Force Field Development and Nanoreactor Chemistry

Wang, Lee‐Ping

doi:10.1007/978-3-030-05600-1_6

Cited by 2 publications

(2 citation statements)

References 109 publications

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“…Wang and co-workers developed an elegant approach for the MD-based exploration of reaction pathways in the condensed phase, using QM methods, referred to as the ab initio nanoreactor (NR) 44,45 . The NR was designed to model high-velocity molecular collisions of small molecules by using a fictitious biasing force to promote chemical reactions and the formation of new molecules, thus automatically exploring reaction pathways between arbitrary reactants and products.…”

Section: Articlementioning

confidence: 99%

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Zhang,

Makoś,

Jadrich

et al. 2024

Nat. Chem.

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Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.

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Section: Articlementioning

confidence: 99%

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Zhang,

Makoś,

Jadrich

et al. 2024

Nat. Chem.

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show abstract

“…Wang and co-workers developed an elegant but expensive approach for the MD-based exploration of reaction pathways 2/18 in the condensed phase, referred to as the ab initio nanoreactor (NR). 39,40 The NR was designed to model high-velocity molecular collisions of small molecules by using a fictitious biasing force to promote chemical reactions and the formation of new molecules, thus automatically exploring reaction pathways between arbitrary reactants and products. The NR took an intermediate stance between physically-realistic simulation and rule-based enumeration approaches.…”

Section: Introductionmentioning

confidence: 99%

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Messerly

Zhang

Jadrich

et al. 2023

Preprint

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Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, developing reactive MLIPs requires prior knowledge of reaction networks to generate fitting data and refitting to extensive datasets for each new application. For this reason, many fields of chemistry would greatly benefit from a general reactive MLIP, i.e., an MLIP that is applicable to a broad range of reactive chemistry such that it can be applied to new systems without the need for retraining. In this work, we develop a general reactive MLIP through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting potential (ANI-1xnr) is then applied to study five distinct condensed-phase reactive chemistry systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early-earth small molecules. In all studies, ANI-1xnr closely matches experiment and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N, and O elements that does not need to be refit for each application, enabling high-throughput in silico reactive chemistry experimentation.

show abstract

Force Field Development and Nanoreactor Chemistry

Cited by 2 publications

References 109 publications

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

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