled cluster and auxiliary field quantum Monte Carlo energies of onedimensional hydrogen chains.…”

Section: Gpr Protocolmentioning

confidence: 99%

Machine learning for a finite size correction in periodic coupled cluster theory calculations

Weiler,

Mihm,

Shepherd

2022

Preprint

View full text Add to dashboard Cite

We introduce a straightforward Gaussian process regression (GPR) model for the transition structure factor of metal periodic coupled cluster singles and doubles (CCSD) calculations. This is inspired by the method introduced by Liao and Grüneis for interpolating over the transition structure factor to obtain a finite size correction for CCSD [J. Chem. Phys. 145, 141102 (2016)], and by our own prior work using the transition structure factor to efficiently converge CCSD for metals to the thermodynamic limit [Nat. Comput. Sci. 1, 801 (2021)]. In our CCSD-FS-GPR method to correct for finite size errors, we fit the structure factor to a 1D function in the momentum transfer, G. We then integrate over this function by projecting it onto a k-point mesh to obtain comparisons with extrapolated results. Results are shown for lithium, sodium, and the uniform electron gas.

show abstract

Section: Gpr Protocolmentioning

confidence: 99%

Machine learning for a finite size correction in periodic coupled cluster theory calculations

Weiler,

Mihm,

Shepherd

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To explore the potential of UQ for chemical research, we build upon previous work by Proppe and colleagues, [15,16] addressing Mössbauer spectroscopy, [9,17] dispersion corrections to density functional theory, [18,19] reaction kinetics, [20,21] acid‐base equilibria, [22] and exchange spin coupling [23] . This foundation will support our endeavor to pave the way for a novel approach to determining reactivity parameters with steadily increasing accuracy.…”

Section: Introductionmentioning

confidence: 97%

Uncertainty Quantification of Reactivity Scales**

Proppe

Kircher

2022

ChemPhysChem

Self Cite

View full text Add to dashboard Cite

According to Mayr, polar organic synthesis can be rationalized by a simple empirical relationship linking bimolecular rate constants to as few as three reactivity parameters. Here, we propose an extension to Mayr's reactivity method that is rooted in uncertainty quantification and transforms the reactivity parameters into probability distributions. Through uncertainty propagation, these distributions can be transformed into uncertainty estimates for bimolecular rate constants. Chemists can exploit these virtual error bars to enhance synthesis planning and to decrease the ambiguity of conclusions drawn from experimental data. We demonstrate the above at the example of the reference data set released by Mayr and co‐workers [J. Am. Chem. Soc. 2001, 123, 9500; J. Am. Chem. Soc. 2012, 134, 13902]. As by‐product of the new approach, we obtain revised reactivity parameters for 36 π‐nucleophiles and 32 benzhydrylium ions.

show abstract

“…In recent decades, models based on artificial intelligence techniques have performed impressive predictions in different knowledge fields. For example, in exact sciences (Sauceda et al, 2021;Hajibabaei and Kim, 2021;Cerioti et al, 2021;Bahlke et al, 2020;Chmiela et al, 2020;Saar et al, 2021;Artrith and Urban, 2016;Ch'ng et al, 2017;Shallue and Vanderburg, 2018;Sadowski et al, 2016), in social sciences (Ng et al, 2020;Lattner and Grachten, 2019), in technology (Bae et al, 2021;Cunneen et al, 2019;Feldt et al, 2018;Huang et al, 2014), and health sciences (Bashyam et al, 2020;Zhou et al, 2019;Themistocleous et al, 2021;Lagree et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Bokhimi

2021

Front. Chem. Eng.

View full text Add to dashboard Cite

We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

show abstract

Exchange Spin Coupling from Gaussian Process Regression

Cited by 21 publications

References 169 publications

Machine learning for a finite size correction in periodic coupled cluster theory calculations

Machine learning for a finite size correction in periodic coupled cluster theory calculations

Uncertainty Quantification of Reactivity Scales**

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Contact Info

Product

Resources

About