LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential

Barker, James; Bulin, Johannes; Hamaekers, Jan; Mathias, Sonja

doi:10.1007/978-3-319-62458-7_2

Cited by 24 publications

(23 citation statements)

References 18 publications

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“…by defining a distance metric. Several methods have been proposed over the past decade to describe structures, primarily for predicting atomic scale properties using machine learning [17][18][19][20][21][22][23][24] . They all respect the appropriate physical symmetries; many are based on atomic densities, and these are essentially equivalent in some limit, differing only in the basis onto which the density is projected 25 .…”

Section: Essential Concepts and Methods For Mapping Atomic Structure Low-dimensional Embeddingsmentioning

confidence: 99%

Mapping Materials and Molecules

et al. 2020

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The visualization of data is indispensable in scientific research, from the early stages when human insight forms, to the final step of communicating results. In computational physics, 1 chemistry and materials science, it can be as simple as making a scatter plot, or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bio-activities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural datasets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool, ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large datasets, but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science 2 has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of datasets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.

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Section: Essential Concepts and Methods For Mapping Atomic Structure Low-dimensional Embeddingsmentioning

confidence: 99%

Mapping Materials and Molecules

et al. 2020

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“…Table 4 gives a coarse characterization of popular representations. 275 , 410 , 411 , 414 , 416 , 417 , 452 , 453 To create this overview, we had to adopt a reductionist perspective, which inevitably hides the complexities involved in developing robust atomistic representations. Whether a representation satisfies a particular property can sometimes not be answered unequivocally.…”

Section: Applications Of Machine Learning To Chemical Systemsmentioning

confidence: 99%

“…Most atomic descriptors use length-scale hyperparameters specifically chosen for a given problem and system. 275 , 410 , 411 , 414 , 416 , 417 , 452 , 453 There are several ways to automate hyperparameter selections. Ref ( 373 ) introduced general heuristics for choosing the SOAP hyperparameters for a system with arbitrary chemical composition based on characteristic bond lengths.…”

Section: Applications Of Machine Learning To Chemical Systemsmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

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Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

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“…13,14 Atomic variants of the CM have also been proposed and tested on QM9. 15 Other representations have also been benchmarked on QM9 (or QM7 which is a smaller but similar data set), such as Fourier series of radial distance distributions, 16 motifs, 17 the smooth overlap of atomic positions (SOAP) 18 in combination with regularized entropy match, 19 constant size descriptors based on connectivity and encoded distance distributions. 20 Ramakrishnan et al 8 introduced a ∆-ML approach, where the difference between properties calculated at coarse/accurate quantum level of theories is being modeled.…”

Section: Introductionmentioning

confidence: 99%

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

Faber

Hutchison

Huang

et al. 2017

J. Chem. Theory Comput.

576

648

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We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of 13 electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out-of-sample errors as a function of training set size with up to ∼118k distinct molecules. Molecular structures and properties at the hybrid density functional theory (DFT) level of theory come from the QM9 database [ Ramakrishnan et al. Sci. Data 2014 , 1 , 140022 ] and include enthalpies and free energies of atomization, HOMO/LUMO energies and gap, dipole moment, polarizability, zero point vibrational energy, heat capacity, and the highest fundamental vibrational frequency. Various molecular representations have been studied (Coulomb matrix, bag of bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed distribution based variants including histograms of distances (HD), angles (HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian ridge regression (BR) and linear regression with elastic net regularization (EN)), random forest (RF), kernel ridge regression (KRR), and two types of neural networks, graph convolutions (GC) and gated graph networks (GG). Out-of sample errors are strongly dependent on the choice of representation and regressor and molecular property. Electronic properties are typically best accounted for by MG and GC, while energetic properties are better described by HDAD and KRR. The specific combinations with the lowest out-of-sample errors in the ∼118k training set size limit are (free) energies and enthalpies of atomization (HDAD/KRR), HOMO/LUMO eigenvalue and gap (MG/GC), dipole moment (MG/GC), static polarizability (MG/GG), zero point vibrational energy (HDAD/KRR), heat capacity at room temperature (HDAD/KRR), and highest fundamental vibrational frequency (BAML/RF). We present numerical evidence that ML model predictions deviate from DFT (B3LYP) less than DFT (B3LYP) deviates from experiment for all properties. Furthermore, out-of-sample prediction errors with respect to hybrid DFT reference are on par with, or close to, chemical accuracy. The results suggest that ML models could be more accurate than hybrid DFT if explicitly electron correlated quantum (or experimental) data were available.

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LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential

Cited by 24 publications

References 18 publications

Mapping Materials and Molecules

Mapping Materials and Molecules

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

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