8 citations · 19 across the 10 of their papers we have counts for
11 papers
Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
Ashima Khanna, Dominik Grimm
Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informative. Existing reinforcement le…
Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
Serra Korkmaz, Adam Izdebski, Jonathan Pirnay +5
Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, such candidates are rare and expensive to evaluate, yiel…
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…
GraphXForm: Graph transformer for computer-aided molecular design
Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf +4
Generative deep learning has become pivotal in molecular design for drug discovery, materials science, and chemical engineering. A widely used paradigm is to pretrain neural networ…
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial Optimization
Jonathan Pirnay, Dominik G. Grimm
The constructive approach within Neural Combinatorial Optimization (NCO) treats a combinatorial optimization problem as a finite Markov decision process, where solutions are built…
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Jonathan Pirnay, Dominik G. Grimm
Current methods for end-to-end constructive neural combinatorial optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from re…
Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge
Quirin Göttl, Jonathan Pirnay, Jakob Burger +1
Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learnin…
Policy-Based Self-Competition for Planning Problems
Jonathan Pirnay, Quirin Göttl, Jakob Burger +1
AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficientl…
Convex Envelope Method for determining liquid multi-phase equilibria in systems with arbitrary number of components
Quirin Göttl, Jonathan Pirnay, Dominik G. Grimm +1
The determination of liquid phase equilibria plays an important role in chemical process simulation. This work presents a generalization of an approach called the convex envelope m…
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
Florian Haselbeck, Dominik G. Grimm
Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. The…
Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning
Quirin Göttl, Dominik G. Grimm, Jakob Burger
Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsh…