2019 MRS Fall Meeting & Exhibit

Symposium MT02 : Closing the Loop—Using Machine Learning in High-Throughput Discovery of New Materials

2019-12-02   Show All Abstracts

Symposium Organizers

Jason Hattrick-Simpers, National Institute of Standards and Technology
Barnabas Poczos, Carnegie Mellon University
Markus Reiher, ETH Zurich
Aleksandra Vojvodic, University of Pennsylvania

Symposium Support

Bronze
Machine Learning: Science and Technology | IOP Publishing
Matter & Patterns | Cell Press
MT02.01/MT03.01: Joint Session: Autonomous Science I
Session Chairs
Tonio Buonassisi
Jason Hattrick-Simpers
Kedar Hippalgaonkar
Benji Maruyama
Monday AM, December 02, 2019
Hynes, Level 2, Room 210

8:00 AM - MT02.01.01/MT03.01.01
Autonomous Research Systems for Materials Development—2019 Workshop Summary

Benji Maruyama1,Eric Stach2,Gilad Kusne3,Jason Hattrick-Simpers3,Brian DeCost3

Air Force Research Laboratory1,University of Pennsylvania2,National Institute of Standards and Technology3

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8:30 AM - MT02.01.02/MT03.01.02
Self-Driving Laboratories for Accelerating Discovery of Thin-Film Materials

Curtis Berlinguette1,Jason Hein1,Alan Aspuru-Guzik2,3,Benjamin MacLeod1,Fraser Parlane1,Brian Lam1

The University of British Columbia1,Canadian Institute for Advanced Research (CIFAR)2,The University of Toronto3

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9:00 AM - MT02.01.03/MT03.01.03
An Inter-Laboratory High Throughput Experimental and Open Materials Data Study of Sn-Zn-Ti-O

Jason Hattrick-Simpers1,Andriy Zakutayev2,Sara Barron1,Zachary Trautt1,Nam Nguyen1,Kamal Choudhary1,John Perkins2,Caleb Phillips2,Gilad Kusne1,Feng Yi1,Apurva Mehta3,Martin Green1

National Institute of Standards and Technology1,National Renewable Energy Laboratory2,SLAC National Accelerator Laboratory3

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9:15 AM - MT02.01.04/MT03.01.04
Automatic Microcrack Inspection in Photovoltaics Silicon Wafers by Unsupervised Anomaly Detection via Variational Auto-Encoder

Zhe Liu1,Felipe Oviedo1,Emanuel Sachs1,Tonio Buonassisi1

Massachusetts Institute of Technology1

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9:30 AM - MT02.01.05/MT03.01.05
Screening of High-Capacity Oxygen Storage Materials with Machine Learning Approach

Nobuko Ohba1,Takuro Yokoya2,Seiji Kajita1,Kensuke Takechi1

Toyota Central R&D Laboratories, Inc.1,Toyota Motor Corporation2

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9:45 AM -
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10:15 AM - MT02.01.06/MT03.01.06
The Metaphysics of Chemical Reactivity and Materials Discovery

Lee Cronin1

University of Glasgow1

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10:45 AM - MT02.01.07/MT03.01.07
Robot-Accelerated Perovskite Investigation and Discovery (RAPID)—A High-throughput Approach Towards Metal Halide Perovskite Single Crystal Discovery

Zhi Li1,Mansoor Ani Nellikkal2,Liana Alves2,Peter Parrilla2,Ian Pendleton2,Matthias Zeller3,Joshua Schrier4,Alexander Norquist2,Emory Chan1

Lawrence Berkeley National Lab1,Haverford College2,Purdue University3,Fordham University4

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11:00 AM - MT02.01.08/MT03.01.08
Optimizing Hole Transport Materials with a Self-Driving Thin-Film Laboratory

Benjamin MacLeod1,Fraser Parlane1,Thomas Morrissey1,Florian Häse2,3,4,Loïc Roch2,3,4,Kevan Dettelbach1,Raphaell Moreira1,Lars Yunker1,Michael Rooney1,Joseph Deeth1,Veronica Lai1,Gordon Ng,Henry Situ1,Ray Zhang1,Alán Aspuru-Guzik2,3,4,Jason Hein1,Curtis Berlinguette1

The University of British Columbia1,Harvard University2,University of Toronto3,Vector Institute for Artificial Intelligence4

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11:15 AM - MT02.01.09/MT03.01.09
Convergence of Microfluidics, Colloidal Synthesis and Machine Learning—Real-Time Optimization of Halide Exchange Reactions of Colloidal Inorganic Perovskites Quantum Dots

Robert Epps1,Michael Bowen1,Kameel Abdel-Latif1,Milad Abolhasani1

North Carolina State University1

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11:30 AM - MT02.01.10/MT03.01.10
Autonomously Optimizing Thin Film Morphologies Using Machine Vision

Fraser Parlane1,Benjamin MacLeod1,Nina Taherimakhsousi1,Alan Aspuru-Guzik2,Jason Hein1,Curtis Berlinguette1

The University of British Columbia1,University of Toronto2

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MT02.02/MT03.02: Joint Session: Autonomous Science II
Session Chairs
Gilad Kusne
Markus Reiher
Aleksandra Vojvodic
Monday PM, December 02, 2019
Hynes, Level 2, Room 210

1:30 PM - MT02.02.01/MT03.02.01
Quantum Machine Learning in Chemical Space

Guido Falk von Rudorff1,Anatole von Lilienfeld1

University of Basel1

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2:00 PM - MT02.02.02/MT03.02.02
AI for Automating Materials Discovery

Carla Gomes1

Cornell University1

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2:30 PM - MT02.02.03/MT03.02.03
Machine Learning Methodologies to Enhance Automated Synthesis of New Materials

Gaurav Chopra1,Jonathan Fine1,Armen Beck1

Purdue University1

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2:45 PM - MT02.02.04/MT03.02.04
Autonomous Research Systems—Phase Mapping & Materials Optimization

Gilad Kusne1

National Institute of Standards and Technology1

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3:00 PM -
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3:30 PM - MT02.02.05/MT03.02.05
Information Extraction and Learning by Large-Scale Text-Mining of the Scientific Literature

Gerbrand Ceder1

University of California, Berkeley1

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4:00 PM - MT02.02.06/MT03.02.06
Autonomous Scanning Droplet Cell for On-Demand Alloy Electrodeposition and Characterization

Brian DeCost1,Howie Joress1,Trevor Braun1,Zachary Trautt1,Gilad Kusne1,Jason Hattrick-Simpers1

National Institute of Standards and Technology1

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4:30 PM - MT02.02.07/MT03.02.07
Autonomous Electrolyte Discovery for Batteries with Experimentally Informed Bayesian Optimization

Adarsh Dave1,Sven Burke1,Jared Mitchell1,Kirthevasan Kandasamy1,Biswajit Paria1,Barnabas Poczos1,Venkatasubramanian Viswanathan1,Jay Whitacre1

Carnegie Mellon University1

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MT02.03: Poster Session I: Autonomous Science
Session Chairs
Jason Hattrick-Simpers
Markus Reiher
Aleksandra Vojvodic
Monday PM, December 02, 2019
Hynes, Level 1, Hall B

8:00 PM - MT02.03.01
Autonomous Experimental Phase Analysis of Oxide Systems Demonstrated via Optical Imaging and Spectroscopy

Aine Connolly1,Duncan Sutherland1,Max Amsler1,Sebastian Ament1,Michael Thompson1,Bruce van Dover1,Carla Gomes1

Cornell University1

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8:00 PM - MT02.03.02
Accelerating Materials Discovery with Autonomous Job Control Systems Aided by Machine Learning

Chenru Duan1,Jon Paul Janet1,Aditya Nandy1,Fang Liu1,Heather Kulik1

Massachusetts Institute of Technology1

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8:00 PM - MT02.03.03
Autonomous Experimentation for Mechanical Design

Aldair Gongora1,Bowen Xu1,Wyatt Perry1,Chika Okoye1,Patrick Riley2,Kristofer Reyes3,Elise Morgan1,Keith Brown1

Boston University1,Google Research2,University at Buffalo, The State University of New York3

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8:00 PM - MT02.03.04
Efficient Selection of Categorical Process Variables for Autonomous Experimentation

Florian Häse1,2,3,Loïc Roch1,2,3,Alan Aspuru-Guzik3,2

Harvard University1,Vector Institute for Artificial Intelligence2,University of Toronto3

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2019-12-03   Show All Abstracts

Symposium Organizers

Jason Hattrick-Simpers, National Institute of Standards and Technology
Barnabas Poczos, Carnegie Mellon University
Markus Reiher, ETH Zurich
Aleksandra Vojvodic, University of Pennsylvania

Symposium Support

Bronze
Machine Learning: Science and Technology | IOP Publishing
Matter & Patterns | Cell Press
MT02.04: Machine Learning for Potentials
Session Chairs
Kamal Choudhary
Gabor Csanyi
Olexandr Isayev
Tuesday AM, December 03, 2019
Hynes, Level 2, Room 210

9:00 AM - MT02.04.02
Understanding the Atomic Scale Dynamics in Materials with Unsupervised Learning from Molecular Dynamics

Tian Xie1,Arthur France-Lanord1,Yanming Wang1,Yang Shao-Horn1,Jeffrey Grossman1

Massachusetts Institute of Technology1

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9:15 AM - MT02.04.03
Fast and Accurate Interatomic Potentials by Genetic Programming

Alberto Hernandez1,Adarsh Balasubramanian1,Fenglin Yuan1,Simon Mason1,Tim Mueller1

Johns Hopkins University1

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9:30 AM - MT02.04.04
On-the-Fly Bayesian Active Learning of Interpretable Force Fields for Atomistic Rare Events

Jonathan Vandermause1,Steven Torrisi1,Simon Batzner1,Yu Xie1,Lixin Sun1,Alexie Kolpak2,Boris Kozinsky1

Harvard University1,Massachusetts Institute of Technology2

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9:45 AM - MT02.04.05
Predicting Potential Energy Surfaces with Machine Learning

Matti Hellström1

Software for Chemistry & Materials BV1

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10:00 AM -
BREAK


10:30 AM - MT02.04.06
Data-Driven Materials Design and Machine Learning Using the Materials Project

Kristin Persson1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2

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11:00 AM - MT02.04.07
Constructing Reliable Machine-Learning Potential for Solid-State Reaction: Example of Ni Silicidation

Wonseok Jeong1,Dongsun Yoo1,Kyuhyun Lee1,Seungwu Han1

Seoul National University1

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11:15 AM - MT02.04.08
Gaussian Process-Based Refinement of Dispersion Corrections

Stefan Gugler1,Markus Reiher1,Jonny Proppe2

ETH Zürich1,University of Goettingen2

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11:30 AM - MT02.04.09
Doing Less for More—Multi-Information Bayesian Optimization and the Computational Sciences

Henry Herbol1,Matthias Poloczek2,Paulette Clancy1

Johns Hopkins University1,The University of Arizona2

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11:45 AM - MT02.04.10
Machine Learning-Assisted Acceleration of DFT without Machine-Learning Errors

Alexander Shapeev1

Skolkovo Institute of Science and Technology1

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MT02.05: Machine Learning from Theory
Session Chairs
Vladan Stevanovic
Anatole von Lilienfeld
Tuesday PM, December 03, 2019
Hynes, Level 2, Room 210

1:30 PM - MT02.05.01
Machine-Learning Framework for the Discovery of MOFs for Enhanced Hydrogen Storage

Sanket Deshmukh1,Samrendra Singh1,Abhishek Sose1,Karteek Bejagam1

Virginia Tech1

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1:45 PM - MT02.05.02
Prediction of Microstructure Stress-Strain Curves Using Convolutional Neural Networks

Charles Yang1,Youngsoo Kim2,Seunghwa Ryu2,Grace Gu1

University of California, Berkeley1,Korea Advanced Institute of Science and Technology2

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2:00 PM - MT02.05.03
Accelerating Discovery in Inorganic Chemistry with Machine Learning

Heather Kulik1,Jon Paul Janet1,Chenru Duan1,Aditya Nandy1,Naveen Arunachalam1,Daniel Harper1

Massachusetts Institute of Technology1

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2:30 PM - MT02.05.04
Advances in Interatomic Potentials for Materials

Gabor Csanyi1

University of Cambridge1

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3:00 PM -
BREAK


3:30 PM - MT02.05.05
Polymer Informatics—Current Status and Critical Next Steps

Ramamurthy Ramprasad1

Georgia Institute of Technology1

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4:00 PM - MT02.05.06
Machine Learning Augmented Polymer Design—Challenging the Edisonian Status Quo

Jatin Kumar1

Institute of Materials Research and Engineering1

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4:30 PM - MT02.05.07
A Machine-Learning based Hierarchical Screening Strategy to Expedite Search of Novel Scintillator Chemistries

Anjana Talapatra1,Blas Uberuaga1,Chris Stanek1,Ghanshyam Pilania1

Los Alamos National Laboratory1

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4:45 PM - MT02.05.08
Predicting Densities and Elastic Moduli of SiO2-Based Glasses by Machine Learning

Yong-Jie Hu1,Ge Zhao2,Tyler Del Rose1,Maarten de Jong3,Liang Qi1

University of Michigan1,The Pennsylvania State University2,University of California, Berkeley3

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MT02.06: Poster Session II: Machine Learning for Potentials and from Theory
Session Chairs
Jason Hattrick-Simpers
Barnabas Poczos
Markus Reiher
Aleksandra Vojvodic
Tuesday PM, December 03, 2019
Hynes, Level 1, Hall B

8:00 PM - MT02.06.01
Combining Polymorphism and Machine Learning for Materials Discovery

Fadwa El Mellouhi1

QEERI-HBKU1

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8:00 PM - MT02.06.02
Data-Driven Accurate Positioning of the Band Edges of MXenes

Avanish Mishra1,2,Arunkumar Rajan2,Rinkle Juneja2,Abhishek Singh2

University of Connecticut1,Indian Institute of Science, Bangalore2

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8:00 PM - MT02.06.03
Predicting Nanoscale Static Friction of 2D Materials via Machine Learning Techniques

Behnoosh Sattari Baboukani1,Kristofer Reyes1,Zhijiang Ye2,Prathima Nalam1

University at Buffalo1,Miami university2

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8:00 PM - MT02.06.04
Linking Predictions of Protein Structure and Disorder through Molecular Simulation

Claire Hsu1,Anna Tarakanova2,Markus Buehler1

Massachusetts Institute of Technology1,University of Connecticut2

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8:00 PM - MT02.06.05
Metadynamics Sampling for Training Machine-Learning Interatomic Potential

Dongsun Yoo1,Wonseok Jeong1,Kyuhyun Lee1,Seungwu Han1

Seoul National University1

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8:00 PM - MT02.06.07
Computational Exploration of Near-Infrared Absorbing Polymethine Dyes

Daniele Padula1,Roland Hany1,Frank Nuesch1,Mark Waller2

Empa–Swiss Federal Laboratories for Materials Science and Technology1,Pending.ai2

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8:00 PM - MT02.06.09
A General Machine Learning Framework for Impurity Level Prediction in Semiconductors

Arun Kumar Mannodi Kanakkithodi1,Maria Chan1

Argonne National Laboratory1

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8:00 PM - MT02.06.10
Machine Learning Study of Magnetic Two-Dimensional Materials

Trevor David Rhone1,Shaan Desai1,Wei Chen1,Amir Yacoby1,Efthimios Kaxiras1

Harvard University1

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8:00 PM - MT02.06.11
Machine-Learning-Based Band Gap Predictions of Functionalized MXenes

Abhishek Singh1,Arunkumar Rajan1,Avanish Mishra1,Swanti Satsangi1

Indian Institute of Science Bangalore1

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8:00 PM - MT02.06.12
Machine Learning the Fundamental Tradeoffs between Conductivity and Voltage Stability in Solid State Electrolytes

Karun Kumar Rao1,Michael Nikolaou1,Yan Yao1,Lars Grabow1

University of Houston1

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8:00 PM - MT02.06.13
Spectral Optimization and Temperature Control for Electronic and Optoelectronic Devices Using Machine Learning

Po-Ying Chen1,Quang-Tuyen Le1,Nan-Yow Chen2,An-Cheng Yang2,Yu-Chieh Lo1

National Chiao Tung University1,National Center for High-Performance Computing2

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8:00 PM - MT02.06.14
Deep Learning for Multiscale Atomistic Modeling of Multicomponent Crystal Chemistries Coupled with Hirshfeld Surface Analyses

Arpan Mukherjee1,Aparajita Dasgupta1,Tianmu Zhang1,Scott Broderick1,Krishna Rajan1

University at Buffalo1

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8:00 PM - MT02.06.15
Phase-Field Modeling and Machine Learning of Electric-Thermal-Mechanical Breakdown of Polymer-Based Dielectrics

Jianjun Wang1,Zhonghui Shen2,Yang Shen2,Long-Qing Chen2

The Pennsylvania State University1,Tsinghua University2

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8:00 PM - MT02.06.16
Using Data Driven Models to Gain Insight on Spin- and Oxidation-State Dependent Behavior of Reaction Energetics for Light Alkane Oxidation

Aditya Nandy1,Jon Paul Janet1,Chenru Duan1,Heather Kulik1

Massachusetts Institute of Technology1

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8:00 PM - MT02.06.17
Self-Evolving Neural Network Potentials for Supramolecular Interactions

Wujie Wang1,William Harris1,Rafael Gomez-Bombarelli1

Massachusetts Institute of Technology1

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8:00 PM - MT02.06.18
Artificial Intelligence Design of Tunable Nanocomposites for Crack Resistance

Chi-Hua Yu1,Zhao Qin1,Markus Buehler1

Massachusetts Institute of Technology1

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2019-12-04   Show All Abstracts

Symposium Organizers

Jason Hattrick-Simpers, National Institute of Standards and Technology
Barnabas Poczos, Carnegie Mellon University
Markus Reiher, ETH Zurich
Aleksandra Vojvodic, University of Pennsylvania

Symposium Support

Bronze
Machine Learning: Science and Technology | IOP Publishing
Matter & Patterns | Cell Press
MT02.07/MT03.08: Joint Session: Machine Learning Augmented High-Throughput Experimentation I
Session Chairs
Jason Hattrick-Simpers
Bruce van Dover
Wednesday AM, December 04, 2019
Hynes, Level 2, Room 210

8:00 AM - MT02.07.01/MT03.08.01
Automating Experiments and Data Interpretation in Solar Fuels and Catalysis Research

John Gregoire1

California Institute of Technology1

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8:30 AM - MT02.07.02/MT03.08.02
Cooperative Learning for Materials Systems

Valentin Stanev1

University of Maryland, College Park1

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8:45 AM - MT02.07.03/MT03.08.03
Exploring Catalyst Chemistries beyond Scaling Laws using Statistical Learning

Scott Broderick1,Aparajita Dasgupta1,Thaicia Stona1,Krishna Rajan1

University at Buffalo1

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9:00 AM - MT02.07.04/MT03.08.04
Graph Theory and Machine Learning Uncover Zeolite Transformation Pathways

Daniel Schwalbe Koda1,Wujie Wang1,Rafael Gomez-Bombarelli1

Massachusetts Institute of Technology1

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9:15 AM - MT02.07.05/MT03.08.05
Automatic Processing of the Scientific Literature to Accelerate Nanomaterials Design and Discovery

Anna Hiszpanski1,Brian Gallagher1,Karthik Chellappan1,Peggy Pk Li1,Shusen Liu1,Hyojin Kim1,Jinkyu Han1,Bhavya Kailkhura1,David Buttler1,T. Yong-Jin Han1

Lawrence Livermore National Laboratory1

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9:30 AM -
BREAK


10:00 AM - MT02.07.07/MT03.08.07
High Throughput Experimental Materials Research Methods at NREL

Andriy Zakutayev1

National Renewable Energy Laboratory1

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10:30 AM - MT02.07.08/MT03.08.08
Machine Learning-Assisted High Throughput Synthesis and Characterization of Hybrid Polymer-Carbon Nanotubes Composites for Thermoelectric Application

Daniil Bash1,2,Anas Abutaha2,Yang Xu2,Yee Fun Lim2,Vijila Chellappan2,Zekun Ren3,Isaac Tian3,1,Pawan Kumar2,Swee Liang Wong2,Jose Recatala Gomez2,4,Jayce Cheng2,Tonio Buonassisi5,3,Kedar Hippalgaonkar2

National University of Singapore1,Institute of Materials Research and Engineering2,Singapore-MIT Alliance for Research and Technology (SMART)3,University of Southampton4,Massachusetts Institute of Technology5

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10:45 AM - MT02.07.09/MT03.08.09
Data-driven Materials Design of Halide Perovskites for Photovoltaic Applications

Shijing Sun1,Noor Titan Putri Hartono1,Felipe Oviedo1,Zekun Ren1,Janak Thapa1,Zhe Liu1,Armi Tiihonen1,Ian Marius Peters1,Juan Pablo Correa Baena2,Tonio Buonassisi1,Savitha Ramasamy3

Massachusetts Institute of Technology1,Georgia Institute of Technology2,Institute of Infocomm Research3

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11:00 AM - MT02.07.10/MT03.08.10
Application of Variational Autoencoders to Create Thin Film Structure Zone Diagrams

Lars Banko1,Yury Lysogorskiy1,Ralf Drautz1,Alfred Ludwig1

Ruhr-Universität Bochum1

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11:15 AM - MT02.07.11/MT03.08.11
Generative Adversarial Networks with Molecular Graph Convolution for Learning Secondary Structures of Functional Biomolecules

Siddharth Rath1,Oliver Nakano-Baker1,Jonathan Francis-Landau1,Ximing Lu1,Kevin Jamieson1,Burak Ustundag1,2,Mehmet Sarikaya1

University of Washington1,Istanbul Teknik Universitesi2

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MT02.08/MT03.09: Joint Session: Machine Learning Augmented High-Throughput Experimentation II
Session Chairs
Ichiro Takeuchi
Andriy Zakutayev
Wednesday PM, December 04, 2019
Hynes, Level 2, Room 210

1:30 PM - MT02.08.01/MT03.09.01
Prediction Interpretability in Data-Driven Materials Development

Julia Ling1,Astha Garg1,James Peerless1,Erin Antono1,Edward Kim1,Yoolhee Kim1,Nils Persson1,Malcolm Davidson1

Citrine Informatics1

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2:00 PM - MT02.08.02/MT03.09.02
Network Theory Meets Materials Science

Christopher Wolverton1,Vinay Hegde1,Muratahan Aykol2

Northwestern University1,Toyota Research Institute2

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2:30 PM -
BREAK


3:30 PM - MT02.08.03/MT03.09.03
A Database to Enable the Discovery and Design of Atomically Precise Nanoclusters

Sukriti Manna1,Peter Lile1,Alberto Hernandez1,Tim Mueller1

Johns Hopkins University1

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3:45 PM - MT02.08.04/MT03.09.04
Data Driven Experimental Discovery of New Nitride Materials

Andriy Zakutayev1,Sage Bauers1,Elisabetta Arca1,Wenhao Sun2,Chris Bartel3,John Perkins1,Aaron Holder3,Stephan Lany1,Gerbrand Ceder2

National Renewable Energy Laboratory1,University of California, Berkeley2,University of Colorado Boulder3

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4:00 PM - MT02.08.05/MT03.09.05
Active Learning for Nanophotonic Design via Multi-Fidelity Physical Models

Harry Atwater1,Jialin Song1,Yury Tokpanov1,Yuxin Chen1,Dagny Fleischman1,Katherine Fountaine2,Yisong Yue1

California Institute of Technology1,Northrop Grumman Corporation2

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4:30 PM - MT02.08.06/MT03.09.06
Accelerating Materials Discovery through Rapid Construction of Processing Phase Diagrams

Duncan Sutherland1,Aine Connolly1,Sebastian Ament1,Michael Thompson1,Carla Gomes1,Bruce van Dover1

Cornell University1

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4:45 PM - MT02.08.07/MT03.09.07
High-Throughput Screening of Perovskite-Inspire Materials Using Steady-State Photoconductivity and Bayesian Optimization

Jose Perea1,Felipe Oviedo1,Han Yin1,Janak Thapa1,Armi Tiihonen1,Zhe Liu1,Ian Marius Peters1,Shijing Sun1,Rafael Jaramillo1,Tonio Buonassisi1

Massachusetts Institute of Technology1

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MT02.09: Poster Session III: Machine Learning Augmented High-Throughput Experimentation
Session Chairs
Jason Hattrick-Simpers
Wednesday PM, December 04, 2019
Hynes, Level 1, Hall B

8:00 PM - MT02.09.01
Machine Learning for Revealing Aging Mechanisms of Perovskite Solar Cells

Armi Tiihonen1,Shijing Sun1,Jose Perea1,Felipe Oviedo1,Zhe Liu1,Noor Titan Putri Hartono1,Janak Thapa1,Tonio Buonassisi1

Massachusetts Institute of Technology1

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8:00 PM - MT02.09.02
High-Throughput Discovery of Next Generation Sequencing-Based Peptide-Guided New Materials via Machine Learning

Jacob Rodriguez1,Deniz Yucesoy1,Siddharth Rath1,Jason Stephany1,Doug Fowler1,Mehmet Sarikaya1

University of Washington1

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8:00 PM - MT02.09.03
Data Driven Analysis of Dielectric Constants in Inorganic Materials

Kazuki Morita1,Daniel Davies1,Keith Butler2,1,Aron Walsh1,3

Imperial College London1,Rutherford Appleton Laboratory2,Yonsei University3

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8:00 PM - MT02.09.04
A Semi-Automatic Pipeline for Efficient and Sustained Polymer Data Capture

Pranav Shetty1,Rampi Ramprasad1

Georgia Institute of Technology1

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2019-12-05   Show All Abstracts

Symposium Organizers

Jason Hattrick-Simpers, National Institute of Standards and Technology
Barnabas Poczos, Carnegie Mellon University
Markus Reiher, ETH Zurich
Aleksandra Vojvodic, University of Pennsylvania

Symposium Support

Bronze
Machine Learning: Science and Technology | IOP Publishing
Matter & Patterns | Cell Press
MT02.10: High-Throughput Experimentation and Machine Learning I
Session Chairs
Brian DeCost
John Perkins
Thursday AM, December 05, 2019
Hynes, Level 2, Room 210

8:30 AM - MT02.10.01
Beyond Just Fitting Numbers—Artificial Intelligence for Identifying Statistically Exceptional Materials

Luca Ghiringhelli1,Matthias Scheffler1,2

Fritz-Haber-Institut der MPG1,Humboldt-Universität zu Berlin2

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9:00 AM - MT02.10.02
Crystallographic Information and Thermoelectric Properties Obtained from High-Throughput Experiments of Ca1-xBixMnO3 Powder

Kenjiro Fujimoto1,Yusuke Yamada1,Akihisa Aimi1,Keishi Nishio1,Shingo Maruyama2

Tokyo University of Science1,Tohoku University2

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9:15 AM - MT02.10.03
A High-Throughput Study of Refractory High-Entropy Alloys Guided by Machine Learning

Howie Joress1,Nils Persson1,Brian DeCost1,Jason Hattrick-Simpers1

National Institute of Standards and Technology1

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9:30 AM - MT02.10.04
Accelerating Generation of Fundamental Materials Insights by Analyzing Machine Learning Models

Mitsutaro Umehara1,2,Helge Stein1,Dan Guevarra1,Paul Newhouse1,David Boyd1,John Gregoire1

California Institute of Technology1,Toyota Motor North America2

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9:45 AM - MT02.10.05
A Data Driven Approach for the Accelerated Discovery of Photocathode Materials

Evan Antoniuk1,Yumeng Yue1,Yao Zhou2,Bruce Dunham3,Piero Pianetta3,Theodore Vecchione3,Evan Reed1

Stanford University1,Google2,SLAC National Accelerator Laboratory3

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10:00 AM -
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10:30 AM - MT02.10.06
Adding Domain Knowledge and Causality to Materials Informatics

Vladan Stevanovic1

Colorado School of Mines1

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11:00 AM - MT02.10.07
Mapping and Understanding Large-Scale Stability Trends across the Ternary Metal Nitrides

Wenhao Sun1,2,Chris Bartel2,3,Elisabetta Arca4,Sage Bauers4,Bethany Matthews5,Janet Tate5,Bor-Rong Chen6,Michael Toney6,Laura Schelhas6,Andriy Zakutayev4,Stephan Lany4,Aaron Holder3,Gerbrand Ceder2

University of Michigan–Ann Arbor1,Lawrence Berkeley National Labs2,University of Colorado Boulder3,National Renewable Energy Laboratory4,Oregon State University5,SLAC National Accelerator Laboratory6

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11:15 AM - MT02.10.08
Combinatorial Synthesis and High-Throughput Characterization of Microstructure and Phase Transformation in NiTiCu-X Quaternary Thin-Film Libraries for Elastocaloric Cooling

Naila Al Hasan1,Huilong Hou1,Jonathan Counsell2,Tieren Gao1,Suchismita Sarkar3,Sigurd Thienhaus4,Apurva Mehta3,Alfred Ludwig4,Ichiro Takeuchi1

University of Maryland1,Kratos Analytical Ltd.2,SLAC National Accelerator Laboratory3,Ruhr-Universität Bochum4

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11:30 AM - MT02.10.09
Discovery of Promising Salt Hydrates for Thermal Energy Storage Using High Throughput Computation and Machine Learning

Steven Kiyabu1,Donald Siegel1

University of Michigan1

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11:45 AM - MT02.10.10
Accelerating the Search for Lithium-Ion Conductors with Machine Learning Interatomic Potentials

Koutarou Aoyagi1,2,Chuhong Wang1,Tim Mueller1

Johns Hopkins University1,Toyota Motor Corporation2

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MT02.11: Experimentation and Machine Learning I
Session Chairs
Jason Hattrick-Simpers
Elsa Olivetti
Thursday PM, December 05, 2019
Hynes, Level 2, Room 210

1:30 PM - MT02.11.01
Active Machine Learning for Automating Materials Discovery

Shali Jiang1,Roman Garnett1

Washington University in St. Louis1

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2:00 PM - MT02.11.02
Pan-Sharpening Algorithm for Spectral Map Reconstruction

Nikolay Borodinov1,Natasha Bilkey2,Alison Pawlicki1,Marcus Foston2,Anton Ievlev1,Alex Belianinov1,Stephen Jesse1,Rama Vasudevan1,Sergei Kalinin1,Olga Ovchinnikova1

Oak Ridge National Laboratory1,Washington University in St. Louis2

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2:15 PM - MT02.11.03
Accelerated Catalyst Discovery through Gaussian Processes and Active Learning

Kiran Vaddi1,Olga Wodo1,Krishna Rajan1

University at Buffalo1

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2:30 PM - MT02.11.04
Insights in Chemical Features Impacting the Quality and Lifecycle of 3D Printed Model System—An Integrated Experimental, Modeling and Data Sciences Approach

Amra Peles1,William Rosenthal1,Francesca C Grogan1,Yulan Li1,Erin I Barker1,Zachary C Kennedy1,Timothy Pope1,Christopher Barrett1,Marvin Warner1

Pacific North West Laboratory1

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2:45 PM -
BREAK


3:15 PM - MT02.11.05
The Machine Learning Route to Accelerated Discovery and Inverse Design

Johannes Hachmann1

University of New York, Buffalo1

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3:45 PM - MT02.11.06
Data-Driven Inquiry into Materials Synthesis

Elsa Olivetti1,Edward Kim1,Alexander van Grootel1,Zach Jensen1

Massachusetts Institute of Technology1

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4:15 PM - MT02.11.07
Chemical Dynamics Analysis Pipeline at PNNL

Mathew Thomas1,Malachi Schram1,Jan Strube1,Robert Rallo1,Christopher Barrett1,Kevin Fox1,Noah Oblath1

Pacific Northwest National Laboratory1

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4:30 PM - MT02.11.08
AI Driven Microstructure ExplorationThe Case of Organic Electronics

Baskar Ganapathysubramanian1,Balaji Sesha Sarath Pokuri1,Sambuddha Ghosal1,Prerna Ritesh1,Soumik Sarkar1

Iowa State University of Science and Technology1

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4:45 PM - MT02.11.09
Practical Implementation of Materials Informatics for Discovery of Superionic Conductors

Ryoji Asahi1,Nobuko Ohba1,Masato Matsubara1,Akitoshi Suzumura1,Shin Tajima1,Yumi Masuoka1,Joohwi Lee1,Seiji Kajita1

Toyota Central R&D Labs., Inc.1

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MT02.12: Poster Session IV: Experimentation and Machine Learning
Session Chairs
Jason Hattrick-Simpers
Barnabas Poczos
Aleksandra Vojvodic
Thursday PM, December 05, 2019
Hynes, Level 1, Hall B

8:00 PM - MT02.12.01
Effect of Dielectric Particle Heterogeneity on Capacitance—A Machine Learning Biased Genetic Algorithm Approach

Venkatesh Meenakshisundaram1,2,David Yoo1,2,Andrew Gillman1,Phil Buskohl1

Air Force Research Laboratory1,UES, Inc.2

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8:00 PM - MT02.12.02
Machine Learning Based Data Driven Approach for Optimized Inkjet Printed Electronics

Fahmida Pervin Brishty1,Ruth Urner1,Gerd Grau1

York University1

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8:00 PM - MT02.12.03
Scientific Data Infrastructure for Combinatorial Material Science

Lars Banko1,Sigurd Thienhaus1,Alfred Ludwig1

Ruhr-Universität Bochum1

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8:00 PM - MT02.12.04
Machine Learning Prediction of Glass-Forming Ability and Elastic Modulus for Bulk Metallic Glasses

Jie Xiong1,San Qiang Shi1,Tong-Yi Zhang2

The Hong Kong Polytechnic University1,Shanghai University2

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8:00 PM - MT02.12.05
Accelerated Development of High-Performance Nanocomposite Solar Absorbers Using Bayesian Optimization

Qiangshun Guan1,Afra Alketbi1,Aikifa Raza1,TieJun Zhang1

Khalifa University of Science and Technology1

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8:00 PM - MT02.12.06
Recommender System of Processing Conditions for Inorganic Compounds Based on a Parallel Experimental Dataset

Hiroyuki Hayashi1,2,3,Atsuto Seko1,2,3,Isao Tanaka1,3

Kyoto University1,PRESTO2,National Institute for Materials Science3

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8:00 PM - MT02.12.07
Designing Stretchable MoS2 Kirigami Using Deep Reinforcement Learning

Pankaj Rajak1,Beibei Wang2,Ken-ichi Nomura2,Aiichiro Nakano2,Rajiv Kalia2,Priya Vashishta2

Argonne National Laboratory1,University of Southern California2

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8:00 PM - MT02.12.08
Materials Discovery by Machine Learning and Single Particle Diagnosis

Yukinori Koyama1,Atsuto Seko2,1,Isao Tanaka2,1,Shiro Funahashi1,Naoto Hirosaki1

National Institute for Materials Science1,Kyoto University2

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8:00 PM - MT02.12.09
Predicting Carbon Nanotube Forest Synthesis-Structure-Property Relationships Using Physics-Based Simulation and Deep Learning

Taher Hajilounezhad1,Zakariya Oraibi1,Ramakrishna Surya2,Filiz Bunyak1,Kannappan Palaniappan1,Prasad Calyam1,Matthew R. Maschmann1

University of Missouri-Columbia1,University of Cincinnati2

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8:00 PM - MT02.12.10
Automation of Electron Microscopy to Enable Atomic Datasets for Machine Learning

Matthew Hauwiller1,Abinash Kumar1,James LeBeau1

Massachusetts Institute of Technology1

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8:00 PM - MT02.12.11
Small-Data Driven Machine Learning Screening Framework for Accelerated Discovery of Ferroelectric Oxides

Achintha Ihalage1,Yang Hao1

Queen Mary University of London1

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8:00 PM - MT02.12.12
Creating Glasswing Butterfly-Inspired Durable Antifogging Superomniphobic Supertransmissive, Superclear Nanostructured Glass through Bayesian Learning and Optimization

Sajad Haghanifar1,Paul Leu1

University of Pittsburgh1

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8:00 PM - MT02.12.13
Machine Learning and Optimization in Shape Memory Alloys Using a Large Experimental Database

William Trehern1,Ibrahim Karaman1

Texas A&M University1

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8:00 PM - MT02.12.14
Discovery Paradigm for Novel Organic-Inorganic Halide Perovskites for Optoelectronic Applications through Automated Synthesis

Mahshid Ahmadi1,Katherine Higgins1,Maxim Ziatdinov2,Rama Vasudevan2,Sergei Kalinin2

University of Tennessee, Knoxville1,Oak Ridge National Laboratory2

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2019-12-06   Show All Abstracts

Symposium Organizers

Jason Hattrick-Simpers, National Institute of Standards and Technology
Barnabas Poczos, Carnegie Mellon University
Markus Reiher, ETH Zurich
Aleksandra Vojvodic, University of Pennsylvania

Symposium Support

Bronze
Machine Learning: Science and Technology | IOP Publishing
Matter & Patterns | Cell Press
MT02.13: Experimentation and Machine Learning II
Session Chairs
Jason Hattrick-Simpers
Olga Wodo
Friday AM, December 06, 2019
Hynes, Level 2, Room 210

8:30 AM - MT02.13.01
ML-Aided Thermal Management Materials Design and Small Data Strategy

Yibin Xu1

National Institute for Materials Science1

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9:00 AM - MT02.13.02
Using Advanced Decision Policies in Bayesian-Optimized Machine Learning to Control Carbon Nanotube Growth

Rahul Rao1,Pavel Nikolaev1,Ahmad Islam1,Kristofer Reyes2,Benji Maruyama3

UES Inc Air Force Research Laboratory1,University at Buffalo, The State University of New York2,Air Force Research Laboratory3

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9:15 AM - MT02.13.03
Predicting Solid-Solution Formation—Machine-Learning and a New Physics-Based Rule

Zongrui Pei1,Junqi Yin2,Jeffrey Hawk1,David Alman1,Michael Gao1

National Energy Technology Laboratory1,Oak Ridge National Laboratory2

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9:30 AM - MT02.13.04
freud—Powerful Particle Simulation Analysis Tools for Machine Learning and Materials Design

Bradley Dice1,Vyas Ramasubramani1,Eric Harper1,Matthew Spellings1,Joshua Anderson1,Sharon Glotzer1

University of Michigan1

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9:45 AM - MT02.13.05
Revealing the Spectrum of Unknown Layered Materials with Super-Human Predictive Abilities

Gowoon Cheon1,Ekin D. Cubuk2,Evan Antoniuk1,Joshua Goldberger3,Evan Reed1

Stanford University1,Google Brain2,The Ohio State University3

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10:00 AM -
BREAK


10:30 AM - MT02.13.06
Microstructure Informatics—Expanding Descriptors from Molecular to Microstructural Level

Olga Wodo1

State University of New York at Buffalo1

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11:00 AM - MT02.13.07
High-Throughput Electron Microscopic Analysis of Nanomaterials Based on Machine Learning Techniques

Byoungsang Lee1,Seokyoung Yoon2,Jung Heon Lee1,2

Sungkyunkwan University1,SKKU Advanced Institute of Nanotechnology (SAINT)2

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11:15 AM - MT02.13.08
Inverse Learning of Material Physics Through In Situ Image Data and Continuum Modeling

Hongbo Zhao1,Martin Bazant1

Massachusetts Institute of Technology1

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11:30 AM - MT02.13.09
Evaluating Machine Learning as a Tool for Segmentation of In Situ TEM Data

James Horwath1,Dmitri Zakharov2,Eric Stach1

University of Pennsylvania1,Brookhaven National Laboratory2

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11:45 AM - MT02.13.10
Robust Microstructure Representation

Devyani Jivani1,Olga Wodo1

SUNY Buffalo1

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Symposium Support