6.3k citations
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Showing 2022Show all
3 papers · 1 filter
physics.chem-ph2022★ 75 cited
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
Benedikt Winter, Clemens Winter, Timm Esper +2
The availability of property data is one of the major bottlenecks in the development of chemical processes, often requiring time-consuming and expensive experiments or limiting the…
physics.chem-ph2022★ 80 cited
A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing
Benedikt Winter, Clemens Winter, Johannes Schilling +1
Knowledge of mixtures' phase equilibria is crucial in nature and technical chemistry. Phase equilibria calculations of mixtures require activity coefficients. However, experimental…
cs.LG2022★ 514 cited
Exploration in Deep Reinforcement Learning: A Survey
Pawel Ladosz, Lilian Weng, Minwoo Kim +1
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward pro…