10 papers
Differentiable Thermodynamic Phase-Equilibria for Machine Learning
Karim K. Ben Hicham, Moreno Ascani, Jan G. Rittig +1
Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure in…
Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
Marah Almanasreh, Alexander Mitsos, Eike Cramer
Accurate prediction of polymerization dynamics is essential for process design, control, and optimization. Yet, purely mechanistic models require labor-intensive parameterization o…
Amortized Molecular Optimization via Group Relative Policy Optimization
Muhammad bin Javaid, Hasham Hussain, Ashima Khanna +5
In structurally constrained molecular optimization, state-of-the-art methods restart an expensive oracle-driven search from scratch for every new input structure, scaling poorly to…
Tabular foundation models for in-context prediction of molecular properties
Karim K. Ben Hicham, Jan G. Rittig, Martin Grohe +1
Accurate molecular property prediction is central to drug discovery, catalysis, and process design, yet real-world applications are often limited by small datasets. Molecular found…
Bayesian Optimization of Partially Known Systems using Hybrid Models
Eike Cramer, Luis Kutschat, Oliver Stollenwerk +2
Bayesian optimization (BO) has gained attention as an efficient algorithm for black-box optimization of expensive-to-evaluate systems, where the BO algorithm iteratively queries th…
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
Jan Pavšek, Alexander Mitsos, Elvis J. Sim +1
Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce ex…