A neural network protocol for electronic excitations of
            <i>N</i>
            -methylacetamide

Ye, Sheng; Hu, Wei; Li, Xin; Zhang, Jinxiao; Zhong, Kai; Zhang, Guozhen; Luo, Yi; Mukamel, Shaul; Jiang, Jun

doi:10.1073/pnas.1821044116

Cited by 68 publications

(98 citation statements)

References 22 publications

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“…Nevertheless, this approach is much more general. It can be used for other types of NEA spectra (like emission, 7 two-dimensional, 61 differential transmission, 62 photoelectron, 63 ultrafast Auger, 64 and X-ray photoscattering 65 spectroscopies), with any ensemble distribution (e.g., from molecular dynamics 20 ) and any QC method able to compute excitation energies and transition moments.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Absorption Cross Sections

2020

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We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties—excitation energies and oscillator strengths—are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points.

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Section: Discussionmentioning

confidence: 99%

Machine Learning for Absorption Cross Sections

2020

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“…紫外吸收被广泛用于表征蛋白质结构, 但是对蛋白结构的紫外光谱预测依赖于复杂的理论计算, 计算成本高、速度慢. 江俊等 [59] 以 N-甲基乙酰胺(NMA)作为蛋白多肽的模型化合物. 首先利用分子动力学模拟, 在 200 K, 300 K 和 400 K 下产生出 70000 个 NMA 的构象.…”

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“…另一机器学习运用于理论化学计算的方式是将机器学习嵌入理论计算的方法本身, 以代替理论计算中某些复杂、计算量大而耗时的步骤, 提高计算效率 [60,61] . 例如, Schütt 等 [58] 图 20 建立 UV 光谱与蛋白质原子结构之间的对应关系 [59] . 用随机森林筛选出最重要的 nπ*跃迁能量描述符.…”

Section: 图 19 基于 Svm 的决策树结构unclassified

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Artificial Intelligence for Contemporary Chemistry Research

Zhu¹,

Wu²,

Yu³

2020

Acta Chim. Sinica

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“…In recent years, many efforts have been directed to the efficient improvement of force fields. In particular, machine learning combined with molecular simulation has been verified by many groups to be effective to develop force field including inferring charges based on a set of reference molecules (Botu et al, 2016;Chen et al, 2018;Inokuchi et al, 2018;Engler et al, 2019;Hu et al, 2019;Roman et al, 2019;Sanvito, 2019;Unke and Meuwly, 2019;Ye et al, 2019). Among these, the random forest regression (RFR) method has been proven to be feasible for the prediction of atomic charge without expending much effort on parameter tuning or descriptor selection.…”

Section: Introductionmentioning

confidence: 99%

Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression

Zhao

Huang

et al. 2020

Front. Chem.

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The random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplified heme-oxygen binding model was built from the non-adiabatic dynamic simulation trajectories. The ESP charges of each atom were calculated and used as the real-valued response. The conformational adapted charge model (CAC) of three spin states was constructed by an RFR model using symmetry functions. The results show that our RFR model can effectively predict the on the fly atomic charges with the varying conformations as well as the atomic charge of different spin states in the same conformation, thus achieving the balance of accuracy and efficiency. The average mean absolute error of the predicted charges of each spin state is <0.02 e. The comparison studies on descriptors showed a maximum 0.06 e improvement in prediction of the charge of Fe 2+ by using 11 manually selected structural parameters. We hope that this model can not only provide variable parameters for developing the force field of the multi-spin state but also facilitate automation, thus enabling large-scale simulations of atomistic systems.

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A neural network protocol for electronic excitations of N -methylacetamide

Abstract: This article contains supporting information online at www.pnas.org/lookup/suppl/

Cited by 68 publications

References 22 publications

Machine Learning for Absorption Cross Sections

Machine Learning for Absorption Cross Sections

Artificial Intelligence for Contemporary Chemistry Research

Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression

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