Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

Huang, Bing; Lilienfeld, O. Anatole von

doi:10.1063/1.4964627

Cited by 307 publications

(355 citation statements)

References 31 publications

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“…[40][41][42][43] ML provides a method to identify regularities and correlations in huge datasets, which yield new scientific insights without the need for any prior particular physical knowledge. [44,45,47] Typically, ML algorithms are trained using data obtained from ab initio calculations. [44,45,47] Typically, ML algorithms are trained using data obtained from ab initio calculations.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning–Based Charge Transport Computation for Pentacene

Lederer

Kaiser

Mattoni

et al. 2018

Advcd Theory and Sims

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Insight into the relation between morphology and transport properties of organic semiconductors can be gained using multiscale simulations. Since computing electronic properties, such as the intermolecular transfer integral, using quantum chemical (QC) methods requires a high computational cost, existing models assume several approximations. A machine learning (ML)-based multiscale approach is presented that allows to simulate charge transport in organic semiconductors considering the static disorder within disordered crystals. By mapping fingerprints of dimers to their respective transfer integral, a kernel ridge regression ML algorithm for the prediction of charge transfer integrals is trained and evaluated. Since QC calculations of the electronic structure must be performed only once, the use of ML reduces the computation time radically, while maintaining the prediction error small. Transfer integrals predicted by ML are utilized for the computation of charge carrier mobilities using off-lattice kinetic Monte Carlo (kMC) simulations. Benefiting from the rapid performance of ML, microscopic processes can be described accurately without the need for phenomenological approximations. The multiscale system is tested with the well-known molecular semiconductor pentacene. The presented methodology allows reproducing the experimentally observed anisotropy of the mobility and enables a fast estimation of the impact of disorder.However, to this day, the main issue concerning organic semiconductors is the low charge carrier mobility compared to their inorganic counterpart, [5] which limits the operational speed and performance of electronic devices. Largest measured mobilities are in the range of 10 cm 2 V −1 s −1 for highly crystalline pentacene [6] and rubrene. [7] Due to the lack of insight into the structureproperties relationships, the design of new materials often relies on chemical intuition. This makes it difficult to identify promising materials with enhanced mobility. Thus, theoretical and numerical models are considered as promising pathways to increase the understanding of the relation between charge transport properties and structural morphologies within organic materials at the nanoscale. [8] Various techniques ranging from analytic approaches [9][10][11][12][13][14] to simulation methods such as molecular dynamics (MD) [15][16][17] and kinetic Monte Carlo (kMC) [18][19][20][21][22][23] are utilized to model the impact of molecular structures on transport properties such as the charge carrier mobility. The above approaches consider different length scales: analytic models provide an empirical picture of charge transport in disordered organic materials at the continuum scale, but they do not account for the different molecular structures; MD simulations yield insight into microscopic properties on an atomistic scale (≤ 1 nm), however it is not feasible to obtain mesoscopic transport properties due to the high computational cost; kMC allows to bridge different length scales by implicitly linking structural an...

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Section: Introductionmentioning

confidence: 99%

Machine Learning–Based Charge Transport Computation for Pentacene

Lederer

Kaiser

Mattoni

et al. 2018

Advcd Theory and Sims

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“…Empirically, learning curves are known to have a AN −β train dependence. 70 In the training of neural networks typically 1 < β < 2, 71 while we found values between 0.15 − 0.30 for the properties studied. Since different types of models can have different learning curves, we also looked at random forest, where we found similar plots with β ≈ 0.20.…”

Section: Residual Analysismentioning

confidence: 65%

Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

Elton¹,

Boukouvalas²,

Butrico³

et al. 2018

Preprint

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We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods -sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈ 300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

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“…Figure 4 shows the learning curves, constructed from δ RMS , for the baseline ML force fields (i.e., those of classes (I) and (II), which are based on random data sampling) created for Al, Cu, Ti, W, Si, and C using the optimized V i,α and V ′ i;α . For both fingerprints, δ RMS scales inversely with N t initially 39 but reaches a limit at N t ≳ 500. By using V i,α instead of V ′ i;α , δ RMS drops from ≃0.16 eV/Å to ≃0.09 eV/ Å in case of C. Significant improvement was also obtained for W, Ti, and Cu.…”

Section: Fingerprintingmentioning

confidence: 97%

A universal strategy for the creation of machine learning-based atomistic force fields

et al. 2017

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Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

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Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

Cited by 307 publications

References 31 publications

Machine Learning–Based Charge Transport Computation for Pentacene

Machine Learning–Based Charge Transport Computation for Pentacene

Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

A universal strategy for the creation of machine learning-based atomistic force fields

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