Estimating wear on bottom hole assembly (BHA) components utilizes a rock hardness index using analysis of drill cutting. Estimating the amount of wear on borehole assembly components comprises measuring the rock properties in drilled cuttings from a borehole. A hardness value is assigned to each mineral present in the drilled cuttings. A hardness index is calculated for a drilled borehole interval. A wear resistance factor is assigned to each BHA component of the BHA. The wear resistance factor depends on the wear resistance of each BHA component. A wear value for each BHA component is calculated based on the hardness index for the drilled borehole interval, the wear resistance of the BHA component, and drilling parameters.

Estimating wear on bottom hole assembly (BHA) components utilizes a rock hardness index using analysis of drill cutting. Estimating the amount of wear on borehole assembly components comprises measuring the rock properties in drilled cuttings from a borehole. A hardness value is assigned to each mineral present in the drilled cuttings. A hardness index is calculated for a drilled borehole interval. A wear resistance factor is assigned to each BHA component of the BHA. The wear resistance factor depends on the wear resistance of each BHA component. A wear value for each BHA component is calculated based on the hardness index for the drilled borehole interval, the wear resistance of the BHA component, and drilling parameters.

A material screening process of generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs. Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials includes the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening.

A device and method for measuring angles of orientation of an x-ray imaging system including an x-ray source, an x-ray detector and a sample holder arranged to receive a sample to be analysed. The method includes: emitting a polychromatic x-ray beam through a reference sample installed on the sample holder in order to form a diffraction pattern on the sensing area of the x-ray detector, generating, with the x-ray detector, an observed image comprising the diffraction pattern, and determining the orientation of the x-ray source and the orientation of the x-ray detector by comparing the observed image with at least one target image comprising a diffraction pattern obtained for the reference sample with preset orientations of the x-ray source and of the x-ray detector.

A device and method for measuring angles of orientation of an x-ray imaging system including an x-ray source, an x-ray detector and a sample holder arranged to receive a sample to be analysed. The method includes: emitting a polychromatic x-ray beam through a reference sample installed on the sample holder in order to form a diffraction pattern on the sensing area of the x-ray detector, generating, with the x-ray detector, an observed image comprising the diffraction pattern, and determining the orientation of the x-ray source and the orientation of the x-ray detector by comparing the observed image with at least one target image comprising a diffraction pattern obtained for the reference sample with preset orientations of the x-ray source and of the x-ray detector.

A measurement device includes an analyzer configured to analyze a diffraction image of X-rays scattered from a subject; estimate a surface contour shape of a measurement area of the subject; extract feature data from shape information, and determine shape parameters for representing the surface contour shape; calculate a theoretical scattering intensity of each of the scattered X-rays when values of the shape parameters are changed; calculate a difference between a measured scattering intensity of each scattered X-ray and the corresponding theoretical scattering intensity, and generate a regression model of a relationship between a corresponding value of the shape parameter and the difference for each shape parameter; extract one shape parameter candidate value reducing the difference from the regression model, and calculate a theoretical scattering intensity of the shape parameter candidate value; and estimate the value of the shape parameter minimizing the difference while repeatedly changing the shape parameter candidate value.

A measurement device includes an analyzer configured to analyze a diffraction image of X-rays scattered from a subject; estimate a surface contour shape of a measurement area of the subject; extract feature data from shape information, and determine shape parameters for representing the surface contour shape; calculate a theoretical scattering intensity of each of the scattered X-rays when values of the shape parameters are changed; calculate a difference between a measured scattering intensity of each scattered X-ray and the corresponding theoretical scattering intensity, and generate a regression model of a relationship between a corresponding value of the shape parameter and the difference for each shape parameter; extract one shape parameter candidate value reducing the difference from the regression model, and calculate a theoretical scattering intensity of the shape parameter candidate value; and estimate the value of the shape parameter minimizing the difference while repeatedly changing the shape parameter candidate value.

Methods of introducing a small molecule into a crystal of a macromolecule, of obtaining a microcrystal having a macromolecule and a small molecule from a crystal of the macromolecule, of determining a structural model for a complex having a macromolecule and a small molecule, of identifying a small molecule that complexes with a macromolecule, and of screening a library of small molecules for their binding to a macromolecule are disclosed.

Methods of introducing a small molecule into a crystal of a macromolecule, of obtaining a microcrystal having a macromolecule and a small molecule from a crystal of the macromolecule, of determining a structural model for a complex having a macromolecule and a small molecule, of identifying a small molecule that complexes with a macromolecule, and of screening a library of small molecules for their binding to a macromolecule are disclosed.

An apparatus for inspecting a semiconductor device according to an embodiment includes an X-ray irradiation unit configured to make monochromatic X-rays obliquely incident on the semiconductor device, which is an object at a predetermined angle of incidence, a detection unit configured to detect observed X-rays observed from the object using a plurality of two-dimensionally disposed photodetection elements, an analysis apparatus configured to generate X-ray diffraction images obtained by photoelectrically converting the observed X-rays, and a control unit configured to change an angle of incidence and a detection angle of the X-rays, in which the analysis apparatus acquires an X-ray diffraction image every time the angle of incidence is changed, extracts a peak X-ray diffraction image, X-ray intensity of which becomes maximum for each of pixels and compares the peak X-ray diffraction image among the pixels to thereby estimate a stress distribution of the object.