Challenges, opportunities, and prospects in metal halide perovskites from theoretical and machine learning perspectives
Metal halide perovskite (MHP) is a promising next generation energy material for various
applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs …
applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs …
Science‐Driven Atomistic Machine Learning
JT Margraf - Angewandte Chemie International Edition, 2023 - Wiley Online Library
Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …
Mechanism of charge transport in lithium thiophosphate
Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …
True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis
Quantifying the accuracy of the satellite-based soil moisture (SM) data is important for a
number of key applications, such as: combining satellite-based SM products for long-term …
number of key applications, such as: combining satellite-based SM products for long-term …
Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems
Complex systems are typically characterized by intricate internal dynamics that are often
hard to elucidate. Ideally, this requires methods that allow to detect and classify in an …
hard to elucidate. Ideally, this requires methods that allow to detect and classify in an …
Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance
Feature selection is essential in the analysis of molecular systems and many other fields, but
several uncertainties remain: What is the optimal number of features for a simplified …
several uncertainties remain: What is the optimal number of features for a simplified …
Improved decision making with similarity based machine learning: applications in chemistry
D Lemm, GF von Rudorff… - … Learning: Science and …, 2023 - iopscience.iop.org
Despite the fundamental progress in autonomous molecular and materials discovery, data
scarcity throughout chemical compound space still severely hampers the use of modern …
scarcity throughout chemical compound space still severely hampers the use of modern …
Neural potentials of proteins extrapolate beyond training data
We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional
CG molecular mechanics force fields. We conclude that NN force fields are able to …
CG molecular mechanics force fields. We conclude that NN force fields are able to …
DADApy: Distance-based analysis of data-manifolds in Python
DADApy is a Python software package for analyzing and characterizing high-dimensional
data manifolds. It provides methods for estimating the intrinsic dimension and the probability …
data manifolds. It provides methods for estimating the intrinsic dimension and the probability …
Federated learning in computational toxicology: an industrial perspective on the Effiris Hackathon
In silico approaches have acquired a towering role in pharmaceutical research and
development, allowing laboratories all around the world to design, create, and optimize …
development, allowing laboratories all around the world to design, create, and optimize …