Digital Discovery Blog

Welcome to Digital Discovery!

We are delighted to welcome Professor Joshua Schrier, Fordham University, USA as a new member of the Editorial Board of Digital Discovery.

“Chemistry has always been advanced by the dialog between data and theory. Machine learning, artificial intelligence, simulation, and laboratory automation are new languages for connecting experiment, theory, and computation.”

Joshua Schrier is a physical chemist interested in computational methods to accelerate the discovery of new materials by using a combination of physics-based simulations, cheminformatics, machine learning, and automated experimentation. He is the Kim B. and Stephen E. Bepler Professor of Chemistry at Fordham University in New York City. Prior to joining Fordham in 2018, he was an associate professor at Haverford College, and a Luis W. Alvarez computational sciences postdoctoral fellow at Lawrence Berkeley National Laboratory. As a faculty member, he has received awards including the Dreyfus Teacher-Scholar, U.S. Department of Energy Visiting Faculty, and Fulbright scholar awards.

Read some of Joshua’s recent papers below.

Autonomous experimentation systems for materials development: A community perspective
Eric Stach, Brian DeCost, A. Gilad Kusne, Jason Hattrick-Simpers, Keith A. Brown, Kristofer G. Reyes, Joshua Schrier, Simon Billinge, Tonio Buonassisi, Ian Foster, Carla P. Gomes, John M. Gregoire, Apurva Mehta, Joseph Montoya, Elsa Olivetti, Chiwoo Park, Eli Rotenberg, Semion K. Saikin, Sylvia Smullin Valentin Stanev and Benji Maruyama
Matter, 2021, 4, 2702–2726

Predicting inorganic dimensionality in templated metal oxides
Qianxiang Ai, Davion Marquise Williams, Matthew Danielson, Liam G. Spooner, Joshua A. Engler, Zihui Ding, Matthias Zeller, Alexander J. Norquist, and Joshua Schrier
J. Chem. Phys., 2021,154, 184708

Using automated serendipity to discover how trace water promotes and inhibits lead halide perovskite crystal formation
Philip W. Nega,  Zhi Li, Victor Ghosh, Janak Thapa,  Shijing Sun,  Noor Titan Putri Hartono,  Mansoor Ani Najeeb Nellikkal,  Alexander J. Norquist,  Tonio Buonassisi,  Emory M. Chan, and  Joshua Schrier
Appl. Phys. Lett., 2021, 119, 041903

Robot-Accelerated Perovskite Investigation and Discovery
Zhi Li, Mansoor Ani Najeeb, Liana Alves, Alyssa Z. Sherman, Venkateswaran Shekar, Peter Cruz Parrilla, Ian M. Pendleton, Wesley Wang, Philip W. Nega, Matthias Zeller, Joshua Schrier, Alexander J. Norquist, and Emory M. Chan
Chem. Mater., 2020, 32, 5650–5663

Please join us in welcoming Professor Schrier to Digital Discovery.

Welcome to Digital Discovery!

We are delighted to welcome Dr Kedar Hippalgaonkar, Nanyang Technological University and A*STAR, Singapore as a new member of the Editorial Board of Digital Discovery.

“Digital Research means the development of data-driven platforms, both theoretical and experimental, that can augment scientific pursuit and allow for open-ended materials discovery. I am excited to join the fabulous Editorial team at Digital Discovery to support and propagate this vision!”

Assistant Professor Kedar Hippalgaonkar is a joint appointee with the Materials Science and Engineering Department at Nanyang Technological University (NTU) and a Senior Scientist at the Institute of Materials Research and Engineering (IMRE) at the Agency for Science Technology and Research (A*STAR) in Singapore.  He is a 2020 NRF Fellow and MOE Inauguration Grant Awardee and has received the Materials Horizons (2021) and JMC A (2019) Emerging Investigatorships. He is leading the multi-PI Accelerated Materials Development for Manufacturing (AMDM) program focusing on the development of new materials, processes and optimization using Machine Learning, AI and high-throughput computations and experiments in electronic, thermoelectric, polymeric and structural materials. He led the Pharos Program on Hybrid (inorganic-organic) thermoelectrics for ambient applications from 2016-2020.

Dr Hippalgaonkar is using machine learning and data science for materials discovery. His approach to materials-by-design is built on creating and utilizing materials data by high-performance computing and high-throughput experiments to synthesize and characterize materials for optical and electronic properties.  He is keen on developing tools such as process optimization, design of experiments and materials, and process fingerprinting from materials development to device applications.  His research interests lie in designing functional materials, especially for energy applications. He has fundamental knowledge in solid state physics, 1D (nanowires) and 2D (TMDCs), as well as inorganic-organic (hybrid) materials. His background is in transport properties of materials, specifically in understanding their thermal, optical and thermoelectric properties.

Read some of Kedar’s recent papers below.

Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites
Daniil Bash, Yongqiang Cai, Vijila Chellappan, Swee Liang Wong, Xu Yang, Pawan Kumar, Jin Da Tan, Anas Abutaha, Jayce JW Cheng, Yee‐Fun Lim, Siyu Isaac Parker Tian, Zekun Ren, Flore Mekki‐Berrada, Wai Kuan Wong, Jiaxun Xie, Jatin Kumar, Saif A Khan, Qianxiao Li, Tonio Buonassisi and Kedar Hippalgaonkar
Adv. Func. Materials, 2021, 31, 36, 2102606

Electronic transport descriptors for the rapid screening of thermoelectric materials
Tianqi Deng, Jose Recatala-Gomez, Masato Ohnishi, D. V. Maheswar Repaka, Pawan Kumar, Ady Suwardi, Anas Abutaha, Iris Nandhakumar, Kanishka Biswas, Michael B. Sullivan, Gang Wu, Shiomi, Shuo-Wang Yang and Kedar Hippalgaonkar
Mater. Horiz.., 2021, 8, 2463–2474

Two-step machine learning enables optimized nanoparticle synthesis
Flore Mekki-Berrada, Zekun Ren, Tan Huang, Wai Kuan Wong, Fang Zheng, Jiaxun Xie, Isaac Parker Siyu Tian, Senthilnath Jayavelu, Zackaria Mahfoud, Daniil Bash, Kedar Hippalgaonkar, Saif Khan, Tonio Buonassisi, Qianxiao Li and Xiaonan Wang
npj Comput. Mater., 2021, 7, 55

Inertial effective mass as an effective descriptor for thermoelectrics via data-driven evaluation
Ady Suwardi, Daniil Bash, Hong Kuan Ng, Jose Recatala Gomez, D. V. Maheswar Repaka, Pawan Kumara and Kedar Hippalgaonkar
J. Mater. Chem. A, 2019, 7, 23762–23769

Please join us in welcoming Dr Hippalgaonkar to Digital Discovery.

Welcome to Digital Discovery!

We are delighted to welcome Dr Linda Hung, Toyota Research Institute, USA, as a new member of the Editorial Board of Digital Discovery.

“By coupling machine learning and data science methods with experiment and simulation, we can accelerate the development of new, sustainable materials.”

Linda Hung is a Senior Research Scientist in the Accelerated Materials Design and Discovery division at Toyota Research Institute (TRI). She obtained her PhD in applied and computational mathematics from Princeton University, and has held research positions at the Ecole Polytechnique (France), the University of Illinois Chicago, and the National Institute of Standards and Technology before joining TRI in 2017.

She has a background in density functional theory and other first-principles simulation methods, with applications in computational spectroscopy.  Her current work explores how machine learning can accelerate materials simulation, and how to integrate data-driven methods into discovery workflows. Her research focuses on energy materials, and involves the development of software tools aiming to shorten the materials innovation timeline.

Read some of Linda’s recent papers below:

Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships
Steven B. Torrisi, Matthew R. Carbone, Brian A. Rohr, Joseph H. Montoya, Yang Ha, Junko Yano, Santosh K. Suram and Linda Hung
npj Comput. Mater., 2020, 6, 109

BEEP: A Python library for Battery Evaluation and Early Prediction
Patrick Herring, Chirranjeevi Balaji Gopal, Muratahan Aykol, Joseph H. Montoya, Abraham Anapolsky, Peter M.Attia, William Gent, Jens S.Hummelshøj, Linda Hung, Ha-Kyung Kwon, Patrick Moore, Daniel Schweigert, Kristen A.Severson, Santosh Suram, Zi Yang, Richard D.Braatz and Brian D.Storey
SoftwareX, 2020, 11, 100506

Network analysis of synthesizable materials discovery
Muratahan Aykol, Vinay I. Hegde, Linda Hung, Santosh Suram, Patrick Herring, Chris Wolverton and Jens S. Hummelshøj
Nat. Commun., 2019, 10, 2018

Please join us in welcoming Dr Hung to Digital Discovery!

Welcome to Digital Discovery!

We are delighted to welcome Professor Jason E. Hein, University of British Columbia, Canada, as a new member of the Editorial Board of Digital Discovery.

“The fusion between advanced automation, machine learning and chemical synthesis is rapidly expanding the boundaries of our understanding. This interdisciplinary ecosystem is helping researchers explore further, challenge experimental biases and in my opinion, represents one of the most exciting opportunities imaginable.”

Jason Hein received his BSc in Biochemistry in 2000 and PhD in asymmetric reaction methodology in 2005 from the University of Manitoba (NSERC PGS-A/B, Prof. Philip G. Hultin). In 2006, he became an NSERC postdoctoral research fellow with Professor K. Barry Sharpless and Professor. Valery V. Fokin at the Scripps Research Institute in La Jolla, CA. In 2010, he became a senior research associate with Professor Donna G. Blackmond at the Scripps Research Institute. He began his independent career at the University of California, Merced in 2011, employing in-situ kinetic reaction analysis to rapidly profile and study complex networks of reactions. In 2015, he moved to the University of British Columbia to continue the development of automated reaction analytical technology to serve mechanistic organic chemistry. His research has resulted in a collection of prototype modular robotic tools and integrated analytical hardware which create the first broadly applicable automated reaction profiling toolkit geared toward enabling autonomous research and discovery. He was the co-lead of Project ADA; the world’s first autonomous discovery platform for thin film materials, supported by Natural Resources Canada, co-PI of the MADNESS team supported by the DARPA Accelerated Molecular Discovery Program and an Associate Director of the Acceleration Consortium spearheaded by the University of Toronto.

Read some of Jason’s recent papers below.

A robust new tool for online solution-phase sampling of crystallizations
Andrew J. Kukor, Mason A. Guy, Joel M. Hawkins  and Jason E. Hein
React. Chem. Eng., 2021, DOI: 10.1039/D1RE00284H

Data-Science Driven Autonomous Process Optimization
Melodie Christensen, Lars P. E. Yunker, Folarin Adedeji, Florian Häse, Loïc M. Roch, Tobias Gensch, Gabriel dos Passos Gomes, Tara Zepel, Matthew S. Sigman, Alán Aspuru-Guzik and Jason E. Hein
Commun. Chem., 2021, 4, 112.

Automated Solubility Screening Platform Using Computer Vision
Parisa Shiri, Veronica Lai, Tara Zepel, Daniel Griffin, Jonathan Reifman, Sean Clark, Shad Grunert, Lars P.E. Yunker, Sebastian Steiner, Henry Situ, Fan Yang, Paloma L. Prieto and Jason E. Hein
iScience, 2021, 24, 102176

Real-Time HPLC-MS Reaction Progress Monitoring Using an Automated Analytical Platform
Thomas C. Malig, Josh D. B. Koenig, Henry Situ, Navneet K. Chehal, Philip G. Hultin  and Jason E. Hein
React. Chem. Eng., 2017, 2, 309–314.

Please join us in welcoming Professor Hein to Digital Discovery.