Here we present the Mendeleev–Meyer Force Project which aims at tabulating all materials and substances in a fashion similar to the periodic table. The goal is to group and tabulate substances using nanoscale force footprints rather than atomic number or electronic configuration as in the periodic table. The process is divided into: (1) acquiring nanoscale force data from materials, (2) parameterizing the raw data into standardized input features to generate a library, (3) feeding the standardized library into an algorithm to generate, enhance or exploit a model to identify a material or property. We propose producing databases mimicking the Materials Genome Initiative, the Medical Literature Analysis and Retrieval System Online (MEDLARS) o...
This article was supported by the German Research Foundation (DFG) and the Open Access Publication F...
Cataloged from PDF version of article.We demonstrate high speed force–distance mapping using a doubl...
The computational prediction of the structure and stability of hybrid organic–inorganic interfaces p...
Here we present the Mendeleev-Meyer Force Project which aims at tabulating all materials and substan...
Classical force fields (FFs) based on machine learning (ML) methods show great potential for large s...
Classical force fields (FF) based on machine learning (ML) methods show great potential for large sc...
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departament...
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and app...
The application of materials informatics for the rational design of materials has been inspired by t...
Atomic-scale modeling and understanding of materials have made remarkable progress, but they are sti...
Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tool...
Defence is held on 12.1.2022 15:00 – 19:00 (Zoom), https://aalto.zoom.us/j/61312770423Atomic forc...
Accurate modelling of chemical and physical interactions is crucial for obtaining thermodynamic and ...
Force-spectroscopy by atomic force microscopy (AFM) is the technique of choice to measure mechanical...
Thesis (Ph.D.)--University of Washington, 2022Machine learning and natural language processing techn...
This article was supported by the German Research Foundation (DFG) and the Open Access Publication F...
Cataloged from PDF version of article.We demonstrate high speed force–distance mapping using a doubl...
The computational prediction of the structure and stability of hybrid organic–inorganic interfaces p...
Here we present the Mendeleev-Meyer Force Project which aims at tabulating all materials and substan...
Classical force fields (FFs) based on machine learning (ML) methods show great potential for large s...
Classical force fields (FF) based on machine learning (ML) methods show great potential for large sc...
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departament...
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and app...
The application of materials informatics for the rational design of materials has been inspired by t...
Atomic-scale modeling and understanding of materials have made remarkable progress, but they are sti...
Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tool...
Defence is held on 12.1.2022 15:00 – 19:00 (Zoom), https://aalto.zoom.us/j/61312770423Atomic forc...
Accurate modelling of chemical and physical interactions is crucial for obtaining thermodynamic and ...
Force-spectroscopy by atomic force microscopy (AFM) is the technique of choice to measure mechanical...
Thesis (Ph.D.)--University of Washington, 2022Machine learning and natural language processing techn...
This article was supported by the German Research Foundation (DFG) and the Open Access Publication F...
Cataloged from PDF version of article.We demonstrate high speed force–distance mapping using a doubl...
The computational prediction of the structure and stability of hybrid organic–inorganic interfaces p...