5 citations · 15 across the 9 of their papers we have counts for
9 papers
Realistic Simulation of Valence-band Photoemission Spectra by using First-principles Calculations on the Basis of FLAPW Method
Noriaki Hamada, Satoshi Nakamura, Kenta Takahashi +2
In order to reproduce experimental photoemission spectroscopy (PES) data by using band structure calculations with linearized augmented-plane-wave method, we developed a new method…
MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics
Xiaochen Du, Juno Nam, Jaemoo Choi +7
Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent discrete neural samplers like MDNS…
PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment
Akshay Subramanian, Elton Pan, Juno Nam +6
Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because ca…
LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature
Magdalena Lederbauer, Siddharth Betala, Valerie Gentzke +22
Wide access to advanced experimental methods in materials science has given rise to an abundance of procedural knowledge, which is scattered across decades of scientific literature…
DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning
Elton Pan, Soonhyoung Kwon, Sulin Liu +9
The synthesis of crystalline materials, such as zeolites, remains a significant challenge due to a high-dimensional synthesis space, intricate structure-synthesis relationships and…
Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials
Thorben Prein, Elton Pan, Janik Jehkul +3
Inorganic synthesis planning currently relies primarily on heuristic approaches or machine-learning models trained on limited datasets, which constrains its generality. We demonstr…
Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
Thorben Prein, Elton Pan, Sami Haddouti +8
Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel…
Deep Reinforcement Learning for Inverse Inorganic Materials Design
Elton Pan, Christopher Karpovich, Elsa Olivetti
A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and s…
Constrained Model-Free Reinforcement Learning for Process Optimization
Elton Pan, Panagiotis Petsagkourakis, Max Mowbray +2
Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked tr…