Related papers: Standardizing Force Reconstruction in Dynamic Atom…
Atomic Force Microscopy (AFM) combined with electrical modes provides a powerful contactless approach to characterize material electrical properties at the nanoscale. However, conventional electrostatic models often overlook dynamic charge…
The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational chemistry and materials discovery. In this domain, rigorous…
We consider the imaging problem of the reconstruction of a three-dimensional object via optical diffraction tomography under the assumptions of the Born approximation. Our focus lies in the situation that a rigid object performs an…
Sub-nm resolution images can be achieved by Atomic Force Microscopy (AFM) on samples that are deposited on hard substrates. However, it is still extremely challenging to image soft interfaces, such as biological membranes, due to the…
Traction Force Microscopy (TFM) computes the forces exerted at the surface of an elastic material by measuring induced deformations in volume. It is used to determine the pattern of the adhesion forces exerted by cells or by cellular…
Well-ordered stepped semiconductor surfaces attract intense attention owing to the regular arrangements of their atomic steps that makes them perfect templates for the growth of one- dimensional systems, e.g. nanowires. Here, we report on…
Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from…
Amorphous silicon (a-Si) models are analyzed for structural, electronic and vibrational characteristics. Several models of various sizes have been computationally fabricated for this analysis. It is shown that a recently developed…
Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level $ab~initio$ methods, but at a much lower computational cost. On the other hand, classical molecular mechanics force…
We report on progress in developing compact sensors for atomic force microscopy (AFM), in which the mechanical transducer is integrated with near-field optical readout on a single chip. The motion of a nanoscale, doubly-clamped cantilever…
We present the design and experimental results of a near-field scanning microwave microscope (NSMM) working at a frequency of 1GHz. Our microscope is unique in that the sensing probe is separated from the excitation electrode to…
A numerical analysis of the origin of the atomic-scale contrast in Kelvin probe force microscopy (KPFM) is presented. Atomistic simulations of the tip-sample interaction force field have been combined with a non-contact Atomic Force…
The local structure of a protein strongly impacts its function and interactions with other molecules. Therefore, a concise, informative representation of a local protein environment is essential for modeling and designing proteins and…
Several methods have been proposed for large-scale 3D reconstruction from large, unorganized image collections. A large reconstruction problem is typically divided into multiple components which are reconstructed independently using…
Precise manipulation of single molecules has already led to remarkable insights in physics, chemistry, biology and medicine. However, widespread adoption of single-molecule techniques has been impeded by equipment cost and the laborious…
One of the most intriguing results of single molecule experiments on proteins and nucleic acids is the discovery of functional heterogeneity: the observation that complex cellular machines exhibit multiple, biologically active…
A spreadsheet algorithm is given for the atomic force microscope that accounts for non-linear behavior in the deflection of the cantilever and in the photo-diode response. In addition, the data analysis algorithm takes into account…
External biasing forces are often applied to enhance sampling in regions of phase space which would otherwise be rarely observed. While the typical goal of these experiments is to calculate the potential of mean force (PMF) along the…
Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction…
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM)…