Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session F32: Data Science for IndustryInvited Recordings Available
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Sponsoring Units: GDS FIAP Chair: Maria Longobardi, University of Basel, Switzerland Room: McCormick Place W-192B |
Tuesday, March 15, 2022 8:00AM - 8:36AM |
F32.00001: Quantum computing and its applications in natural sciences Invited Speaker: Ivano Tavernelli The original idea that a quantum machine can potentially solve many-body quantum mechanical problems more efficiently than classical computers is due to R. Feynman who proposed the use of quantum computers to investigate the fundamental properties of nature at the quantum scale. In particular, the solution of problems in electronic structure, material design, high energy physics, and statistical mechanics is a challenging computational task as the number of resources needed increases exponentially with the number of degrees of freedom. Thanks to the development of new quantum technologies witnessed over the last decades, we have now the possibility to address these classes of problems with the help quantum computers. To achieve this goal, new quantum algorithms able to best exploit the potential quantum speedup of state-of-the-art, noisy, quantum hardware have also been developed [1]. |
Tuesday, March 15, 2022 8:36AM - 9:12AM |
F32.00002: Data Science for Biopharmaceutical Development Invited Speaker: Valentin G Stanev Biopharmaceuticals are a promising class of drugs that provide innovative ways to treat many acute and chronic diseases, including cancer. However, manufacturing these complex macromolecules poses unique challenges. In this talk, I will discuss some of the ways in which data science can be used to accelerate the development of biopharmaceutical drug products and thus reduce the cost and increase the availability of many unique therapeutics. I will also talk about my journey from doing theoretical physics research to working for a global pharmaceutical company, and some of the lessons I learned along the way. |
Tuesday, March 15, 2022 9:12AM - 9:48AM |
F32.00003: Extending the Reach of Quantum Monte Carlo Methods via Machine Learning Invited Speaker: Brenda M Rubenstein Quantum Monte Carlo methods are a promising suite of stochastic electronic structure methods that enable the high-accuracy modeling of strongly correlated molecules and materials at comparatively modest costs through the sampling of random numbers. Historically, these methods have excelled at computing energies, but have struggled to efficiently compute forces and scale to large system sizes that approach the thermodynamic limit. In this work, we illustrate how machine learning can address these critical shortcomings. We will first describe our recent endeavors employing active learning with AMPTorch to predict QMC-quality forces for the relaxation of molecular geometries and molecular dynamics simulations. We will illustrate how active learning is particularly crucial in the QMC context because of the lack of forces that can be leveraged for training. In the second portion of this talk, we will subsequently illustrate how Gaussian Process Regression (GPR) can be used to more accurately predict the energies of solids in the thermodynamic limit than conventional scaling equations. Altogether, this work demonstrates how machine learning can be used to extend the capabilities of stochastic methods, increasing their overall practicality and applicability. |
Tuesday, March 15, 2022 9:48AM - 10:24AM |
F32.00004: TBA Invited Speaker: Sarah Sirin
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Tuesday, March 15, 2022 10:24AM - 11:00AM |
F32.00005: Supporting Trusted Reuse of AI Models Invited Speaker: Peter Bajcsy With the widespread use of artificial intelligence (AI) models in physical and life sciences, imaging researchers can automate image-based measurements by reusing disseminated trained AI models. The challenges of reusing shared AI models include (a) trust in models’ performance due to insufficient information about shared AI models and black-box nature of the models, (b) insufficient size of domain-specific training datasets for retraining, and (c) computational resources needed to reproduce the work leading to the disseminated AI model. We will address these challenges by introducing (1) characteristics of AI models derived from optimization curves to be included in AI model cards and (2) traceable fingerprints of AI models to their training data. The presentation will demonstrate the value of quantitative metrics for multi-purpose reuse of AI models, as well as the value of AI model simulations and measurements to establish traceability in non-linear models. |
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