2022 MRS Spring Meeting & Exhibit Landing Banner

Symposium DS01—Integrating Machine Learning and Simulations for Materials Modeling, Design and Manufacturing

2022-05-08   Show All Abstracts

Times shown in HST (GMT-10:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.01: Simulation and Machine Learning I
Session Chairs
Mathieu Bauchy
Sunday AM, May 8, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

9:00 AM - DS01.01.01
Graph Neural Network for Improved Property Predictions of Molecules, Solids and Metal Organic Framworks

Kamal Choudhary1

National Institute of Standards and Technology1

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9:15 AM - DS01.01.02
Theoretical Prediction of the Electronic and Structural Properties of Van der Waals Heterostructures Using a Combined Machine Learning and Density Functional Theory Approach

Daniel Willhelm1,Nathan Wilson1,Raymundo Arroyave1,Xiaoning Qian1,Tahir Cagin1,Ruth Pachter2,Xiaofeng Qian1

Texas A&M University1,Air Force Research Laboratory2

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9:30 AM - DS01.01.03
Efficient Pneumatic Gripper Simulator Using Machine Learning And Optimization

Zhizhou Zhang1,Zeqing Jin1,Grace Gu1

University of California, Berkeley1

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9:45 AM - DS01.01.04
Accelerating Phase-Field Based Predictions via Surrogate Models Trained by Machine Learning Methods

Remi Dingreville1

Sandia National Laboratories1

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10:00 AM - DS01.01
BREAK


10:30 AM - DS01.01.05
A Machine Learning Framework for Damage Mechanism Identification from Acoustic Emission in Unidirectional SiC/SiC CMCs

Caelin Muir1,Bhavana Swaminathan1,Kirk Fields1,Amjad Almansour2,Michael Presby2,Kathleen Sevener3,Craig Smith2,James Kiser2,Samantha Daly1

University of California, Santa Barbara1,NASA Glenn Research Center2,University of Michigan–Ann Arbor3

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10:45 AM - DS01.01.06
Computational and Machine Learning Approach to Electrochemistry of Disordered Rocksalt Cathode Materials

Peichen Zhong1,2,Gerbrand Ceder1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2

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11:00 AM - DS01.01.07
Automated Framework for the Inversion of Experimental Data to Atomistic Structure Using Computer Vision and Multi-Objective Evolutionary Algorithms

Venkata Surya Chaitanya Kolluru1,Eric Schwenker1,Davis Unruh1,Maria Chan1

Argonne National Laboratory1

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11:15 AM - DS01.01.08
Lightweight and Strong Lattice Structure Designs by Generative Machine Learning and Additive Manufacturing

Sangryun Lee1,Zhizhou Zhang1,Grace Gu1

University of California, Berkeley1

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11:30 AM - DS01.01.09
Molecular Dynamics Simulations for the Molecular Polarization of Salt-Free and Salt-Containing Liquids with Stockmayer Fluids and Ensemble Neural Networks

Issei Nakamura1,Tong Gao1,Mark Stevens2,3,Amalie Frischknecht2,3

Michigan Technological University1,Sandia National Laboratories2,Center for Integrated Nanotechnologies3

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DS01.02: Simulation and Machine Learning II
Session Chairs
Mathieu Bauchy
Sunday PM, May 8, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

1:30 PM - *DS01.02.01
Neural Networks for Modeling Materials with Long-Range Interactions

Emine Kucukbenli1,2

Boston University1,Harvard University2

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2:00 PM - DS01.02.02
Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Tian Xie1,Xiang Fu1,Octavian Ganea1,Regina Barzilay1,Tommi Jaakkola1

Massachusetts Institute of Technology1

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2:15 PM - DS01.02.04
Predicting Plastic Anisotropy Using Crystal Plasticity and Bayesian Neural Network Surrogate Models

David Montes de Oca Zapiain1,Hojun Lim1,Taejoon Park2,Farhang Pourboghrat2

Sandia National Laboratories1,The Ohio State University2

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2:30 PM - DS01.02.05
Using ML Tools to Enable High-throughput Studies of Amorphous Material Surfaces, and Its Application to Plasma Etching

Martin Siron1,2,Nita Chandrasekhar2,Kristin Persson1,3

University of California, Berkeley1,Intel Corporation2,Lawrence Berkeley National Laboratory3

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2:45 PM - DS01.02
BREAK


3:15 PM - DS01.02.06
Predicting Compositional Changes of Organic-Inorganic Hybrid Materials with Augmented CycleGAN

Qianxiang Ai1,Alexander Norquist2,Joshua Schrier1

Fordham University1,Haverford College2

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3:30 PM - DS01.02.07
Learning to Simulate Time-Averaged Coarse-Grained Molecular Dynamics with Geometric Machine Learning

Xiang Fu1,Tian Xie1,Nathan Rebello1,Bradley Olsen1,Tommi Jaakkola1

Massachusetts Institute of Technology1

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3:45 PM - DS01.02.08
Atomistic Modeling and Uncertainty Quantification for Mechanical Properties of Graphene Aerogels

Bowen Zheng1,Zeyu Zheng1,Grace Gu1

University of California, Berkeley1

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4:00 PM - DS01.02.09
Predicting Solvent-Polymer Solubility with Machine Learning

Joseph Kern1,Mona Amrihesari1,Shruti Venkatram1,Blair Brettmann1,Rampi Ramprasad1

Georgia Institute of Technology1

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2022-05-09   Show All Abstracts

Times shown in HST (GMT-10:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.03: Simulation and Machine Learning III
Session Chairs
Ekin Cubuk
Badri Narayanan
Monday AM, May 9, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

10:30 AM - *DS01.03.01
Polymer Informatics—Recent Advances in Algorithms to Solve Forward and Inverse Problems

Rampi Ramprasad1

Georgia Institute of Technology1

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11:00 AM - DS01.03.02
Learning Hierarchical Synthesis Recipes by Spectral Shape Matching and Optimization on Hyperbolic Spaces

Kiran Vaddi1,Huat Chiang1,Lilo Pozzo1

University of Washington1

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11:15 AM - DS01.03.03
Studying Disordered Material Dynamics Using a Simulator/Machine Learning Pipeline for X-Ray Speckle Analysis

Sathya Chitturi1,2,Youssef Nashed2,Nicolas Burdet2,Daniel Ratner2,Thomas Lane3,2,Yanwen Sun2,Diling Zhu2,Matt Seaberg2,Chuck Yoon2,Mike Dunne2,1,Joshua Turner2

Stanford University1,SLAC National Accelerator Laboratory2,Deutsches Elektronen-Synchrotron DESY3

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11:30 AM - DS01.03.04
Calibrating DFT Formation Enthalpy Calculations by Multi-Fidelity Machine Learning

Sheng Gong1,Shuo Wang2,Tian Xie1,Woo Hyun Chae1,Runze Liu1,Jeffrey Grossman1

Massachusetts Institute of Technology1,University of Maryland2

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11:45 AM - DS01.03.05
Case Studies in Representation Learning for Inverse Materials Design

Wesley Reinhart1,Arindam Debnath1,Seda Oturak1,Debjyoti Bhattacharya1

The Pennsylvania State University1

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DS01.04: Simulation and Machine Learning IV
Session Chairs
Grace Gu
Monday PM, May 9, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

1:30 PM - *DS01.04.01
Materials Discovery Using Deep Learning and Differentiable Physics

Ekin Cubuk1

Google1

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2:00 PM - DS01.04.02
CO-Induced Restructuring of Pt Nanoparticles from Machine-Learning Molecular Dynamics—Bayesian Active Learning and Neural Network Approaches

Cameron Owen1,Jin Soo Lim1,Lixin Sun1,Yu Xie1,Isabel Diersen1,Boris Kozinsky1,2

Harvard University1,Bosch2

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2:15 PM - DS01.04.03
Learning Hidden Elasticity with Deep Neural Networks

Chun-Teh Chen1,Grace Gu1

University of California, Berkeley1

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2:30 PM - DS01.04.04
Fully Automated Nanoscale to Atomistic Structure from Theory and X-Ray Spectroscopy Experiments

Davis Unruh1,Venkata Surya Chaitanya Kolluru1,Eli Kinigstein1,Xiaoyi Zhang1,Maria Chan1

Argonne National Laboratory1

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2:45 PM - DS01.04.05
Decision Trees in Continuous Action Space for High-Throughput Exploration of Potential Energy Surface of Nanoclusters

Sukriti Manna1,Troy Loeffler1,Rohit Batra1,Suvo Banik1,Henry Chan1,Subramanian Sankaranarayanan1

Argonne National Laboratory1

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3:00 PM - DS01.04
BREAK


3:30 PM - DS01.04.06
High-Throughput Simulation for Machine Learning and Transfer Learning for Applications in Automated Characterization with High-Resolution Transmission Electron Microscopy (HRTEM)

Luis Rangel DaCosta1,Katherine Sytwu2,Catherine Groschner1,Mary Scott1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2

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3:45 PM - DS01.04.07
Many-Body Interatomic Potential with Bayesian Active Learning, an Application to SiC

Yu Xie1,Jonathan Vandermause1,Senja Ramakers2,3,Nakib Protik1,Anders Johansson1,Boris Kozinsky1

Harvard University1,Robert Bosch GmbH2,Ruhr-Universität3

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4:00 PM - DS01.04.08
Process Modeling of Direct Ink Write 3D Printing Using Computer Vision and Machine Learning

Devin Roach1,William Reinholtz1,Adam Cook1

Sandia National Laboratories1

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4:15 PM - DS01.04.09
A Critical Assessment of Neural Network Potentials for Water and the Role of Nuclear Quantum Effects Through the Van Hove Correlation Function

Murali Gopal Muraleedharan1,Paul Kent1

Oak Ridge National Laboratory1

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4:30 PM - DS01.04.10
Machine-Learning Interatomic Potentials for Bulk Metallic Glasses

Nicholas Martinez1,Gabriel Medrano1,Oliviero Andreussi1

University of North Texas1

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4:45 PM - DS01.04.11
Data Ecosystem of the Ultrahigh Temperature Refractory Alloys (ULTERA) Database

Adam Krajewski1,Zi-Kui Liu1

The Pennsylvania State University1

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2022-05-10   Show All Abstracts

Times shown in HST (GMT-10:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.05: Simulation and Machine Learning V
Session Chairs
N M Anoop Krishnan
Badri Narayanan
Tuesday AM, May 10, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

8:30 AM - *DS01.05.01
Active Learning of Neural Network Interatomic Potentials with Differentiable Uncertainty

Rafael Gomez-Bombarelli1

Massachusetts Institute of Technology1

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9:00 AM - DS01.05.02
NequIP—Equivariance Enables Machine Learning Interatomic Potentials at Unprecedented Sample Efficiency and Accuracy

Simon Batzner1,Albert Musaelian1,Lixin Sun1,Mario Geiger2,Jonathan Mailoa3,Mordechai Kornbluth3,Nicola Molinari1,Tess Smidt4,Boris Kozinsky1,3

Harvard University1,EPFL2,Robert Bosch Research and Technology Center3,Massachusetts Institute of Technology4

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9:15 AM - DS01.05.03
Navigating to Islands of Photostability—Multi-Objective Optimization of Perovskite Absorber Compositions for Targeted Photovoltaic Applications Using High-Throughput Robotic Experimentation

Rishi Kumar1,Moses Kodur1,Jack Palmer1,Connor Dolan1,Deniz Cakan1,David Fenning1

University of California, San Diego1

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9:30 AM - DS01.05.04
Understanding Phase Stability and Phase Transition of Boron Suboxide Using First-Principles Based Potentials

Bin Liu1

Kansas State University1

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9:45 AM - DS01.05.05
Exploring Kinetic Pathways for Ice Nucleation Using Evolutionary Reinforcement Learning

Anirban Chandra1,Rohit Batra2,Amanda Dufek3,Suvo Banik1,Isaac Tamblyn4,Pierre Darancet2,Stephen Whitelam3,Subramanian Sankaranarayanan2,1

University of Illinois at Chicago1,Argonne National Laboratory2,Lawrence Berkeley National Laboratory3,University of Ottawa4

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10:00 AM - DS01.05
BREAK


10:30 AM - DS01.05.06
Computer Vision and Artificial Intelligence for Smart Additive Manufacturing

Grace Gu1

University of California, Berkeley1

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10:45 AM - DS01.05.07
Overcoming Data Scarcity in Materials Science with Meta-Learning

Rees Chang1,Yu-Xiong Wang1,Elif Ertekin1

University of Illinois at Urbana-Champaign1

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11:00 AM - DS01.05.08
Free Energy Calculation of Crystalline Solids Using Normalizing Flow

Rasool Ahmad1,Wei Cai1

Stanford University1

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11:15 AM - DS01.05.09
Ab Initio Modeling Data Based Autoencoder to Interpret ARPES Data and Assist Inverse Design of Semiconductor Heterostructures

Sanghamitra Neogi1,Artem Pimachev1

University of Colorado Boulder1

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11:30 AM - DS01.05.10
How Machine Learning Can Help Thermodynamics and Kinetics Modeling in Metallic Materials

Liang Tian1

University of Alabama1

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DS01.06: Simulation and Machine Learning VI
Session Chairs
Rafael Gomez-Bombarelli
Grace Gu
Tuesday PM, May 10, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

1:30 PM - *DS01.06.01
ML+Modeling for Materials Characterization and Design

Maria Chan1

Argonne National Laboratory1

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2:00 PM - DS01.06.02
Designing New Forcefield Using Board AI

Troy Loeffler1,Sukriti Manna1,Henry Chan1,Rohit Batra1,Subramanian Sankaranarayanan1

Argonne National Laboratory1

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2:15 PM - DS01.06.03
GDSPEC—Graph Order and Atomic Density Spectrum for Learning Chemical Environments

Suvo Banik1,Sukriti Manna1,Debdas Dhabal2,Valeria Molinero2,Subramanian Sankaranarayanan1

University of Illinois at Chicago1,University of Utah2

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2:30 PM - DS01.06.04
Multi-Reward Reinforcement Learning Based Inter-Atomic Potential Models for Silica

Aditya Koneru1,2,Henry Chan1,2,Sukriti Manna1,2,Troy Loeffler1,2,Valeria Molinero3,Subramanian Sankaranarayanan1,2

University of Illinois at Chicago1,Argonne National Laboratory2,The University of Utah3

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2:45 PM - DS01.06.05
Towards Systematically Improvable Deep Learning Interatomic Potentials with Deep Interatomic Cluster Expansions (DICE)

Albert Musaelian1,Simon Batzner1,Boris Kozinsky1,2

Harvard University1,Robert Bosch Research and Technology Center2

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3:00 PM - DS01.06
BREAK


3:30 PM - DS01.06.06
Multi-Objective Optimization of Graphene-Based Sensors with Batch Evaluations

Patrick Johnson1,Hud Wahab1,Todd Muller1,Lars Kotthoff1

University of Wyoming1

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3:45 PM - DS01.06.07
Inductive Bisa Graph Network for Robust Molecular Dynamics Simulation of Materials

Pankaj Rajak1,Aravind Krishnamoorthy1,Rajiv Kalia1,Aiichiro Nakano1,Priya Vashishta1,Ekin Cubuk2

University of Southern California1,Google Brain2

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4:00 PM - DS01.06.08
High-Throughput Experiments and Holistic Integration with Computational Data to Accelerate Alloy Design

Ji-Cheng Zhao1

University of Maryland1

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4:15 PM - DS01.06.09
Bio-Inspired Computational Design of Vascularized Electrodes for High-Performance Fast-Charging Batteries Optimized by Deep Learning

Po-Chun Hsu1

Duke University1

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4:30 PM - DS01.06.10
Machine Learning for Exploration of Defects in 2D Grain Boundaries

Jianan Zhang1,Aditya Koneru1,2,Srilok Srinivasan2,Subramanian Sankaranarayanan1,2,Carmen Lilley1

University of Illinois at Chicago1,Argonne National Laboratory2

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4:45 PM - DS01.06.11
Data Problems in Materials Modeling and Closed-Loop Experiments

Henry Chan1,Aditya Koneru2,Subramanian Sankaranarayanan1,2,Valeria Molinero3,Jie Xu1

Argonne National Laboratory1,University of Illinois at Chicago2,The University of Utah3

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DS01.07: Poster Session I: Integrating Machine Learning and Simulations for Materials Modeling, Design and Manufacturing I
Session Chairs
Rafael Gomez-Bombarelli
N M Anoop Krishnan
Tuesday PM, May 10, 2022
Hawai'i Convention Center, Level 1, Kamehameha Exhibit Hall 2 & 3

5:00 PM - DS01.07.01
Machine Learning Model for Electrical and Thermal Conductivities of Copper – Carbon Nanotubes Composites

Faizan Ejaz1,Dong Su Lee2,Jangyup Son2,Jin-Sang Kim2,Beomjin Kwon1

Arizona State University1,Korea Institute of Science and Technology (KIST)2

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5:00 PM - DS01.07.02
A Machine Learning Study for Designing Thin-Film Optical Metamaterials

Goeun Kim2,Tengfei Luo1,Seongmin Kim1,Eungkyu Lee2

University of Notre Dame1,Kyung Hee University2

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5:00 PM - DS01.07.03
Machine Learning-Based Optimization of Biomimetic Hierarchical Porous Structures Inspired by the Sea Glass Sponge

Ailin Chen1,Sangryun Lee1,Grace Gu1

University of California, Berkeley1

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5:00 PM - DS01.07.04
Crystal Level Features Developed Using Edge Prediction on Graphs Derived from Crystals

Divya Sharma1,Xiangyu Chen1,Haili Jia1,Paulette Clancy1

Johns Hopkins University1

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5:00 PM - DS01.07.05
Generative Machine Learning Approach for Asymmetric Cellular Architectures with Enhanced Mechanical Properties

Shao-Yi Yu1,Sangryun Lee1,Grace Gu1

University of California, Berkeley1

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5:00 PM - DS01.07.06
Machine-Learning Accelerated Synthesis of Nitride Materials—Prediction of Synthesis Pathways

Linus Kautzsch1,Aiden Reilly1,Ram Seshadri1,Stephen Wilson1

University of California, Santa Barbara1

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2022-05-11   Show All Abstracts

Times shown in HST (GMT-10:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.08: Simulation and Machine Learning VII
Session Chairs
Mathieu Bauchy
Valeria Molinero
Wednesday AM, May 11, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

8:30 AM - *DS01.08.01
Investigating Atomic-Scale Mechanisms of Crystallization Using Machine Learning

Rodrigo Freitas1

Massachusetts Institute of Technology1

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9:00 AM - DS01.08.02
Data-Driven Decision Making for Autonomous Materials Synthesis

Nathan Szymanski1,2,Pragnay Nevatia1,2,Yan Zeng2,Christopher Bartel1,2,Gerbrand Ceder1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2

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9:15 AM - DS01.08.03
A Cluster-Based Approach for Identifying and Meshing Crystalline Regions in Molecular Dynamics Simulation

Thomas Barrett1,Marilyn Minus1

Northeastern University1

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9:30 AM - DS01.08.04
Automated Discovery of Chemical Reaction Kinetics for Carbon Dioxide Capture Solutions

Theodore van Kessel1,Benjamin Wunsch1,Stacey Gifford1,Flaviu Cipcigan1,James Mcdonagh1,Dmitry Zubarev1,Alexander Harrison1,Stamatia Zavitsanou1

IBM1

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9:45 AM - DS01.08.05
A Data-Driven Approach to Predict Full-Field Nonlinear Stress Distribution and Crack Path in Microstructural Representation of Composites

Maryam Shakiba1,Reza Sepasdar1

Virginia Tech1

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10:00 AM - DS01.08
BREAK


10:30 AM - DS01.08.06
Differentiable Physics for Materials Discovery

Samuel Schoenholz1,Amil Merchant1,Ekin Cubuk1

Google1

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10:45 AM - DS01.08.07
Controlling Hydrogen Cottrell Atmospheres Around Dislocations in Austenitic Stainless Steels Through Alloying Using a Combined MD-DFT Pipeline

Chris Nowak1,Michael Foster1,Ryan Sills2,Xiaowang Zhou1

Sandia National Laboratories1,Rutgers, The State University of New Jersey2

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11:00 AM - DS01.08.08
Machine-Learning Studies of Hydrogen Effects on Stacking Fault Energies in an Fe0.70Ni0.11Cr0.19 Austenitic Stainless Steels

Xiaowang Zhou1,Chris Nowak1,Michael Foster1,Ryan Sills2,Joseph Allen Ronevich1,Chris San Marchi1

Sandia National Laboratories1,Rutgers University2

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11:15 AM - DS01.08.09
On Generalizability of Data-Driven Microstructure-Property Mappings in Organic Solar Cells

Hao Liu1,Nirmal Baishnab2,Balaji Pokuri2,Baskar Ganapathysubramanian2,Olga Wodo1

University at Buffalo, The State University of New York1,Iowa State University of Science and Technology2

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11:30 AM - DS01.08.10
Unsupervised Large-Scale 3D Phase-Contrast Imaging From Scanning Diffraction Measurements

Philipp Pelz1,2,Mingee Cho1,2,Mary Scott1,2,Colin Ophus2

UC Berkeley1,National Center for Electron Microscopy2

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DS01.09: Simulation and Machine Learning VIII
Session Chairs
Mathieu Bauchy
Wednesday PM, May 11, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

1:30 PM - *DS01.09.01
Elucidating the Mechanisms of Synthesis of Zeolites Using Data Science and Molecular Simulations

Valeria Molinero1,Debdas Dhabal1,Suvo Banik2,3,Andressa Bertolazzo1,Subramanian Sankaranarayanan2,3

University of Utah1,University of Illinois at Chicago2,Argonne National Laboratory3

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2:00 PM - DS01.09.03
Ultra-Fast Interpretable Machine-Learning Potentials for Metals and Semiconductors

Richard Hennig2,Stephen Xie1,2,Pawan Prakash2,Robert Schmid3,Matthias Rupp3

KBR at NASA Ames Research Center1,University of Florida2,Universität Konstanz3

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2:15 PM - DS01.09.04
Data-Augmentation for Graph Neural Network Learning of the Relaxed Energy of Unrelaxed Structures

Jason Gibson1,Ajinkya Hire1,Richard Hennig1

University of Florida1

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2:30 PM - DS01.09
BREAK


3:00 PM - DS01.09.05
Graph-Based Strategy for Microstructure Similarity in Large Datasets

Parth Desai1,Namit Juneja1,Varun Chandola1,Jaroslaw Zola1,Olga Wodo1

University at Buffalo, The State University of New York1

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3:15 PM - DS01.09.06
Data-Driven Field Inversion of Molecular Simulations to Construct Free Energy Landscapes of Organic Semiconducting Systems

Chih-Hsuan (Bella) Yang1,Baskar Ganapathysubramanian1,Balaji Pokuri1

Iowa State University1

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3:30 PM - DS01.09.07
Reinforcement Learning for Molecule Space Exploration: Conditioned Latent Representations via Large Scale Self-Supervised Learning

Chih-Hsuan (Bella) Yang1,Hsin-Jung Yang1,Vinayak Bhat2,Parker Sornberger2,Balaji Pokuri1,Chad Risko2,Baskar Ganapathysubramanian1

Iowa State University1,University of Kentucky2

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3:45 PM - DS01.09.08
Determining the Thermal Conductivity and Phonon Behavior of SiC Materials with Quantum Accuracy via Deep Learning Interatomic Potential Model

Baoqin Fu1,Yandong Sun2,Wanrun Jiang3,4,Fu Wang1,Linfeng Zhang5,3,Han Wang6,Ben Xu2

Sichuan University1,China Academy of Engineering Physics2,AI for Science Institute3,Beijing Academy of Artificial Intelligence4,DP Technology5,Institute of Applied Physics and Computational Mathematics6

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4:00 PM - DS01.09.10
Exploring the Necessary Complexity of Interatomic Potentials

Joshua Vita1,Dallas Trinkle1

University of Illinois at Urbana-Champaign1

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DS01.10: Poster Session II: Integrating Machine Learning and Simulations for Materials Modeling, Design and Manufacturing II
Session Chairs
Valeria Molinero
Wednesday PM, May 11, 2022
Hawai'i Convention Center, Level 1, Kamehameha Exhibit Hall 2 & 3

5:00 PM - DS01.10.01
Towards Interpretable Polyamide Property Prediction

Franklin Lee1,Jaehong Park2,Sushmit Goyal1,Yousef Qaroush1,Shihu Wang1,Hong Yoon3,Aravind Rammohan1,Youngseon Shim2

Corning Incorporated1,Samsung Electronics Co, Ltd.2,Corning Precision Materials Co., Ltd.3

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5:00 PM - DS01.10.02
Multiscale Neural-Network Quantum Molecular Dynamics and Molecular Mechanics for Polar Topological Structures

Ken-ichi Nomura1,Thomas Linker1,Shogo Fukushima2,Rajiv Kalia1,Aravind Krishnamoorthy1,Aiichiro Nakano1,Kohei Shimamura2,Fuyuki Shimojo2,Priya Vashishta1

University of Southern California1,Kumamoto University2

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5:00 PM - DS01.10.03
Fast Assessment of Metal Performances Through Dislocation Physics and Machine Learning

Jaehyun Cho1,2,William Tucker1,2,Kevin Wheeler1,Justin Haskins1

NASA Ames Research Center1,Analytical Mechanics Associates2

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5:00 PM - DS01.10.04
Calibrated Uncertainty for Molecular Property Prediction

Jonas Busk1,Peter Jørgernsen1,Arghya Bhowmik1,Mikkel Schmidt1,Ole Winther1,Tejs Vegge1

Technical University of Denmark1

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5:00 PM - DS01.10.05
Learning Interatomic Potentials from First Principles Data Using Symbolic Regression

Bilvin Varughese1,2,Sukriti Manna1,2,Troy Loeffler1,2,Rohit Batra2,Mathew Cherukara2,Subramanian Sakaranarayanan1,2

University of Illinois Chicago1,Argonne National Laboratory2

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5:00 PM - DS01.10.06
Motif-Based Graph Neural Networks for Predicting Quantum Molecular Properties

Pengyu Hong1,Yifei Wang1

Brandeis University1

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5:00 PM - DS01.10.07
Discovery of Structure-Property Relationships of Intercalated Graphite Compounds Using Machine Learning

Olivia Milavetz1,Mahit Dagar1,Tyler Gerstein1,Zachary Baughman1,Ford Hodgkins1,Henry Hunt1,Samantha Lehman1,Phillip Locke1,Elena Barker1,Daniel Carlebach1,Maddy Eatchel1,Anna Jiricko1,Yuchen Yang1,Natascha Knowlton1,2,Kaci Kuntz2,1

Rowland Hall1,The University of Utah2

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2022-05-12   Show All Abstracts

Times shown in HST (GMT-10:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.11: Simulation and Machine Learning IX
Session Chairs
Raymundo Arroyave
Mathieu Bauchy
Thursday AM, May 12, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

8:30 AM - DS01.11.01
Physics-Based Electronic Structure Theory Development Enabling Large-scale Materials Simulations

Jin Qian1,2,Qiang Xu1

Lawrence Berkeley National Laboratory1,California Institute of Technology2

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8:45 AM - DS01.11.03
Neuro-Symbolic Reinforcement Learning for Polymer Discovery

Sarathkrishna Swaminathan1,Dmitry Zubarev1,Tim Erdmann1,Subhajit Chaudhury1,Asim Munawar1

IBM Research1

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9:00 AM - DS01.11.04
Molecular Dynamics Simulations of Solid Electrolyte Interfaces with NequIP Equivariant Machine Learning Models

Juan Gomez1,Liwen Wan2,Simon Batzner1,Albert Musaelian1,Brandon Wood2,Boris Kozinsky1

Harvard University1,Lawrence Livermore National Laboratory2

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9:15 AM - DS01.11.05
Predicting the Dynamics of Atoms in Liquids by a Surrogate Machine-Learned Simulator

Mathieu Bauchy1,Han Liu1

University of California, Los Angeles1

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9:30 AM - DS01.11
BREAK


10:00 AM - DS01.11.06
Machine Learning Force Field for B-C Systems and Applications to Mechanical Deformation

Qi An1

University of Nevada, Reno1

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10:15 AM - DS01.11.07
Using Convolutional Neural Networks to Segment Scanning Electron Microscopy Images of Graphene

Aagam Shah1,Joshua Schiller1,Elif Ertekin1,Sameh Tawfick1

University of Illinois at Urbana-Champaign1

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10:30 AM - DS01.11.08
Physically-Informed Machine Learning Enhances Predictive Design of Fluorescent DNA-Stabilized Silver Clusters

Peter Mastracco1,Alexander Gorovitz2,Anna Gonzalez Rosell1,Joshua Evans3,Petko Bogdanov2,Stacy Copp1

University of California, Irvine1,State University of New York at Albany2,Chaffey College3

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DS01.12: Simulation and Machine Learning X
Session Chairs
N M Anoop Krishnan
Thursday PM, May 12, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

1:30 PM - *DS01.12.01
Towards Microstructure-Aware Autonomous Alloy Design

Raymundo Arroyave1,Abhilash Molkeri1,Danial Khatamsaz1,Richard Couperthwaite1,Jaylen James1,Douglas Allaire1,Ankit Srivastava1

Texas A&M University1

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2:00 PM - DS01.12.02
Study of HfO2 Phases Using Machine Learning Potentials

Sebastian Bichelmaier1,2,Jesús Carrete Montaña1,Georg K.H. Madsen1

Technical University of Vienna1,KAI GmbH2

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2:15 PM - DS01.12.03
Intelligent Design of Solid-State Mechanochemical Transformations for Supramolecular Structures

Jan Gröls1,Bernardo Castro-Dominguez1

University of Bath1

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2:30 PM - DS01.12.04
Cost-Efficient Training of a Neural Network Potential by Means of Active Learning for Fast and Accurate Molecular Dynamics Simulations

Sung-Ho Lee1,2,Valerio Olevano3,2,Benoit Sklénard1,2

CEA-Leti1,Université Grenoble Alpes2,CNRS, Institut Néel3

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2:45 PM - DS01.12
BREAK


3:15 PM - DS01.12.05
A-RAFFLE—The Search for New Materials

Joe Pitfield1,Steven Hepplestone1

University of Exeter1

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3:30 PM - DS01.12.06
Hierarchical Molecular Time Dynamics Models

Max Wilson1,Arghya Bhowmik1,Ole Winther1,Tejs Vegge1

DTU1

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3:45 PM - DS01.12.07
AI-Enhanced Manufacturing to Improve Material Formulations

Federico Zipoli1,Victor Viterbo1,Oliver Schilter1,Théophile Gaudin1,Leonid Kahle1,Teodoro Laino1

IBM Research Zurich1

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4:00 PM - DS01.12.08
The Identification of Transition Mechanism and Estimation of the rate of Atomic Rearrangements Accelerated with Gaussian Process Regression

Hannes Jonsson1,2

University of Iceland1,Faculty of Physical Sciences2

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4:15 PM - DS01.12.09
Machine Learning Assisted Modelling of a Ductile Fracture

Sandra Baltic1,Mohammad Zhian Asadzadeh1,Patrick Hammer2,Julien Magnien1,Hans-Peter Gänser1,Thomas Antretter3,René Hammer1

Materials Center Leoben Forschung GmbH1,Temple University2,Montanuniversität Leoben3

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2022-05-13   Show All Abstracts

Times shown in HST (GMT-10:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.13: Simulation and Machine Learning XI
Session Chairs
Badri Narayanan
Friday AM, May 13, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

8:30 AM - DS01.13.02
Unified Language of Synthesis Actions for Representation of Synthesis Protocols—Making Steps Toward Autonomous Materials Synthesis

Zheren Wang1,2,Kevin Cruse1,2,Yuxing Fei1,2,Ann Chia1,Yan Zeng2,Haoyan Huo1,2,Tanjin He1,2,Bowen Deng1,2,Olga Kononova1,Gerbrand Ceder1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2

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8:45 AM - DS01.13.03
Structure and Dielectric Properties of Aqueous LiOH Solutions Using Neural Network Quantum Molecular Dynamics

Ruru Ma1,Aravind Krishnamoorthy1,Nitish Baradwaj1,Ken-ichi Nomura1,Kohei Shimamura2,Pankaj Rajak1,Fuyuki Shimojo2,Aiichiro Nakano1,Rajiv Kalia1,Priya Vashishta1

University of Southern California1,Kumamoto University2

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9:00 AM - DS01.13.04
Large-Scale Dynamics Simulations of Complex Liquid Electrolytes with NequIP Equivariant Machine Learning

Nicola Molinari1,2,Albert Musaelian1,Simon Batzner1,Boris Kozinsky1,2

Harvard University1,Robert Bosch LLC2

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9:15 AM - DS01.13.05
A Reinforcement Learning-Based Approach to find the Global Minimum of Atomically Precise Nanoclusters

Sukriti Manna1,Suvo Banik1,Troy Loeffler1,Subramanian Sankaranarayanan1

Argonne National Laboratory1

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9:30 AM - DS01.13
BREAK


10:00 AM - DS01.13.06
Automation to Improve the Research Process via Human-Robot Interactions

Anesia Auguste1,2,Jennifer Ruddock1,2,Erick Braham2,Ezra Ameperosa2,Andrew Gillman2

UES, Inc.1,Air Force Research Laboratory2

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10:15 AM - DS01.13.07
Efficient Multiscale Multiphysics Modeling with Machine Learning Based Surrogate Models

Joshua Stuckner1

NASA Glenn Research Center1

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10:30 AM - DS01.13.08
Exploring Polymer Degradation Pathways Using Reinforcement Learning and Monte Carlo Tree Search

Rohit Batra1,Aditya Koneru2,Suvo Banik2,Henry Chan1,Sukriti Manna2,Jie Xu1,Subramanian Sankaranarayanan1,2

Argonne National Laboratory1,University of Illinois at Chicago2

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10:45 AM - DS01.13.09
Predicting Indium Phosphide Quantum Dot Properties Using Machine Learning on Synthetic Procedures

Hao Nguyen1,Florence Dou1,Nayon Park1,Shenwei Wu1,Brandi Cossairt1

University of Washington1

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DS01.14: Simulation and Machine Learning XII
Session Chairs
N M Anoop Krishnan
Subramanian Sankaranarayanan
Friday PM, May 13, 2022
Hawai'i Convention Center, Level 3, Lili'U Theater, 310

1:30 PM - DS01.14.01
Deep Learning Techniques for Integrated Circuit Die Performance Prediction

Alexander Kovalenko1,2,Petr Lenhard1,Radomír Lenhard1

Inference Technologies1,Czech Technical University in Prague2

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1:45 PM - DS01.14.02
Understanding Self-Assembly Behavior with Self-Supervised Learning

Matthew Spellings1,Maya Martirossyan2,Julia Dshemuchadse2

Vector Institute1,Cornell University2

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2:00 PM - DS01.14.03
AI Physicist—Data-Driven Discovery of Mathematical Expressions via Natural Language Processing

Juwon Na1,Seungchul Lee1

Pohang University of Science and Technology1

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2:15 PM - DS01.14.04
Deep Learning-Based Prediction of Electrical Properties of Polymers with Feature Extraction of Process Conditions

Hajime Shimakawa1,Akiko Kumada1,Masahiro Sato1

The University of Tokyo1

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2:30 PM - DS01.14
BREAK


3:00 PM - DS01.14.05
Multi-Property Prediction of Polymers and Exploration of Optimal Polymer Structures with Deep Learning

Hajime Shimakawa1,Chihiro Tateyama1,Masahiro Sato1,Akiko Kumada1

The University of Tokyo1

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3:15 PM - DS01.14.06
Data-Driven Prediction of CO2 Absorption Performances of Aqueous Amine Solutions via Multi-Task Transfer Learning

Yuta Aoki1

The Institute of Statistical Mathematics1

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3:30 PM - DS01.14.08
Informing Experiments Through Visualization and Machine-learned Representations of Text-Mined Materials Synthesis Conditions

Kevin Cruse1,2,Sanghoon Lee1,2,Viktoriia Baibakova1,2,Amalie Trewartha2,Christopher Bartel2,Maged Abdelsamie2,Kootak Hong2,Zheren Wang1,2,Haoyan Huo1,2,Tanjin He1,2,Olga Kononova2,Carolin Sutter-Fella2,Anubhav Jain2,Gerbrand Ceder1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2

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3:45 PM - DS01.14.09
Structure and Dynamics of Supercritical Water Determined with Neural Network Quantum Molecular Dynamics

Nitish Baradwaj1,Aravind Krishnamoorthy1,Ken-ichi Nomura1,Kohei Shimamura2,Aiichiro Nakano1,Rajiv Kalia1,Priya Vashishta1

University of Southern California1,Kumamoto University2

Show Abstract

2022-05-23   Show All Abstracts

Times shown in EDT (GMT-4:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.15: Simulation and Machine Learning XIII
Session Chairs
Mathew Cherukara
Badri Narayanan
Monday AM, May 23, 2022
DS01-Virtual

10:30 AM - *DS01.15.01
Modelling of Complex Energy Materials with Machine Learning

Nongnuch Artrith1

Debye Institute for Nanomaterials Science1

Show Abstract

11:00 AM - DS01.15.02
Optimization of Superconductors Fabrication by High-Throughput Experimentation and Machine Learning

Albert Queraltó1,Kapil Gupta1,Adrià Pacheco1,Lavinia Saltarelli1,Diana Franco1,Nerea Jiménez1,Pablo Gallego1,Cristian Mocuta2,Susagna Ricart1,Xavier Obradors Berenguer1,Teresa Puig1

ICMAB-CSIC1,SOLEIL Synchrotron2

Show Abstract

11:15 AM - DS01.15.03
Identification of Enzymatic Active Sites with Unsupervised Language Modelling

Matteo Manica1,Loïc Kwate Dassi1,Daniel Probst1,Philippe Schwaller1,Yves Nana Teukam1,Teodoro Laino1

IBM Research Europe1

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11:30 AM - DS01.15.04
Regression Transformer—Blending Numerical and Textual Tokens for Concurrent Property Prediction and Conditional Generation

Jannis Born1,2,Matteo Manica1

IBM Research Europe1,ETH Zürich2

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11:45 AM - DS01.15.05
Disambiguation of Amorphous Magnetic Microwire Signatures

Akshar Varma1,Xiaoyu Zhang1,Brian Lejeune1,Laura Cebada Almagro2,Rafael Perez del Real3,4,M. Pilar Marin Palacios2,3,O. Fitchorova1,Laura Lewis1,Ravi Sundaram1

Northeastern University1,Complutense University of Madrid2,Unidad Asociada (CSIC)3,Instituto de Ciencia de Materiales de Madrid4

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12:00 PM - DS01.15.06
Data-Driven Approaches for Defect Concentration Prediction of Microwave-Synthesized TiO2

Shuyan Zhang1,Jie Gong1,B. Reeja Jayan1,Alan McGaughey1

Carnegie Mellon University1

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12:15 PM - DS01.15.07
Strain Engineering of Monolayer MoS2 on SiO2 Substrate by Developing a Neural Network Interatomic Potential Based on Density Functional Theory

Ali Barooni1,Mahdi Shirazi2,Ehsan Hosseinian1

University of Tehran1,University of Amsterdam2

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12:20 PM - DS01.15.08
Application of Radiation Detection Materials for Radiation Source Mapping with Machine Learning

Ryotaro Okabe1,Tongtong Liu1,Shangjie Xue1,Lin-wen Hu1,Mingda Li1

Massachusetts Institute of Technology1

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12:25 PM - DS01.15.09
Long Time-Scale Accuracy of Neural Network Potentials in Molecular Dynamics Simulations

Difan Zhang1,Stefan Dernbach1,Zhao Chen1,Ethan Herron1,Robert Rutherford1,Aaron Tuor1,Jan Drgona1,Draguna Vrabie1,Vanda Glezakou1,Roger Rousseau1

Pacific Northwest National Laboratory1

Show Abstract

DS01.16: Simulation and Machine Learning XIV
Session Chairs
Mathew Cherukara
Grace Gu
Monday PM, May 23, 2022
DS01-Virtual

1:00 PM - *DS01.16.01
Controlled Conjugated Polymer Assembly by Autonomous Solution-Processing Platform

Jie Xu1

Argonne National Lab1

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1:30 PM - DS01.16.02
Design of Graphene-Based Anhydrous Proton Conducting Materials Using Deep Learning Methods

Siddarth Achar1,Leonardo Bernasconi1,Linfeng Zhang2,J. Karl Johnson1

University of Pittsburgh1,Beijing Institute of Big Data Research2

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1:45 PM - DS01.16.03
Deep Neural Networks for Predicting Formation Energy and Synthesizability of Crystal Structures

Ali Davariashtiyani1,Zahra Kadkhodaie2,Sara Kadkhodaei1

University of Illinois at Chicago1,New York University2

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2:00 PM - DS01.16.04
Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High State of Charge (x < 0.25)

Juan Garcia1,Joshua Gabriel1,Noah Paulson1,John Low1,Marius Stan1,Hakim Iddir1

Argonne National Laboratory1

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2:15 PM - DS01.16.05
Accelerating the Prediction of Large Carbon Clusters via Structure Search—Evaluation of Machine-Learning and Classical Potentials

Bora Karasulu1,2,Jean-Marc Leyssale3,Patrick Rowe2,4,Cedric Weber5,Carla de Tomas2

University of Warwick1,Happy Electron Ltd.2,University of Bordeaux3,University of Cambridge4,King's College London5

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2:30 PM - DS01.16.06
On-Demand Generation of Large Polymer Datasets for Accelerated Materials Discovery

Pedro Arrechea1,Dmitry Zubarev1,James Hedrick1,Nathaniel Park1,Tim Erdmann1

IBM1

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2:45 PM - DS01.16.07
Finite-temperature Crystal Structure Prediction of Lithium Using Machine Learning Potentials

James Chapman1,Stanimir Bonev1

Lawrence Livermore National Laboratory1

Show Abstract

DS01.17: Simulation and Machine Learning XV
Session Chairs
Mathew Cherukara
N M Anoop Krishnan
Monday PM, May 23, 2022
DS01-Virtual

6:30 PM - *DS01.17.01
Inverse Design of Silver Nanoparticles Using Multi-Target Machine Learning

Amanda Barnard1,Sichao Li1

Australian National University1

Show Abstract

7:00 PM - *DS01.17.02
Smart Systems Engineering Contributing to the Life Cycle of Material Discovery and a Net-Zero Future

Xiaonan Wang1,2

Tsinghua University1,National University of Singapore2

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7:30 PM - *DS01.17.03
Robust Topological Designs for Extreme Metamaterial Micro-Structures

Souvik Chakraborty1

IIT Delhi1

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8:00 PM - DS01.17.04
Computing Device Signatures in Resistive-Switching Memory Materials—Utilization of Machine Learning

Shao Xiang Go1,Qiang Wang1,Bo Wang1,Yu Jiang1,Natasa Bajalovic1,Desmond K. Loke1

Singapore University of Technology and Design1

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8:05 PM - DS01.11.02
Machine Learning the Scaling Property of Density Functionals via Data Augmentation

Weiyi Gong1,Tao Sun2,Peng Chu1,Hexin Bai1,Anoj Aryal1,Shah Tanvir-Ur-Rahman Chowdhury1,Jie Yu1,Haibin Ling2,John Perdew1,Qimin Yan1

Temple University1,Stony Brook University, The State University of New York2

Show Abstract

8:20 PM - *DS01.13.01
Reinforcement Learning for Inverse Design of Materials

Subramanian Sankaranarayanan1

Argonne National Laboratory1

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2022-05-24   Show All Abstracts

Times shown in EDT (GMT-4:00)

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Mathew Cherukara, Argonne National Laboratory
Grace Gu, Massachusetts Institute of Technology
Badri Narayanan, University of Louisville
DS01.18: Simulation and Machine Learning XVI
Session Chairs
Mathew Cherukara
Jie Xu
Tuesday AM, May 24, 2022
DS01-Virtual

10:30 AM - *DS01.18.01
Discovering Interactions Laws of Multiparticle Systems with Lagrangian Neural Networks

N M Anoop Krishnan1,Ravinder Bhattoo1,Sayan Ranu1

Indian Institute of Technology Delhi1

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11:00 AM - DS01.18.02
Images as Molecular Descriptors for Materials Discovery

Matthew Wilkinson1,Uriel Martinez Hernandez1,Bernardo Castro-Dominguez1

University of Bath1

Show Abstract

11:15 AM - DS01.18.03
Deep Reinforcement Learning for Autonomous Discovery of Atomic Transition Pathways

Bjarke Hastrup1,Jonas Busk1,Peter Jørgernsen1,Tejs Vegge1,Arghya Bhowmik1

Technical University of Denmark1

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11:30 AM - DS01.18.04
Achieving Machine Learning Generalizability Using Out-of-Domain Prediction of Adsorption Energies on High-Entropy Alloys

Ritesh Kumar1,Abhishek Singh1

Indian Institute of Science1

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11:45 AM - DS01.18.05
Atomistic Simulation of Plasmonic Hot Carrier Dynamics Using Machine Learning

Adela Habib1,Benjamin Nebgen1,Nicholas Lubbers1,Sergei Tretiak1

Los Alamos National Laboratory1

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12:00 PM - *DS01.18.06
Predicting New Materials that Exhibit Magnetocaloric Effects Using Concerted Text-Mining and Machine-Learning with Computational Screening

Jacqueline Cole1,2

University of Cambridge1,ISIS Pulsed Neutron and Muon Source2

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