Machine learning exciton dynamics

Häse, Florian; Valleau, Stéphanie; Pyzer‐Knapp, Edward O.; Aspuru‐Guzik, Alán

doi:10.1039/c5sc04786b

Cited by 139 publications

(132 citation statements)

References 69 publications

(121 reference statements)

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“…Among those, many authors point out the requirement that descriptors should be invariant with respect to translations and rotations of atomic positions, as well as reordering of atomic indices. Popular descriptors with these properties can be categorized into few cases: structural data such as Coulomb matrices [248,457,470], molecular strings or graphs [264,455,457], and polymer fingerprinting [457][458][459]; simple atomic properties of the constituent species [460,462,466], and DFT-derived data, such as PBE/LDAlevel bandgaps and hybrid-level electronic density [206,272,452,456,461,471,472]. Frequently a combination of two or more classes of descriptors [453,457,460,473] as well as experimental data as features [272,465] is found in the literature.…”

Section: Electronic Propertiesmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Electronic Propertiesmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…Recently Häse et al proposed a machine-learning technique based on the Coulomb matrix to compute the excitation energies and spectral densities of BChls in the FMO complex. 48 …”

Section: Introductionmentioning

confidence: 99%

Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks

Shen

Yang

2016

J. Chem. Theory Comput.

123

143

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Molecular dynamics simulation with multiscale quantum mechanics/molecular mechanics (QM/MM) methods is a very powerful tool for understanding the mechanism of chemical and biological processes in solution or enzymes. However, its computational cost can be too high for many biochemical systems because of the large number of ab initio QM calculations. Semiempirical QM/MM simulations have much higher efficiency. Its accuracy can be improved with a correction to reach the ab initio QM/MM level. The computational cost on the ab initio calculation for the correction determines the efficiency. In this paper we developed a neural network method for QM/MM calculation as an extension of the neural-network representation reported by Behler and Parrinello. With this approach, the potential energy of any configuration along the reaction path for a given QM/MM system can be predicted at the ab initio QM/MM level based on the semiempirical QM/MM simulations. We further applied this method to three reactions in water to calculate the free energy changes. The free-energy profile obtained from the semiempirical QM/MM simulation is corrected to the ab initio QM/MM level with the potential energies predicted with the constructed neural network. The results are in excellent accordance with the reference data that are obtained from the ab initio QM/MM molecular dynamics simulation or corrected with direct ab initio QM/MM potential energies. Compared with the correction using direct ab initio QM/MM potential energies, our method shows a speed-up of 1 or 2 orders of magnitude. It demonstrates that the neural network method combined with the semiempirical QM/MM calculation can be an efficient and reliable strategy for chemical reaction simulations.

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“…70 , where a ML approach was used to predict the electrical conductance of disordered one-dimensional channels, and Refs. 65,71 , in which excitation energy dynamics and transfer times in light harvesting systems were predicted with ML tools by considering a large dataset of model Hamiltonians.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Machine Learning Prediction of DNA Charge Transport

Korol¹,

Segal²

2019

J. Phys. Chem. C

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First principle calculations of charge transfer in DNA molecules are computationally expensive given that charge carriers migrate in interaction with intra-and inter-molecular atomic motion. Screening sequences, e.g. to identify excellent electrical conductors is challenging even when adopting coarse-grained models and effective computational schemes that do not explicitly describe atomic dynamics. In this work, we present a machine learning (ML) model that allows the inexpensive prediction of the electrical conductance of millions of long double-stranded DNA (dsDNA) sequences, reducing computational costs by orders of magnitude. The algorithm is trained on short DNA nanojunctions with n = 3 − 7 base pairs. The electrical conductance of the training set is computed with a quantum scattering method, which captures charge-nuclei scattering processes. We demonstrate that the ML method accurately predicts the electrical conductance of varied dsDNA junctions tracing different transport mechanisms: coherent (short-range) quantum tunneling, onresonance (ballistic) transport, and incoherent site-to-site hopping. Furthermore, the ML approach supports physical observations that clusters of nucleotides regulate DNA transport behavior. The input features tested in this work could be used in other ML studies of charge transport in complex polymers, in the search for promising electronic and thermoelectric materials.

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Machine learning exciton dynamics

Abstract: Machine learning ground state QM/MM for accelerated computation of exciton dynamics.

Cited by 139 publications

References 69 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks

Machine Learning Prediction of DNA Charge Transport

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