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Symposium F.MT07 : Data Science and Automation to Accelerate Materials Development and Discovery

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Symposium Organizers

Tonio Buonassisi, Massachusetts Institute of Technology
Kristen Brosnan, Superior Technical Ceramics
Keith Brown, Boston University
Kedar Hippalgaonkar, Nanyang Technological University
F.MT07.01: Al-Ready Data for Materials Science
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FAIR Data Infrastructures Towards New Horizons for Materials Research

Claudia Draxl1,2

Humboldt-Universität zu Berlin1,Fritz-Haber-Institut Berlin2

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F.MT07.02: Analysis of Experimental Data
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FAIR Digital Object Framework and High Throughput Experiment

Zachary Trautt1,Raymond Plante1,Gretchen Greene1,Jason Hattrick-Simpers1,Brian DeCost1,Gilad Kusne1,Andriy Zakutayev2

National Institute of Standards and Technology1,National Renewable Energy Laboratory2

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F.MT07.03: Constitutive Modeling
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Scientific AI in Materials Science—Reproducibility, Trusting Archival Data and Single Scalar Labels

Jason Hattrick-Simpers1,Brian DeCost1,Zachary Trautt1,Gilad Kusne1,Eva Campo2,Martin Green1

National Institute of Standards and Technology1,National Science Foundation2

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F.MT07.04: Data Mining
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Jump Planner for Autonomous Materials Discovery

Chiwoo Park1,Peihua Qiu2,Jennifer Carpena-Núñez3,Rahul Rao3,Michael Susner3,Benji Maruyama3

Florida State University1,University of Florida2,Air Force Research Laboratory3

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F.MT07.05: Materials Representations
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Machine Learning of Experimental Databases for Predictions and Discovery of Novel Functional Materials

Ichiro Takeuchi1

University of Maryland1

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F.MT07.06: Material Design
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Active Learning Guided Online Materials Synthesis and Full-Map Understanding

Xiaonan Wang1

National University of Singapore1

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F.MT07.07: Science of Optimization
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AMANDA—An Autonomous Materials and Device Application Platform for the Laboratory of the Future and the Next Generation Materials Research

Christoph Brabec1,2

FAU1,Forschungszentrum Jülich GmbH2

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F.MT07.08: Machine Learning in Theory and First Principles
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Applying Automated Process Analytical Technology to Challenges in Material Synthesis

Jason Hein1

University of British Columbia1

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F.MT07.09: Transfer Learning
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Real-Time Control and Analysis in Autonomous Materials Synthesis

Kristofer Reyes1,Soojung Baek1,Kevin Yager2,Frank Alexander2,Jennifer Carpena3,Rahul Rao3,Benji Maruyama3

University at Buffalo, The State University of New York1,Brookhaven National Laboratory2,Air Force Research Laboratory3

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F.MT07.10: Poster Session: Data Science and Automation to Accelerate Materials Development and Discovery
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Optimization of Multiple Physical Properties by Machine Learning Incorporating the Concept of Deviation Value

Kokin Nakajin1,2,Takuya Minami1,Toshio Fujita1,2,Masaaki Kawata3,Katsumi Murofushi1,Hiroshi Uchida1,Kazuhiro Omori1,Yoshishige Okuno1

Showa Denko1,Research Association of High-Throughput Design and Development for Advanced Functional Materials(ADMAT)2,National Institute of Advanced Industrial Science and Technology (AIST)3

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Available on demand - F.MT07.10.03
Charting Lattice Thermal Conductivity of Inorganic Crystals

Taishan Zhu1,Sheng Gong1,Tian Xie1,Prashun Gorai2,Jeffrey Grossman1

Massachusetts Institute of Technology1,Colorado School of Mines2

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Available on demand - F.MT07.10.05
Authoring Interactive and Extensible Visualizations from a Materials Knowledge Graph

Michael Deagen1,James McCusker2,Sam Stouffer2,Tolulomo Fateye3,Linda Schadler1,L. Catherine Brinson3

The University of Vermont1,Rensselaer Polytechnic Institute2,Duke University3

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Available on demand - F.MT07.10.06
Data-Driven Thermoelectric Modelingš—Prospects and Current Challenges

Mamadou Mbaye1,Sangram Pradhan1,Messaoud Bahoura1

Norfolk State University1

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Available on demand - F.MT07.10.09
Inverse Design of Colossal Permittivity Materials with Crystal Graph Convolutional Neural Networks

Dillon Yost1,Sheng Gong1,Tian Xie1,Jeffrey Grossman1

Massachusetts Institute of Technology1

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Available on demand - F.MT07.10.10
Automated Cluster Analysis of 2-Dimensional X-Ray Diffraction for Composition Spread Oxide Thin Film Fabricated by Combinatorial Synthesis, Aiming to Visual Information-Guided Material Discovery

Akihiro Yamashita1,2,Takahiro Nagata2,Shinjiro Yagyu2,Toru Asahi1,Toyohiro Chikyow2

Waseda University1,National Institute for Materials Science2

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Available on demand - F.MT07.10.12
Machine Learning Materials Properties for Small Datasets

Pierre-Paul De Breuck1,Geoffroy Hautier1,Gian-Marco Rignanese1

Université catholique de Louvain1

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Available on demand - F.MT07.10.14
Development of All-Atom and Coarse-Grained Embedded Atom Method Potentials for Gold Using Particle Swarm Optimization

Fangxi Wang1,Gaurav Anand1,Troy Gustke1,Abhishek Sose1,Soumil Joshi1,Sanket Deshmukh1

Virginia Tech1

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Available on demand - F.MT07.10.15
Mechanical Characterization of Ceramic Nanocomposites via Multi-Fidelity Neural Networks

Christos Athanasiou1,Xing Liu1,Lu Lu1,Nitin Padture1,Huajian Gao1,Brian Sheldon1

Brown University1

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Available on demand - F.MT07.10.16
Adaptive Spectral Graph Convolutional Neural Network in Crystal Property Prediction

Jiali Li1,Lingtong Chen2,Zekun Ren3,Xiaoli Liu1,Qian Xie4,Xiaonan Wang1

National University of Singapore1,University of Southern California2,Singapore MIT Alliance for Research and Technology3,Anhui University of Technology4

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Available on demand - F.MT07.10.17
Computational Structure Prediction for Discovery of Novel Crystalline Phases

Lauren McRae1,Scott Warren1

University of North Carolina Chapel Hill1

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Available on demand - F.MT07.10.18
Tackling Data Scarcity in Materials Research—Augmentation of Training Data for Classification of X-Ray Diffraction (XRD) Patterns

Shreyaa Raghavan1,Zhe Liu1,Tonio Buonassisi1

Massachusetts Institute of Technology1

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Available on demand - F.MT07.10.19
Monte Carlo Simulations of Materials with Autoregressive Generative Models

James Damewood1,Daniel Schwalbe Koda1,Rafael Gomez-Bombarelli1

Massachusetts Institute of Technology1

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Available on demand - F.MT07.10.20
Boltzmann Sigmoidal Modeling Analysis of Patterned GaAsSbN Nanowires

Sean Johnson1,Rabin Pokharel1,Michael Lowe1,Hirandeep Kuchoor1,Surya Nalamati1,Shanthi Iyer1

North Carolina A&T State University1

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Available on demand - F.MT07.10.21
Accelerating Materials Development and Discovery via the Signac Data Management Framework

Brandon Butler1,Bradley Dice1,Vyas Ramasubramani1,Carl Simon Adorf1,Sharon Glotzer1

University of Michigan1

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Available on demand - F.MT07.10.22
Self-Supervised Graph Representation Learning for Cell-Penetrating Peptides

Ting-Chi Liu1,Wei-Han Hui1,Chia-Ching Chou1,Shu-Wei Chang1

National Taiwan University1

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Available on demand - F.MT07.10.23
Excitonic Effects in Absorption Spectra of Carbon Dioxide Reduction Photocatalysts

Tathagata Biswas1

Arizona State University1

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Available on demand - F.MT07.10.24
Prediction of Change of DOS Associated with Bond Formation Using Machine Learning

Eiki Suzuki1,Kiyou Shibata1,Teruyasu Mizoguchi1

The University of Tokyo1

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Available on demand - F.MT07.10.25
Data-Driven Approach to the Prediction of Mechanical Properties in Carbon Fiber Reinforced Composites

Vade Shah1,Steven Zadourian1,Charles Yang1,Zilan Zhang1,Grace Gu1

University of California, Berkeley1

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Available on demand - F.MT07.10.26
Late News: Inverse Design of Potential Singlet Fission Molecules Using a Transfer Learning Based Approach

Akshay Subramanian1,Utkarsh Saha1,Tejasvini Sharma1,Naveen Tailor1,Soumitra Satapathi1

Indian Institute of Technology Roorkee1

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Available on demand - F.MT07.10.27
Late News: Multigenerational Crumpling of 2D Materials for Anticounterfeiting Patterns with Deep Learning Authentication

Lin Jing1,Po-Yen Chen1

National University of Singapore1

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Available on demand - F.MT07.10.28
Late News: Matching Optimal Physical Descriptors of Two-Layer Materials in Search of High Interfacial Thermal Conductance Using Gaussian Process Regression

Ainur Koshkinbayeva1,Azat Abdullaev1,Zhandos Utegulov1

Nazarbayev University1

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Available on demand - F.MT07.10.29
Late News: Improving the Performance of Machine-Learning Models for the Prediction of AxA(1-x)BX3 Perovskite Bandgaps

Heesoo Park1,Raghvendra Mall2,Adnan Ali1,Halima Bensmail2,Stefano Sanvito3,Fadwa El Mellouhi1

Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University1,Qatar Computing Research Institute, Hamad Bin Khalifa University2,School of Physics, AMBER and CRANN Institute, Trinity College, Dublin3

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Available on demand - F.MT07.10.30
Late News: Data-Driven Discovery of the Functional Form of the Superconducting Critical Temperature

Stephen Xie1,Yundi Quan1,Gregory Stewart1,James Hamlin1,Peter Hirschfeld1,Richard Hennig1

University of Florida1

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F.MT07.01: Al-Ready Data for Materials Science
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The Perovskite Database Project—How Do We Get Experimentalists to Share All Their Data in a Form Useful for Others?

Tor Jacobsson1,2,Eva Unger1

Helmholtz-Zentrum Berlin für Materialien und Energie1,Uppsala University2

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F.MT07.02: Analysis of Experimental Data
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Automated Prediction of Crystal Lattice Parameters from Powder Diffraction Data

Sathya Chitturi1,2,Daniel Ratner1,2,Kevin Stone1,2,Richard Walroth1,2,Vivek Thampy1,2,Mike Dunne1,2,Christopher Tassone1,2

SLAC National Accelerator Laboratory1,Stanford University2

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F.MT07.03: Constitutive Modeling
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Neural Network Approach for Predicting Organic Molecular Properties from Core-Loss Spectroscopy

Kakeru Kikumasa1,Shin Kiyohara2,Kiyou Shibata1,Teruyasu Mizoguchi1

The University of Tokyo1,Tokyo Institute of Technology2

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F.MT07.04: Data Mining
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Autonomous Discovery of Materials for Intercalation Electrodes

Ivano E. Castelli1,Felix Tim Bölle1,Tejs Vegge1,Juan Maria Garcia Lastra1

Technical University of Denmark1

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F.MT07.05: Materials Representations
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Platform for Polymer Inverse-Design by Graph Generative Algorithms

Seiji Takeda1,Toshiyuki Hama1,Hsianghan Hsu1,Victoria Piunova2,Dmitry Zubarev2,Daniel Sanders2,Daiju Nakano1

IBM Research - Tokyo1,IBM Almaden Research Center2

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F.MT07.06: Material Design
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Meta-Reinforcement Learning as the Driver of Data Acquisition in Autonomous Polymer Discovery

Sarathkrishna Swaminathan1,Chinyere Agunwa1,Victoria Piunova1,Krystelle Lionti1,Daniel Sanders1,Dmitry Zubarev1

IBM Research1

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F.MT07.07: Science of Optimization
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An Automated Formulation System to Accelerate Development of Printed Thin-Film Materials

Michael Heiber1,Kane Scipioni1,Amir Khalighi1,Kuya Takami1,James Corson1,Colin McNeece1,Christopher Farrow1,Roger Bonnecaze2,Eric Jones1

Enthought1,The University of Texas at Austin2

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F.MT07.08: Machine Learning in Theory and First Principles
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Transferable Nonlocal Exchange Density Functionals via Machine Learning

Kyle Bystrom1,Boris Kozinsky1

Harvard University1

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F.MT07.09: Transfer Learning
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An Autonomous Experimentation System with Multiple Information Sources

Aldair Gongora1,Emily Whiting1,Patrick Riley2,Kristofer Reyes3,Elise F. Morgan1,Keith Brown1

Boston University1,Google2,University at Buffalo, The State University of New York3

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F.MT07.01: Al-Ready Data for Materials Science
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Accuracy, Uncertainty, Inspectability—Learning with Compositionally-Restricted Attention-Based Networks

Steven Kauwe1,Anthony Wang2,Taylor Sparks1

The University of Utah1,Technische Universität Berlin2

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F.MT07.02: Analysis of Experimental Data
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Accelerating Structural Investigation in InGaO3(ZnO)m Using Rapid and Cost-Effective Optical Pre-Screening

Vidit Gupta1,Aine Connolly1,Duncan Sutherland1,Max Amsler1,2,Sebastian Ament1,R. Van Dover1,Michael Thompson1

Cornell University1,University of Bern2

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F.MT07.03: Constitutive Modeling
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Assessing the Predictive Power of the Pauling Rules Using Modern Data Analysis

Janine George1,David Waroquiers1,Davide Di Stefano1,Guido Petretto1,Gian-Marco Rignanese1,Geoffroy Hautier1

Université catholique de Louvain1

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F.MT07.04: Data Mining
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Largest Possible Mobility of Molecular Semiconductors and Strategies to Achieve It

Tahereh Nematiaram1,Daniele Padula1,Alessandro Troisi1,Alessandro Landi1

University of Liverpool1

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F.MT07.05: Materials Representations
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Navigating Chemical Composition Spaces Using Gaussian Processes and Representation Learning

Jens Hummelshoej1,Muratahan Aykol1,Joseph Montoya1,Linda Hung1,Santosh Suram1

Toyota Research Insititue1

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F.MT07.06: Material Design
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Model-Based Reinforcement Learning for Predictive Synthesis of MoS2

Pankaj Rajak1,Aravind Krishnamoorthy2,Ankit Mishra2,Ye Luo1,Rajiv Kalia2,Aiichiro Nakano2,Priya Vashishta2

Argonne National Laboratory1,University of Southern California2

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F.MT07.07: Science of Optimization
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Applications and Development of an Autonomous Alloy Electrochemical Deposition and Corrosion Platform

Howie Joress1,Brian DeCost1,Bruce Ravel1,2,Suchismita Sarker3,Najlaa Hassan1,Trevor Braun1,Apurva Mehta3,Jason Hattrick-Simpers1

National Institute of Standards and Technology1,Brookhaven National Laboratory2,SLAC National Accelerator Laboratory3

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F.MT07.08: Machine Learning in Theory and First Principles
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Quantifying Uncertainty in High-Throughput Density Functional Theory

Vinay Hegde1,Christopher Borg1,Zachary del Rosario1,2,Yoolhee Kim1,Maxwell Hutchinson1,Erin Antono1,Julia Ling1,Paul Saxe3,James Saal1,Bryce Meredig1

Citrine Informatics1,Olin College of Engineering2,Virginia Tech3

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F.MT07.09: Transfer Learning
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Combining Synthetic and Experimental Data in Multi-Fidelity Machine Learning Models to Accelerate Glass Discovery

Mathieu Bauchy1,Kai Yang1

University of California, Los Angeles1

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F.MT07.01: Al-Ready Data for Materials Science
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Late News: Uncertainity-aware microstructural database to accelerate data consolidation in organic electronics

Olga Wodo3,Christopher W. Hong1,Snigdha Motadaka2,Boris Glavic1,Oliver Kennedy2

Illinois Institute of Technology1,University at Buffalo, The State University of New York2,State University of New York at Buffalo3

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F.MT07.02: Analysis of Experimental Data
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Image Similarity Latent Manifolds for Materials Microscopy

Tri Nguyen1,Yichen Guo1,Joshua Agar1

Lehigh University1

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F.MT07.03: Constitutive Modeling
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A Bayesian Approach to Quantify the Single-Crystal Elastic Constants in a Polycrystalline β Metastable Titanium Alloy

Ravi raj purohit Purushottam Raj Purohit1,Thiebaud Richeton1,Lionel Germain1,Stephane Berbenni1,Nathalie Gey1,Olivier Castelnau2

Université de Lorraine1,Arts et Métiers ParisTech/ CNRS 80062

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F.MT07.04: Data Mining
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Crystal Synthesis Prediction Using Deep Learning

Ali Davari1,Sara Kadkhodaei1

University of Illinois at Chicago1

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F.MT07.05: Materials Representations
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Machine Learning Based Design of Transition Metal Catalysts

Pascal Friederich3,Gabriel dos Passos Gomes1,David Balcells2,Alán Aspuru-Guzik1

University of Toronto1,University of Oslo2,Karlsruhe Institute of Technology3

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F.MT07.06: Material Design
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Accelerating Inverse Design of Conjoined Photovoltaic and Thermoelectric Generator Systems via Physics-Driven Machine Learning

Aleks Siemenn1,Tonio Buonassisi1,Zhe Liu1

Massachusetts Institute of Technology1

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F.MT07.07: Science of Optimization
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Accessing Creativity and Institutional Knowledge of Polymer Chemists via Expert-in-the-Loop AI—Case of Acrylic Polymer Design

Victoria Piunova1,Dmitry Zubarev1,Petar Ristoski1,Anna Lisa Gentile1,Linda Kato1,Seiji Takeda2,Toshiyuki Hama2,Hsianghan Hsu2,Daiju Nakano2,Daniel Gruhl1,Seteve Welch1,Daniel Sanders1

IBM Almaden Research Center1,IBM Tokyo Research Laboratory2

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F.MT07.08: Machine Learning in Theory and First Principles
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Two-Tier Machine Learning Acceleration of Molecular Dynamics with Enhanced Sampling—Predicting Reaction Rates of Hydrogenation Reactions on Metal Catalysts

Lixin Sun1,Simon Batzner1,Steven Torrisi1,Yu Xie1,Jin Soo Lim1,Jonathan Vandermause1,Boris Kozinsky1

Harvard University1

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F.MT07.01: Al-Ready Data for Materials Science
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Late News: Data Automation and Data Intelligence for Data-Driven R&D

Max Petersen1,Rob Brown1

Dotmatics1

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F.MT07.02: Analysis of Experimental Data
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Combining Experiment, Physics-Based Modeling and Bayesian Inference to Enhance Voltammetric Characterization

Alexis Fenton Jr.1,Fikile Brushett1

Massachusetts Institute of Technology1

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F.MT07.03: Constitutive Modeling
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Automatic Generation of Computational Reaction Networks for Unbiased Exploration of Chemical Pathways

Evan Spotte-Smith1,2,Samuel Blau1,Xiaowei Xie1,2,Brandon Wood1,Hetal Patel1,2,Shyam Dwaraknath1,Kristin Persson1,2

Lawrence Berkeley National Laboratory1,University of California, Berkeley2

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F.MT07.04: Data Mining
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Physically Informed Deep Learning for Accelerated Photosensitizer Discovery

Jiali Li1,Shidang Xu1,Pengfei Cai1,Xiaoli Liu1,Bin Liu1,Xiaonan Wang1

National University of Singapore1

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F.MT07.05: Materials Representations
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Accelerating Materials Discovery Using Autonomous Experimentation

R. Van Dover1,Michael Thompson1,Carla Gomes1,Bart Selman1,John Gregoire2,Max Amsler1

Cornell University1,California Institute of Technology2

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F.MT07.06: Material Design
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Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models

Zhenpeng Yao1,2,Benjamín Sánchez-Lengeling1,N. Scott Bobbitt3,Benjamin Bucior3,Tom Woo4,Omar Farha3,Randall Snurr3,Alán Aspuru-Guzik1,2

Harvard University1,University of Toronto2,Northwestern University3,University of Ottawa4

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F.MT07.07: Science of Optimization
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Robot-Accelerated, Data-Enabled Investigation into Metal Halide Perovskite Reaction Networks

Emory Chan1,Zhi Li1,Jakob Dahl2,1,Mansoor Ani Nellikkal3,Joshua Schrier4,Alexander Norquist3

Lawrence Berkeley National Laboratory1,University of California, Berkeley2,Haverford College3,Fordham University4

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F.MT07.08: Machine Learning in Theory and First Principles
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Late News: End-to-End Automatic Differentiation for Experimentally-Informed Molecular Dynamics

Wujie Wang1,Simon Axelrod1,Rafael Gomez-Bombarelli1

Massachusetts Institute of Technology1

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F.MT07.03: Constitutive Modeling
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Microscope is a Laboratory—Supervised and Unsupervised Automated Experiment in Scanning Probe and Electron Microscopy

Sergei Kalinin1,Maxim Ziatdinov1,Kyle Kelly1,Ondrej Dyck1,Stephen Jesse1,Andrew Lupini1,Rama Vasudevan1

Oak Ridge National Laboratory1

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F.MT07.08: Machine Learning in Theory and First Principles
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Autonomous Materials Discovery with Boundless Objective-Free Exploration

Koji Tsuda1

The University of Tokyo1

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F.MT07.05: Materials Representations
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Automatic Cyclometalated Iridium(III) Complex Design Using a Data-Driven Fragmentation Representation of Molecules

Hsianghan Hsu1,Seiji Takeda1,Toshiyuki Hama1,Jed Pitera2,Daiju Nakano1

IBM Research - Tokyo1,IBM Almaden Research Center2

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F.MT07.07: Science of Optimization
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A Framework Enabling AI-Assisted High-Throughput Experimentation

Linda Hung1,Brian Rohr1,John Gregoire2,Li Cheng Kao3,Jens Hummelshoej1,Wenhui Li3,Junko Yano3,Steven Torrisi4,Santosh Suram1

Toyota Research Institute1,California Institute of Technology2,Lawrence Berkeley National Laboratory3,Harvard University4

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F.MT07.03: Constitutive Modeling
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Contrasting Error Cancellation in Ab Initio and Machine Learning Predictions of Thermodynamic Stability

Christopher Bartel1,Amalie Trewartha1,Qi Wang2,Alexander Dunn2,1,Anubhav Jain2,Gerbrand Ceder1,2

University of California, Berkeley1,Lawrence Berkeley National Laboratory2

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F.MT07.05: Materials Representations
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Extracting Relevant Features For Understanding and Predicting Metal-Insulator Materials from Machine Learning Models

Alexandru Georgescu1,Aubrey Toland2,Peiwen Ren1,Nicholas Wagner1,Elsa Olivetti2,James Rondinelli1

Northwestern University1,Massachusetts Institute of Technology2

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F.MT07.07: Science of Optimization
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Benchmarking the Performance of Bayesian Optimization Across Multiple Experimental Domains

Harry Liang1,Aldair Gongora2,Zekun Ren3,Zhe Liu1,Armi Tiihonen1,Shijing Sun1,Flore Mekki-Berrada4,Saif Khan4,Daniil Bash5,Kedar Hippalgaonkar5,Keith Brown2,John Fisher1,Tonio Buonassisi1

Massachusetts Institute of Technology1,Boston University2,Singapore-MIT Alliance for Research and Technology3,National University of Singapore4,Agency for Science, Technology and Research (A*STAR)5

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F.MT07.08: Machine Learning in Theory and First Principles
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Available on demand - F.MT07.08.07
Accelerated Search and Optimization of Uniform Pseudo-Magnetic Fields and Magnetic Edges in Strained Monolayer Graphene Using Artificial Intelligence

Zhuofa Chen1,Stefan Sorescu1,Jason Inirio1,Mounika Vutukuru1,Paul Hanakata2,Anna Swan1

Boston University1,Harvard University2

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F.MT07.03: Constitutive Modeling
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F-MT07

Available on demand - F.MT07.03.09
Automated Calculation and Convergence of Defect Transport Tensors

Thomas Swinburne1,Danny Perez2

CNRS / CINaM1,Los Alamos National Laboratory2

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F.MT07.05: Materials Representations
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F-MT07

Available on demand - F.MT07.05.09
Inferring Physical Laws of Material Degradation in Perovskite Solar Cells Using Machine Learning

Richa Naik1,Armi Tiihonen1,Shijing Sun1,Zhe Liu1,Tonio Buonassisi1

Massachusetts Institute of Technology1

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F.MT07.07: Science of Optimization
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F-MT07

Available on demand - F.MT07.07.09
Reassessing High-Dimensional Optimization Strategies Using Interpretable Machine Learning For Perovskite Solar Cell Manufacturing

Zhe Liu1,Nicholas Rolston2,Zekun Ren3,Felipe Oviedo1,Harry Liang1,Reinhold Dauskardt2,Tonio Buonassisi1,3

Massachusetts Institute of Technology1,Stanford University2,Singapore-MIT Alliance for Research and Technology3

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F.MT07.08: Machine Learning in Theory and First Principles
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F-MT07

Available on demand - F.MT07.08.08
Efficient Uncertainty Estimation of Neural Network Potentials with Atomic-Level Resolution Enabled by Replica Ensemble

Wonseok Jeong1,Dongsun Yoo1,Kyuhyun Lee1,Jisu Jung1,Seungwu Han1

Seoul National Univ1

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F.MT07.03: Constitutive Modeling
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F-MT07

Available on demand - F.MT07.03.10
Machine-Learning Approaches to Elucidate Structure-Property Relationships in 2D Materials

Siyu Tian1,Alexandra Carvalho2,Zekun Ren1,Mohammed AlEzzi2,Antonio Castro Neto2,Kostya Novoselov2,Tonio Buonassisi1,3

Singapore-MIT Alliance for Research and Technology1,National University of Singapore2,Massachusetts Institute of Technology3

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F.MT07.05: Materials Representations
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F-MT07

Available on demand - *F.MT07.05.10
Learning to Electrodeposit Corrosion-Resistant Alloy Coatings Through Autonomous Electrochemical Experimentation

Brian DeCost1,Howie Joress1,Bruce Ravel1,Suchismita Sarker2,Apurva Mehta2,Najlaa Hassan1,Trevor Braun1,Jason Hattrick-Simpers1

National Institute of Standards and Technology1,SLAC National Accelerator Laboratory2

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F.MT07.07: Science of Optimization
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F-MT07

Available on demand - *F.MT07.07.10
Accelerated Quantum Dot Development in Flow—Convergence of Flow Chemistry, Colloidal Synthesis and Machine Learning

Milad Abolhasani1

North Carolina State University1

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F.MT07.08: Machine Learning in Theory and First Principles
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F-MT07

Available on demand - F.MT07.08.09
Machine Learning as a Solution to the Electronic Structure Problem

Beatriz Gonzalez del Rio1,Christopher Kuenneth1,Huan Tran1,Rampi Ramprasad1

Georgia Institute of Technology1

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Available on demand - F.MT07.08.10
Prediction on Electron Density of States in Metal Nanoclusters by Atomic Configuration via Machine Learning

Kiyou Shibata1,Teruyasu Mizoguchi1

The University of Tokyo1

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F.MT07.05: Materials Representations
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F-MT07

Available on demand - F.MT07.05.11
Late News: Representations for Data-Driven Material Discovery

Kiran Vaddi1,Olga Wodo1

University at Buffalo1

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F.MT07.07: Science of Optimization
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F-MT07

Available on demand - F.MT07.07.11
Using Quantified Uncertainty to Guide Efficient Phase Diagram Determination via Sequential Learning

Theresa Davey1,Brandon Bocklund2,Zi-Kui Liu2,Ying Chen1

Tohoku University1,The Pennsylvania State University2

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F.MT07.05: Materials Representations
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F-MT07

Available on demand - F.MT07.05.13
Topology-Driven Completion of Chemical Data

Dmitry Zubarev1,Petar Ristoski1

IBM Research - Almaden1

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F.MT07.07: Science of Optimization
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F-MT07

Available on demand - F.MT07.07.12
A Unified Bayesian Approach to Learning Many-Body Potentials

Jonathan Vandermause1,Boris Kozinsky1

Harvard University1

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F.MT07.05: Materials Representations
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F-MT07

Available on demand - F.MT07.05.14
Molecule Representation Governs Predictive Accuracy—Case Study on Predicting Minimum Inhibitory Concentration of Large Molecules

Armi Tiihonen1,Sarah Cox-Vazquez2,Harry Liang1,Zekun Ren3,Noor Titan Putri Hartono1,Shijing Sun1,Guillermo Bazan2,Tonio Buonassisi1

Massachusetts Institute of Technology1,National University of Singapore2,Singapore-MIT Alliance for Research and Technology3

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F.MT07.07: Science of Optimization
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F-MT07

Available on demand - F.MT07.07.14
Automated Workflows for Surface Generation and 0D and 2D Structural and Electronic Optimisation and Analysis via WASP@N and SAINT

Helen Duncan1,Scott Woodley1

University College London1

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