Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …
predict or elucidate the relationship between the atomic-scale structure of matter and its …
Machine learning approaches for biomolecular, biophysical, and biomaterials research
CA Rickert, O Lieleg - Biophysics Reviews, 2022 - pubs.aip.org
A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake
images created within seconds—all those things that have been unthinkable for a long time …
images created within seconds—all those things that have been unthinkable for a long time …
Deep learning for automated classification and characterization of amorphous materials
It is difficult to quantify structure–property relationships and to identify structural features of
complex materials. The characterization of amorphous materials is especially challenging …
complex materials. The characterization of amorphous materials is especially challenging …
Structure-property maps with Kernel principal covariates regression
Data analyses based on linear methods constitute the simplest, most robust, and transparent
approaches to the automatic processing of large amounts of data for building supervised or …
approaches to the automatic processing of large amounts of data for building supervised or …
Identifying and tracking defects in dynamic supramolecular polymers
A central paradigm of self-assembly is to create ordered structures starting from molecular
monomers that spontaneously recognize and interact with each other via noncovalent …
monomers that spontaneously recognize and interact with each other via noncovalent …
A new kind of atlas of zeolite building blocks
We have analyzed structural motifs in the Deem database of hypothetical zeolites to
investigate whether the structural diversity found in this database can be well-represented …
investigate whether the structural diversity found in this database can be well-represented …
TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra
Many molecular systems and physical phenomena are controlled by local fluctuations and
microscopic dynamical rearrangements of the constitutive interacting units that are often …
microscopic dynamical rearrangements of the constitutive interacting units that are often …
HydraScreen: a generalizable structure-based deep learning approach to drug discovery
A Prat, H Abdel Aty, O Bastas… - Journal of Chemical …, 2024 - ACS Publications
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug
discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed …
discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed …
Machine learning at the atomic-scale
F Musil, M Ceriotti - ar** the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings from Ab Initio Random Structure Search and Experiments
Machine learning is changing how we design and interpret experiments in materials
science. In this work, we show how unsupervised learning, combined with ab initio random …
science. In this work, we show how unsupervised learning, combined with ab initio random …