Mapping Materials and Molecules

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, 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 bioactivities. It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. 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 data sets 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 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 data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Mapping Materials and Molecules.

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, 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 bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. 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 data sets 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 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 data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Localization and potential role of matrix metalloproteinase-1 and tissue inhibitors of metalloproteinase-1 and -2 in different phases of bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) can evolve in prematurely born infants who require mechanical ventilation because of hyaline membrane disease (HMD). The development of BPD can be divided in an acute, a regenerative, a transitional, and a chronic phase. During these different phases, extensive remodeling of the lung parenchyma with re-epithelialization of the alveoli and formation of fibrosis occurs. Matrix metalloproteinase-1 (MMP-1) is an enzyme that is involved in re-epithelialization processes, and dysregulation of MMP-1 activity contributes to fibrosis. Localization of MMP-1 and its inhibitors, tissue inhibitor of metalloproteinase (TIMP)-1 and TIMP-2, were investigated in lung tissue obtained from infants who died during different phases of BPD development. In all studied cases (n = 50) type-II pneumocytes were found to be immunoreactive for MMP-1, TIMP-1, and TIMP-2. During the acute and regenerative phase of BPD, type-II pneumocytes re-epithelialize the injured alveoli. This may suggest that MMP-1 and its inhibitors, expressed by type-II pneumocytes, play a role in the re-epithelialization process after acute lung injury. Although MMP-1 staining intensity remained constant in type-II pneumocytes during BPD development, TIMP-1 increased during the chronic fibrotic phase. This relative elevation of TIMP-1 compared with MMP-1 is indicative for reduced collagenolytic activity by type-II pneumocytes in chronic BPD and may contribute to fibrosis. Fibrotic foci in chronic BPD contained fibroblasts immunoreactive for MMP-1 and TIMP-1 and -2. This may indicate that decreased collagen turnover by fibroblasts contributes to fibrosis in BPD development

Cell death during sepsis: integration of disintegration in the inflammatory response to overwhelming infection

Sepsis is a major health problem and a leading cause of death worldwide. In recent years, a crescendo of attention has been directed to the mechanisms of cell death that develop during this disease, since these are viewed as important contributors to the proinflammatory and anti-inflammatory responses associated with poor outcome. Here we discuss mechanisms of cell death evident severe bacterial infection and sepsis including necrosis, apoptosis, pyroptosis, and extracellular trap-associated neutrophil death, with a particular emphasis on lymphocyte apoptosis and its contribution to the immunosuppressed phenotype of late sepsis. Individual bacterial pathogens express virulence factors that modulate cell death pathways and influence the sepsis phenotype. A greater knowledge of cell death pathways in sepsis informs the potential for future therapies designed to ameliorate immune dysfunction in this syndrome

Machine learning in chemical reaction space.

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for 'reactive' ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses

Data-efficient machine learning for molecular crystal structure prediction.

The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level-for which an efficient implementation is available-with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP

Kernel charge equilibration: Efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model

Abstract State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel charge equilibration (kQEq). This model is based on classical charge equilibration (QEq) models expanded with an environment-dependent electronegativity. In contrast to previously reported neural network models with a similar concept, kQEq takes advantage of the linearity of both QEq and Kernel Ridge Regression to obtain a closed-form linear algebra expression for training the models. Furthermore, we avoid the ambiguity of charge partitioning schemes by using dipole moments as reference data. As a first application, we show that kQEq can be used to generate accurate and highly data-efficient models for molecular dipole moments.</jats:p