Search Results for author: Andrew J. Medford

Found 8 papers, 4 papers with code

Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems

1 code implementation12 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).

A Priori Calibration of Transient Kinetics Data via Machine Learning

no code implementations27 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.

BIG-bench Machine Learning

A Universal Framework for Featurization of Atomistic Systems

1 code implementation4 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.

BIG-bench Machine Learning Computational Efficiency

Kinetics-Informed Neural Networks

no code implementations30 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.

Disentanglement Multiobjective Optimization

Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning

no code implementations17 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.

BIG-bench Machine Learning

Heterogeneity in susceptibility dictates the order of epidemiological models

1 code implementation10 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.

Epidemiology

ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features

no code implementations20 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.

BIG-bench Machine Learning

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