61 citations · 61 across the 2 of their papers we have counts for
6 papers
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng +4
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from…
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Jiang Wang, Stefan Chmiela, Klaus-Robert Müller +2
Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorit…
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Felix Sattler, Jackie Ma, Patrick Wagner +6
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS…
Building and Interpreting Deep Similarity Models
Oliver Eberle, Jochen Büttner, Florian Kräutli +3
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'. Before similarities a…
Exploring Chemical Compound Space with Quantum-Based Machine Learning
O. Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko
Rational design of compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout chemical compound space (CCS) -- the…
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky +2
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global pot…