1 code implementation • 1 Nov 2023 • Anuroop Sriram, Sihoon Choi, Xiaohan Yu, Logan M. Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J. Medford, David S. Sholl
We also trained state-of-the-art ML models on this dataset to approximate calculations at the DFT level.
1 code implementation • 12 Apr 2023 • Gabriel S. Gusmão, Andrew J. Medford
Physics-informed neural networks (PINNs) have proven a suitable mathematical scaffold for solving inverse ordinary (ODE) and partial differential equations (PDE).
no code implementations • 27 Sep 2021 • M. Ross Kunz, Adam Yonge, Rakesh Batchu, Zongtang Fang, Yixiao Wang, Gregory Yablonsky, Andrew J. Medford, Rebecca Fushimi
As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing in the calibration of the inert and feed mixture products without need of prior calibration experiments or heuristic input from the user.
1 code implementation • 4 Feb 2021 • Xiangyun Lei, Andrew J. Medford
However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales.
no code implementations • 30 Nov 2020 • Gabriel S. Gusmão, Adhika P. Retnanto, Shashwati C. da Cunha, Andrew J. Medford
Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes.
no code implementations • 17 Nov 2020 • M. Ross Kunz, Adam Yonge, Zongtang Fang, Andrew J. Medford, Denis Constales, Gregory Yablonsky, Rebecca Fushimi
As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps.
1 code implementation • 10 May 2020 • Christopher Rose, Andrew J. Medford, C. Franklin Goldsmith, Tejs Vegge, Joshua S. Weitz, Andrew A. Peterson
The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in disease susceptibility.
no code implementations • 20 Aug 2019 • Xiangyun Lei, Fred Hohman, Duen Horng Chau, Andrew J. Medford
In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory.