Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy

Hu, Wei; Ye, Sheng; Zhang, Yujin; Li, Tianduo; Zhang, Guozhen; Luo, Yi; Mukamel, Shaul; Jiang, Jun

doi:10.1021/acs.jpclett.9b02517

Cited by 81 publications

(74 citation statements)

References 37 publications

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“…After greatly shortening calculation time, large molecular crystals with more than 1000 atoms in a unit cell were calculated. Hu et al used RF method to assist the signal prediction of surface‐enhanced Raman spectroscopy (SERS), which also remedied the drawbacks of ab initio methods in spectroscopy simulations.…”

Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 99%

Machine learning: Accelerating materials development for energy storage and conversion

Chen

Zhou

2020

InfoMat

261

171

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With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery.In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energyrelated fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science. K E Y W O R D Sbig data, energy storage and conversion, machine learning, property prediction

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Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 99%

Machine learning: Accelerating materials development for energy storage and conversion

Chen

Zhou

2020

InfoMat

261

171

View full text Add to dashboard Cite

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“…ML is becoming an indispensable and versatile tool in the chemical sciences [24] with application to molecular properties, [25][26][27][28][29][30][31] spectroscopy, [32][33][34][35][36] and chemical synthesis. [37][38][39][40] Model performance depends concomitantly on the learning algorithm, the quality of the reference training set, and the input representation of the chemical system.…”

Section: Introductionmentioning

confidence: 99%

Machine learning transition temperatures from 2D structure

Sifain

Rice

Yalkowsky

et al. 2021

Journal of Molecular Graphics and Modelling

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A priori knowledge of physicochemical properties such as melting and boiling could expedite materials discovery. However, theoretical modeling from first principles poses a challenge for efficient virtual screening of potential candidates. As an alternative, the tools of data science are becoming increasingly important for exploring chemical datasets and predicting material properties. Herein, we extend a molecular representation, or set of descriptors, first developed for quantitative structure-property relationship modeling by Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER). This molecular representation has group-constitutive and geometrical descriptors that map to enthalpy and entropy; two thermodynamic quantities that drive thermal phase transitions. We extend the UPPER representation to include additional information about sp 2 -bonded fragments. Additionally, instead of using the UPPER descriptors in a series of thermodynamically-inspired calculations, as per Yalkowsky, we use the descriptors to construct a vector representation for use with machine learning techniques. The concise and easy-to-compute representation, combined with a gradient-boosting decision tree model, provides an appealing framework for predicting experimental transition temperatures in a diverse chemical space. An application to energetic materials shows that the method is predictive, despite a relatively modest energetics reference dataset. We also report competitive results on diverse public datasets of melting points (i.e., OCHEM, Enamine, Bradley, and Bergstrom) comprised of over 47k structures. Open source software is available at https://github.com/USArmyResearchLab/ARL-UPPER. File list (2)download file view on ChemRxiv manuscript-bcb.pdf (776.33 KiB) download file view on ChemRxiv si.pdf (671.20 KiB)

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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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Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy

Cited by 81 publications

References 37 publications

Machine learning: Accelerating materials development for energy storage and conversion

Machine learning: Accelerating materials development for energy storage and conversion

Machine learning transition temperatures from 2D structure

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

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