AMOEBA+ Classical Potential for Modeling Molecular Interactions

Liu, Chengwen; Piquemal, Jean‐Philip; Ren, Pengyu

doi:10.1021/acs.jctc.9b00261

Cited by 120 publications

(155 citation statements)

References 157 publications

(311 reference statements)

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“…We expect continued improvement from other methods, particularly multiple efforts to improve classical force fields, [99][100][101][102] inclusion of polarizable atomic charges, [103][104][105][106][107][108][109][110] novel force fields from experimental data, density functional and other quantum methods, [111][112][113][114][115][116] and continued development of approximate semiempirical quantum methods. 37 At present, we can highly recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies.…”

Section: Dipole Moment Rangesmentioning

confidence: 99%

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Folmsbee¹,

Hutchison²

2020

Preprint

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We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.

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Section: Dipole Moment Rangesmentioning

confidence: 99%

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Folmsbee¹,

Hutchison²

2020

Preprint

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“…Future work will focus on the algorithmic boosting of our initial implementation of classical non-polarizable force fields. In addition, the next iteration of the paper will propose more detailed benchmarks for new polarizable approaches, including SIBFA [35,36] and ongoing modifications of AMOEBA such as AMOEBA+ [37] and HIPPO [38,39].…”

Section: Resultsmentioning

confidence: 99%

Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

Jolly

Durán

Lagardère³

et al. 2019

LiveCoMS

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This living paper reviews the present High Performance Computing (HPC) capabilities of the Tinker-HP molecular modeling package. We focus here on the reference, double precision, massively parallel molecular dynamics engine present in Tinker-HP and dedicated to perform large scale simulations. We show how it can be adapted to recent Intel ® Central Processing Unit (CPU) petascale architectures. First, we discuss the new set of Intel ® Advanced Vector Extensions 512 (Intel AVX-512) instructions present in recent Intel processors (e.g., the Intel ® Xeon ® Scalable and Intel ® Xeon Phi ™ 2nd generation processors) allowing for larger vectorization enhancements. These instructions constitute the central source of potential computational gains when using the latest processors, justifying important vectorization efforts for developers. We then briefly review the organization of the Tinker-HP code and identify the computational hotspots which require Intel AVX-512 optimization and we propose a general and optimal strategy to vectorize those particular parts of the code. We present our optimization strategy in a pedagogical way so it can benefit other researchers interested in improving performances of their own software. Finally we compare the performance enhancements obtained to unoptimized code, both sequentially and at the scaling limit in parallel for classical non-polarizable (CHARMM) and polarizable force fields (AMOEBA).

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“…Other simplified variations of AMOEBA water models with the aim at accelerating molecular simulations also appeared, including iAMOEBA and uAMOEBA, in which more experimental properties ( ρ , Δ H vap , C p , κ , α , and dielectric constant ɛ r ) can be well reproduced at different temperatures. By incorporating more physics such as short‐range charge transfer and charge penetration, and modifying the distance dependence behavior of many‐body polarization energy, the AMOEBA+ water model aiming at improving the accuracy and transferability of AMOEBA model has been developed very recently . Additionally, based on the AMOEBA03 water model, the AMOEBA FFs for biomolecules have also been greatly improved in recent years, and most of these developments have been implemented in TINKER simulation software …”

Section: Introductionmentioning

confidence: 99%

“…By incorporating more physics such as short-range charge transfer and charge penetration, and modifying the distance dependence behavior of many-body polarization energy, [35] the AMOEBA+ water model aiming at improving the accuracy and transferability of AMOEBA model has been developed very recently. [36] Additionally, based on the AMOEBA03 water model, the AMOEBA FFs for biomolecules have also been greatly improved in recent years, and most of these developments have been implemented in TINKER simulation software. [37][38][39][40][41] However, the computational efficiency remains a major challenge that limits the wide application of the AMOEBA FFs to simulations of complex biomolecules.…”

Section: Introductionmentioning

confidence: 99%

Three‐site and five‐site fixed‐charge water models compatible with AMOEBA force field

et al. 2020

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In a typical biomolecular simulation using Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field, the vast majority molecules in the simulation box consist of water, and these water molecules consume the most CPU power due to the explicit mutual induction effect. To improve the computational efficiency, we here develop two new nonpolarizable water models (with flexible bonds and fixed charges) that are compatible with AMOEBA solute: the 3‐site AW3C and 5‐site AW5C. To derive the force‐field parameters for AW3C and AW5C, we fit to six experimental liquid thermodynamic properties: liquid density, enthalpy of vaporization, dielectric constant, isobaric heat capacity, isothermal compressibility and thermal expansion coefficient, at a broad range of temperatures from 261.15 to 353.15 K under 1.0 atm pressure. We further validate our AW3C and AW5C water models by showing that they can well reproduce the radial distribution function g(r), self‐diffusion constant D, and hydration free energy from the AMOEBA03 water model and the experimental observations. Furthermore, we show that our AW3C and AW5C water models can greatly accelerate (>5 times) the bulk water as well as biomolecular simulations when compared to AMOEBA water. Specifically, we demonstrate that the applications of AW3C and AW5C water models to simulate a DNA duplex lead to a threefold acceleration, and in the meanwhile well maintain the structural properties as the fully polarizable AMOEBA water. We expect that our AW3C and AW5C water models hold great promise to be widely applied to simulate complex bio‐molecules using the AMOEBA force field.

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AMOEBA+ Classical Potential for Modeling Molecular Interactions

Cited by 120 publications

References 157 publications

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

Three‐site and five‐site fixed‐charge water models compatible with AMOEBA force field

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