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![An Interpretable Machine Learning Model for Advancing Time Series Predictions CSED Computational Sciences and Engineering Division ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-07/an_interpretable_machine_learning_model_.png?h=c791aa4d&itok=DncMrH64)
Oak Ridge National Laboratory researchers developed an interpretable long short-term memory (iLSTM) network for time-series prediction.
![ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-04/2022-G00330_KESER%20Illustration_0.jpg?h=1cb48fc4&itok=c6ZuDdDg)
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
![Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.](/sites/default/files/styles/list_page_thumbnail/public/2022-04/CCSD_NeuralNetworkBanner.png?h=b16f811b&itok=fxqDEvs_)
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
![Earth Day](/sites/default/files/styles/list_page_thumbnail/public/2022-04/Earth%20image.png?h=8f74817f&itok=5rQ_su9Z)
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
![Gilles Buchs](/sites/default/files/styles/list_page_thumbnail/public/2022-04/GillesBuchs_0.jpg?h=933ee03b&itok=rLYQf5B4)
Gilles Buchs has spent his career working in the fields of nanoscience, photonics systems and quantum technologies in academia and industry.
![Exploring the smallest distance scales with particle colliders often requires detailed calculations of the spectra of outgoing particles (smallest filled green circles). Image Credit: Benjamin Nachman, Berkeley Lab](/sites/default/files/styles/list_page_thumbnail/public/2022-04/Nachman-schematic_0.png?h=ba0ef1c4&itok=11-E7fDz)
Lawrence Berkeley National Laboratory physicists Christian Bauer, Marat Freytsis and Benjamin Nachman have leveraged an IBM Q quantum computer through the Oak Ridge Leadership Computing Facility’s Quantum Computing User Program to capture part of a