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MRS is making frequent changes to the program, which is updated daily. Please check back for updates. Presenters: please refer to official email correspondence from MRS Meetings to confirm your presentation date and time.  Please contact meetings@mrs.org if there are discrepancies.

Symposium DS03—Combining Machine Learning with Simulations for Materials Modeling

2021-11-30   Show All Abstracts

Symposium Organizers

N M Anoop Krishnan, Indian Institute of Technology Delhi
Mathieu Bauchy, University of California, Los Angeles
Sumanta Das, University of Rhode Island
Christian Hoover, Arizona State University
DS03.01: Machine Learning for Accelerating Simulations I
Session Chairs
Mathieu Bauchy
Tuesday AM, November 30, 2021
Sheraton, 5th Floor, The Fens

10:30 AM - *DS03.01.01
Bioinspired Artificial Intelligence and Protein Materials by Design

Markus Buehler1

Massachusetts Institute of Technology1

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11:00 AM - DS03.01.03
Per-Atom Magnetic Moment Prediction in Transition Metal Oxides

Jaclyn Lunger1,Jessica Karaguesian1,Daniel Schwalbe Koda1,Yang Shao-Horn1,Rafael Gómez-Bombarelli1

Massachusetts Institute of Technology1

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11:15 AM - DS03.01.04
Benchmarking Descriptors, Models and Systems for Many-Body Machine Learned Force Fields in Molten Transition Metals

Steven Torrisi1,2,Cameron Owen2,Isabel Diersen2,Lixin Sun2,Jin Soo Lim2,Yu Xie2,Jonathan Vandermause2,Boris Kozinsky2,3

Toyota Research Institute1,Harvard University2,Bosch3

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DS03.02: Machine Learning for Targeted Material Design I
Session Chairs
Sumanta Das
Christian Hoover
Tuesday PM, November 30, 2021
Sheraton, 5th Floor, The Fens

1:30 PM - DS03.02.01
Integrating High-Performance Computing and Machine Learning-Enabled Design of Materials

Nathan Frey1,Lin Li1,Vijay Gadepally1

Massachusetts Institute of Technology1

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1:45 PM - DS03.02.02
Hybrid Multi-Scale and Data-Driven Models for Designing the Smart Properties of Nanocomposite Materials

Matteo Fasano1,Atta Muhammad1,Rajat Srivastava1,Eliodoro Chiavazzo1,Pietro Asinari1,2

Politecnico di Torino1,Istituto Nazionale di Ricerca Metrologica2

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2:00 PM - DS03.02.03
Transforming Automated Quantum Chemistry Calculation Workflows with Machine Learning—Towards Faster and More Accurate Materials Discovery

Chenru Duan1,Heather Kulik1

Massachusetts Institute of Technology1

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2:15 PM - DS03.02.04
Davis Computational Spectroscopy Workflow—From Structure to Spectra

Lucas Samir Ramalho Cavalcante1,Luke Daemen2,Nir Goldman3,Ambarish Kulkarni1,Adam Moule1

University of California, Davis1,Oak Ridge National Laboratory2,Lawrence Livermore National Laboratory3

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2:30 PM - DS03.02.05
Materials from Fire—A New Approach to Design Material from Nature

Markus Buehler1,Mario Milazzo1

Massachusetts Institute of Technology1

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2:45 PM - DS03.02.07
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning

Arun Kumar Mannodi Kanakkithodi1,Maria Chan2,Xiaofeng Xiang3,Laura Jacoby3,Robert Biegaj3,Rishi Kumar4,David Fenning4

Purdue University1,Argonne National Laboratory2,University of Washington3,University of California, San Diego4

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3:00 PM - DS03.02.08
AI-Aided Interpretation of Electronic Transport Measurements

Luca Bonaldo1,Terry Stearns1,Ilaria Siloi2,Nicholas Mecholsky3,Marco Fornari1

Central Michigan University1,University of Southern California2,Catholic University of America3

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3:15 PM - DS03.02.06
De Novo Inverse Design of Nanoporous Materials by Machine Learning

Mathieu Bauchy1

University of California, Los Angeles1

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3:30 PM - DS03.02
Break


4:00 PM - DS03.02.09
Reconstructing Representative Dislocation Structures from XRD Measurements Through Machine Learning of Discrete Dislocation Dynamics Simulations

Dylan Madisetti1,Christopher Stiles2,1,Jaafar El-Awady1

Johns Hopkins University1,Johns Hopkins University Applied Physics Laboratory2

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4:15 PM - DS03.02.10
High-Throughput Glass Transition Temperature Computations for Polymers Using Machine Learning Based Molecular Dynamics

Christopher Kuenneth1,Kuan-Hsuan Shen1,Rampi Ramprasad1

Georgia Institute of Technology1

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4:30 PM - DS03.02.12
Tailoring Deep Learning Neural Networks for Atomic Column and Nanoparticle Segmentation in HR-TEM Data

Matthew Helmi Leth Larsen1,William Bang Lomholdt1,Anders Siig Dreisig1,Stig Helveg1,Ole Winther1,Thomas Hansen1,Jakob Schiotz1

Technical University of Denmark1

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4:45 PM - DS03.02.13
Machine Learning for Grain Boundary Solute Segregation

Malik Wagih1,Christopher Schuh1

Massachusetts Institute of Technology1

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2021-12-01   Show All Abstracts

Symposium Organizers

N M Anoop Krishnan, Indian Institute of Technology Delhi
Mathieu Bauchy, University of California, Los Angeles
Sumanta Das, University of Rhode Island
Christian Hoover, Arizona State University
DS03.03: Machine Learning for Targeted Material Design II
Session Chairs
Sumanta Das
Christian Hoover
Wednesday AM, December 1, 2021
Sheraton, 5th Floor, The Fens

10:30 AM - DS03.03.01
Computational Discovery and Informatics-Assisted Classification of Double Spinel Compounds

Ghanshyam Pilania1,Vancho Kocevski1,Blas Uberuaga1

Los Alamos National Laboratory1

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10:45 AM - DS03.03.02
Ensemble Neural Network for Lithium Dendrite Growth

Issei Nakamura1

Michigan Technological University1

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11:00 AM - DS03.03.03
It Can Play More Than Just Board Games—Applying Monte Carlo Tree Search Toward Materials Optimization Problems in a Continuous Action Space

Troy Loeffler1,Sukriti Manna1,Rohit Batra1,Henry Chan1,Srilok Srinivasan1,Subramanian Sankaranarayanan1

Argonne National Laboratory1

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11:15 AM - DS03.03.04
A Deep Learning Augmented Genetic Algorithm Approach for 2D Fracture Discovery and Design

Andrew Lew1,Markus Buehler1

Massachusetts Institute of Technology1

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11:30 AM - DS03.03.05
Multi Reward Reinforcement Learning Based Bond Order Potential to Study Strain Assisted Phase Transitions in Phosphorene

Aditya Koneru1,2,Rohit Batra2,Sukriti Manna1,2,Troy Loeffler1,2,Henry Chan1,2,Harpal Singh3,Mathew Cherukara2,Subramanian Sankaranarayanan1,2

University of Illinois at Chicago1,Argonne National Laboratory2,Sentient Science Corporation3

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11:45 AM - DS03.03.06
Graph Neural Networks for an Accurate and Interpretable Prediction of the Properties of Polycrystalline Materials

Minyi Dai1,Mehmet F Demirel1,Yingyu Liang1,Jiamian Hu1

University of Wisconsin-Madison1

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DS03.04: Machine Learning for Accelerating Simulations II
Session Chairs
Mathieu Bauchy
Wednesday PM, December 1, 2021
Sheraton, 5th Floor, The Fens

2:00 PM - DS03.04.02
A Machine Learning Correction to DFTB and Its Effectiveness on Charge Transfer Salts

Corina Magdaleno1,Lucas Cavalcanté1,Makena Dettmann1,Karina Masalkovaité1

University of California, Davis1

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2:15 PM - DS03.04.03
A Machine Learning Approach for Longitudinal Spin Fluctuation Effects in bcc Fe at TC and Under Earth’s Core Conditions

Marian Arale Brännvall1,Davide Gambino1,Rickard Armiento1,Bjorn Alling1

Linköping University1

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2:30 PM - DS03.04.04
Late News: Accelerated Prediction of Atomically Precise Cluster Structures Using On-the-Fly Machine Learning

Yunzhe Wang1,Shanping Liu1,Peter Lile1,Sam Norwood1,Alberto Hernandez1,Sukriti Manna1,Tim Mueller1

Johns Hopkins University1

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2021-12-02   Show All Abstracts

Symposium Organizers

N M Anoop Krishnan, Indian Institute of Technology Delhi
Mathieu Bauchy, University of California, Los Angeles
Sumanta Das, University of Rhode Island
Christian Hoover, Arizona State University
DS03.05: Machine Learning for Accelerating Simulations III
Session Chairs
Sumanta Das
Thursday AM, December 2, 2021
Sheraton, 5th Floor, The Fens

10:30 AM - DS03.05.01
Active Learning of Many-Body Bayesian Potentials for Large-Scale Simulations of Phase Transformations and Thermal Transport

Yu Xie1,Jonathan Vandermause1,Senja Ramakers2,3,Nakib Protik1,4,Anders Johansson1,Boris Kozinsky1,5

Harvard University1,Robert Bosch GmbH2,Ruhr-Universität Bochum3,Catalan Institute of Nanoscience and Nanotechnology (ICN2)4,Robert Bosch LLC5

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10:45 AM - DS03.05.02
End to End Force Field Parametrization for Polymer Electrolytes Using Machine Learning

Pablo Leon1,Rafael Gómez-Bombarelli1

Massachusetts Institute of Technology1

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11:00 AM - DS03.05.03
Development of Deep and RF-MEAM Potentials to Model Physical and Thermo-Mechanical Properties of Metal-Rich Carbides

Tyler McGilvry-James1,Bikash Timalsina1,Andrew Duff2,Nirmal Baishnab3,Puja Adhikari4,Saro San4,Wai-Yim Ching4,Ridwan Sakidja1

Missouri State University1,STFC Daresbury Laboratory2,Iowa State University3,University of Missouri–Kansas City4

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11:15 AM - DS03.05.04
Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions

Ramin Ansari1,Amirata Ghorbani2,John Kieffer1

University of Michigan1,Stanford University2

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11:30 AM - DS03.05.05
Late News: Insights from Computational Studies on the Anisotropic Volume Change of Ni-Rich Cathodes at High State of Charge Using a Chemistry Informed Machine Learning Model

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

Argonne National Laboratory1

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11:45 AM - DS03.05.06
Late News: Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces

Siddarth Achar1,2,Derek Stewart2,Julian Schneider3

University of Pittsburgh1,Western Digital Corporation2,Synopsys Inc.3

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12:00 PM - DS03.05.07
Late News: (Garcia High School Student) Performance Analysis of an AI-Guided Coarse-Graining Methodology for More Efficient Protein Modeling

Raaghav Malik1,Ziji Zhang2,Miriam Rafailovich2,Marcia Simon2,Yuefan Deng2,Peng Zhang2

Columbus Academy1,Stony Brook University, The State University of New York2

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DS03.06: Machine Learning for Accelerating Simulations IV
Session Chairs
Mathieu Bauchy
Christian Hoover
Thursday PM, December 2, 2021
Sheraton, 5th Floor, The Fens

1:45 PM - DS03.06.03
NequIP—E(3)-Equivariant Convolutions Enable Sample-Efficient, Scalable and Highly Accurate Machine Learning Interatomic Potentials

Simon Batzner1,Albert Musaelian1,Lixin Sun1,Tess Smidt2,Mario Geiger3,Jonathan Mailoa4,Mordechai Kornbluth4,Nicola Molinari1,Boris Kozinsky1

Harvard University1,Lawrence Berkeley National Laboratory2,EPFL3,Robert Bosch Research and Technology Center4

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2:00 PM - DS03.06.02
DICE—A Linear-Scaling N-Body Interatomic Potential from E(3)-Equivariant Convolutions

Albert Musaelian1,Simon Batzner1,Lixin Sun1,Steven Torrisi1,Boris Kozinsky1

Harvard University1

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2:15 PM - DS03.06.04
Differentiable Sampling of Molecular Geometries with Uncertainty-Based Adversarial Attacks

Aik Rui Tan1,Daniel Schwalbe Koda1,Rafael Gómez-Bombarelli1

Massachusetts Institute of Technology1

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2:30 PM - DS03.06.05
Complex Dynamics of the CO/Pt Interaction from Bayesian Active Learning Simulations

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

Harvard University1

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2021-12-06   Show All Abstracts

Symposium Organizers

N M Anoop Krishnan, Indian Institute of Technology Delhi
Mathieu Bauchy, University of California, Los Angeles
Sumanta Das, University of Rhode Island
Christian Hoover, Arizona State University
DS03.07: Machine Learning for Accelerating Simulations V
Session Chairs
Sumanta Das
N M Anoop Krishnan
Monday AM, December 6, 2021
DS03-Virtual

8:00 AM - *DS03.07.01
Four Generations of Neural Network Potentials for Materials Science

Jörg Behler1

University of Göttingen1

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8:30 AM - DS03.07.02
Local Gaussian Process Regression for Interatomic Potentials

Spencer Hill1,Tucker Carrington1,Sergei Manzhos2,Manabu Ihara2

Queen’s University1,Tokyo Institute of Technology2

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8:45 AM - DS03.07.03
Development of SNAP Machine Learned Interatomic Potentials for Materials in Extreme Environments

Mary Alice Cusentino1,Mitchell Wood1,Aidan Thompson1

Sandia National Laboratories1

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9:00 AM - DS03.07.04
Machine Learning in Multiscale Mechanics of Materials

Huck Beng Chew1,Yue Cui1

University of Illinois at Urbana-Champaign1

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9:15 AM - DS03.07.05
Late News: (Garcia High School Student) MR-Net—Multi-Representation Learning for Protein-Ligand Binding Affinity Prediction

Emirhan Kurtulus1,Bernard Essuman2,Ziji Zhang2,Miriam Rafailovich2,Marcia Simon2,Yuefan Deng2,Peng Zhang2

Cagaloglu Anatolian High School1,Stony Brook University, The State University of New York2

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9:30 AM - DS03.07.06
MorpheusFF—A Chemical Approach to the Force Field of Amorphous Solids and of Liquids

Isaías Rodríguez1,David Hinojosa1,Renela Valladares2,Ariel Valladares1

Instituto de Investigaciones en Materiales, UNAM1,Facultad de Ciencias, UNAM2

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9:45 AM - DS03.07.07
Accelerated Crystal Structure Search for Multinary Phases—Introduction to SPINNER Framework and Its Application

Wonseok Jeong1,Sungwoo Kang1,Changho Hong1,Seungwoo Hwang1,Younchae Yoon1,Youngho Kang2,Seungwu Han1

Seoul National Univ1,Incheon National Univ2

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DS03.08: Machine Learning for Accelerating Simulations VI
Session Chairs
Mathieu Bauchy
N M Anoop Krishnan
Monday AM, December 6, 2021
DS03-Virtual

10:30 AM - *DS03.08.01
Data-Driven Atomistic Models for the Simulation of Material Failure

James Kermode1

University of Warwick1

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11:00 AM - DS03.08.02
Machine Learning Based Atomistic Simulation Approach for Accurate Prediction of Cation Distribution in Complex Spinel Oxides

Guofeng Wang1,Ying Fang1,Siming Zhang1

University of Pittsburgh1

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11:15 AM - DS03.08.03
Machine Learning at the Exascale for Atomistic Simulations with Improved Accuracy, Length and Time Scales

Mitchell Wood1,Aidan Thompson1

Sandia National Laboratories1

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11:30 AM - DS03.08.04
Deep Learning Methods for Fast Prediction of Protein Vibrational Modes

Darnell Granberry1,Kai Guo1,Markus Buehler1

MIT Lab for Atomistic and Molecular Modeling1

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11:45 AM - DS03.08.05
Late News: Machine Learing Accelerates Atomistic Dynamics Simulations for Single-Molecule Photodynamics and Multi-Scale Modeling of Organic Solids

Patrick Reiser1,Jingbai Li2,Manuel Konrad1,Steven Lopez2,Wolfgang Wenzel1,Pascal Friederich1

Karlsruhe Institute of Technology1,Northeastern University2

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12:00 PM - DS03.08.06
Late News: Characterizing Possible Failure Modes in Physics-Informed Neural Networks

Aditi Krishnapriyan1,2,Amir Gholami1,Shandian Zhe3,Robert Kirby3,Michael Mahoney1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2,The University of Utah3

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12:15 PM - DS03.01.02
Molecular Dynamics Simulation Using Graph Neural Networks

Ravinder Bhattoo1,Sayan Ranu1,N M Anoop Krishnan1

Indian Institute of Technology Delhi1

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DS03.09: Machine Learning for Accelerating Simulations VII
Session Chairs
Mathieu Bauchy
Christian Hoover
Monday PM, December 6, 2021
DS03-Virtual

4:00 PM - *DS03.09.01
Automatically Interpreting Materials Characterization Data to Model Materials Properties Using Machine Learning

Jacqueline Cole1,2

University of Cambridge1,ISIS Pulsed Neutron and Muon Source2

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4:30 PM - DS03.09.02
Data-Driven Design of Switchable Nanomaterials Composed of Polarizable Nanoparticles

Siva Dasetty1,Igor Coropceanu1,Jiyuan Li1,Juan J. dePablo1,Dmitri Talapin1,Andrew Ferguson1

The University of Chicago1

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4:45 PM - DS03.09.03
A Hierarchical Design on Bioinspired Structural Composites Using Reinforcement Learning

Chi Hua Yu1,2,Bor-Yann Tseng3,Cheng-Che Tung3,Zhenze Yang2,Elena Zhao4,Po-Yu Chen3,Chuin-Shan Chen5,Markus Buehler2

National Cheng Kung University1,Massachusetts Institute of Technology2,National Tsing Hua University3,Deerfield Academy4,National Taiwan University5

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5:00 PM - DS03.09.04
Machine-learned Potential Energy Model for Extensive Sampling of the Microstructure of Complex Mixed-Ion Perovskite

Hsin-An Chen1,Chun-Wei Pao1

Academia Sinica1

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5:15 PM - DS03.09.05
Accelerating Lattice Thermal Conductivity Calculations with Neural Network Potentials

Kyeongpung Lee1,Jeong Min Choi1,Wonseok Jeong1,Jaehoon Kim1,Seungwu Han1

Seoul National University1

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5:30 PM - DS03.09.06
Late News: Structured Gaussian Process Regression Models to Address Difficulties in Modeling Very High Dimensional Data with Product Kernels

Eita Sasaki1,Manabu Ihara1,Sergei Manzhos1

Tokyo Institute of Technology1

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5:45 PM - DS03.09.07
Nanocrystalline Microstructure Modulation Based on Reinforcement Learning

Hao Sun1

Queen's University1

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DS03.10: Machine Learning for Accelerating Simulations VIII
Session Chairs
Sumanta Das
Christian Hoover
Monday PM, December 6, 2021
DS03-Virtual

6:30 PM - *DS03.10.01
Machine Learning an Energy and Density-Optimized Exchange-Correlation Functional in DFT

Marivi Fernandez-Serra1

Stony Brook University1

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7:00 PM - DS03.10.02
A Machine-Learned Potential Energy Model for Large-Scale Atomistic Simulation of 2D Ruddlesden-Popper Perovskite Material

Svetozar Najman1,2,Po Yu Yang1,Chun-Wei Pao1

Research Center for Applied Sciences, Academia Sinica1,National Tsing Hua University2

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7:15 PM - DS03.10.03
Quantum-Accurate Machine-Learned Potential Model for Large-Scale Atomistic Simulations of Mechanical Properties Chemically Complex Alloy

Po Yu Yang1,Cheng-Lun Wu1,Chun-Wei Pao1

Academia Sinica1

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7:30 PM - DS03.10.04
Development of Coarse-Grained Models of Carbohydrates

Parisa Farzeen1,Soumil Joshi1,Sanket Deshmukh1

Virginia Tech1

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7:45 PM - DS03.10.05
Decision Trees in Continuous Action Space for Determination of High Dimensional Potential Energy Surfaces

Sukriti Manna1,Troy Loeffler2,Rohit Batra1,Suvo Banik2,Henry Chan1,Subramanian Sankaranarayanan2

Argonne National Laboratory1,University of Illinois at Chicago2

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8:00 PM - DS03.10.06
Machine Learned Interatomic Potentials for the Calculation of the Detonation State of Energetic Materials with Quantum Accuracy

Cong Huy Pham1,Nir Goldman1,Laurence Fried1

Lawrence Livermore National Laboratory1

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8:15 PM - DS03.10.07
Low-Dimensional Manifolds Underpin the Atomic Structure of Glassy Materials

Thomas Hardin1,Mark Wilson1,Michael Shields2

Sandia National Laboratories1,Johns Hopkins University2

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2021-12-07   Show All Abstracts

Symposium Organizers

N M Anoop Krishnan, Indian Institute of Technology Delhi
Mathieu Bauchy, University of California, Los Angeles
Sumanta Das, University of Rhode Island
Christian Hoover, Arizona State University
DS03.11: Machine Learning for Targeted Material Design III
Session Chairs
Christian Hoover
N M Anoop Krishnan
Tuesday AM, December 7, 2021
DS03-Virtual

8:00 AM - *DS03.11.01
Computational Materials Modeling with Integrated Machine Learning

Michele Ceriotti1

EPFL1

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8:30 AM - DS03.11.02
A Machine Learning-Based 3D Battery Design Method

Kaito Miyamoto1,2,Scott Broderick1,Krishna Rajan1

University at Buffalo, The State University of New York1,Toyota Research Institute of North America2

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8:45 AM - DS03.11.03
PSP—A Toolkit for Efficient Generation of 3D Atomic-Level Polymer Models

Harikrishna Sahu1,Huan Tran1,Kuan-Hsuan Shen1,Joseph H. Montoya2,Ramamurthy Ramprasad1

Georgia Institute of Technology, USA1,Toyota Research Institute2

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9:00 AM - DS03.11.04
Inverse Design of Two-Dimensional Materials with Invertible Neural Networks

Victor Fung1,Jiaxin Zhang1,Guoxiang Hu2,Panchapakesan Ganesh1,Bobby Sumpter1

Oak Ridge National Laboratory1,The City University of New York2

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9:15 AM - DS03.11.05
Gaussian Process Regression Acceleration of Mechanism and Transition Rate Calculations

Hannes Jonsson1

University of Iceland1

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9:30 AM - DS03.11.06
Late News: Analogical Materials Discovery—An Unsupervised Learning Approach for Inverse Design of Disordered Perovskite Materials

Achintha Ihalage1,Yang Hao1

Queen Mary University of London1

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9:45 AM - DS03.02.11
Machine Learning Models to Predict Defect Properties in High Entropy Alloys

Dilpuneet Aidhy1,Gaurav Arora1,Anus Manzoor1

University of Wyoming1

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DS03.12: Machine Learning for Targeted Material Design IV
Session Chairs
Mathieu Bauchy
Sumanta Das
Tuesday PM, December 7, 2021
DS03-Virtual

1:00 PM - *DS03.12.01
AI-Enabled Nanoscale Imaging

Mathew Cherukara1

Argonne National Laboratory1

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1:30 PM - DS03.12.02
Deep Learning Model to Predict Complex Stress and Strain Fields in Hierarchical Composites

Zhenze Yang1,Chi Hua Yu1,Markus Buehler1

Massachusetts Institute of Technology1

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1:45 PM - DS03.12.04
A Deep Learning Approach to the Inverse Problem of Modulus Identification in Elasticity

Bo Ni1,2,Huajian Gao3

Brown University1,Massachusetts Institute of Technology2,Nanyang Technological University3

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2:00 PM - DS03.12.05
Predicting Properties of BCC Refractory Multicomponent Alloys Using Physics Informed Statistical Learning

Liang Qi1,Yong-Jie Hu1,2

Univ of Michigan1,Drexel University2

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2:15 PM - *DS03.12.06
Data-Driven Insights into Materials Design

Abhishek Singh1

Indian Institute of Science1

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2:45 PM - DS03.06.01
Predicting Ground State and Metastable Crystal Structures Using Elemental and Phonon Mode Descriptors

Aria Mansouri Tehrani1,Bastien Grosso1,Ramon Frey1,Nicola Spaldin1

ETH Zurich1

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DS03.13: Machine Learning for Targeted Material Design V
Session Chairs
Mathieu Bauchy
Christian Hoover
Tuesday PM, December 7, 2021
DS03-Virtual

9:00 PM - *DS03.13.01
Graph-Based Neural ODEs for Learning Dynamical Systems

Yizhou Sun1,Zijie Huang1,Wei Wang1

University of California, Los Angeles1

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9:30 PM - DS03.13.02
Development of a Realistic 3D Model of Multicrystalline Si Structure Using Image Processing and Machine Learning of Optical Images and Finite Element Stress Analysis on the Model

Kenta Yamakoshi1,Kentaro Kutsukake2,Takuto Kojima1,Hiroaki Kudo1,Noritaka Usami1

Nagoya University1,RIKEN2

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9:45 PM - DS03.13.03
Chemical Hardness-Driven Interpretable Machine Learning Approach for Rapid Search of Photocatalysts

Ritesh Kumar1

Indian Institute of Science1

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10:00 PM - DS03.13.04
Prediction of Toughness in Heterogeneous Materials Using Machine Learning

A. N. Sadi1,Zubaer Hossain1

University of Delaware1

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10:15 PM - DS03.13.05
Late News: Development of Machine-Learning Force Fields for Complex Materials Systems in Energy Applications

Kwangnam Kim1,Aniruddha Dive1,Andrew Grieder1,2,Nicole Adelstein2,ShinYoung Kang1,Liwen Wan1,Brandon Wood1

Lawrence Livermore National Laboratory1,San Francisco State University2

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10:30 PM - *DS03.04.01
Differentiable Simulations and Deep Learning for Materials Design

Ekin Cubuk1

Google1

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