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.

Welcome to Digital Discovery!

We are delighted to welcome Professor Yousung Jung, KAIST, South Korea, as a new member of the Editorial Board of Digital Discovery.

“Just as computational chemistry has emerged as a new branch of chemistry after the development of computers and algorithms to use them, digital chemistry dealing with data, machine learning, and automation may become a new discipline in 21st century chemistry. I am delighted to be part of Digital Discovery which can play a central role in the upcoming advancement of that field.”

Yousung Jung is a Professor of Chemical and Biomolecular Engineering at KAIST. His research background and current interests involve quantum chemistry and machine learning to develop efficient methods for fast and accurate simulations of complex molecular and materials systems, and their applications towards the understanding of molecules and materials for new discovery. Some of his recent works include the use of data science and machine learning to understand the structure-property-synthesizability relations for molecules and materials, and using the obtained knowledge for inverse design. He received his PhD in Theoretical Chemistry from the University of California, Berkeley, with Martin Head-Gordon. After postdoctoral work at Caltech with Rudy Marcus, he joined the faculty at KAIST in 2009. He has received the following awards: the Hanseong Science Award from Hanseong Son Jae Han Foundation; the KAIST Technology Innovation Award; the Pole Medal by the Asia-Pacific Association of Theoretical and Computational Chemists; a Korean Chemical Society Young Physical Chemist Award, and a KCS-Wiley Young Chemist Award.

Read some of Yousung’s recent papers below.

Predicting potentially hazardous chemical reactions using an explainable neural network
Juhwan Kim, Geun Ho Gu, Juhwan Noh, Seongun Kim, Suji Gim, Jaesik Choi and Yousung Jung
Chem. Sci, 2021, 12, 11028–11037

Machine-enabled inverse design of inorganic solid materials: promises and challenges
Juhwan Noh, Geun Ho Gu, Sungwon Kim and Yousung Jung
Chem. Sci., 2020, 11, 4871–4881

Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning
Jidon Jang, Geun Ho Gu, Juhwan Noh, Juhwan Kim and Yousung Jung
J. Am. Chem. Soc.2020, 142, 18836–18843

Please join us in welcoming Professor Jung to Digital Discovery.

Welcome to Digital Discovery!

We are delighted to welcome Dr Anat Milo, Ben-Gurion University of the Negev, Israel, as a new member of the Editorial Board of Digital Discovery.

“There remains little doubt that the path to a more efficient process for chemical discovery passes through organization, digitalization and curation of data and its broad distribution. The cherry on top is that we also get to use this data to gain a better understanding of the natural world.”

Anat Milo received her BSc/BA in Chemistry and Humanities from the Hebrew University of Jerusalem in 2001, her MSc from UPMC Paris in 2004 with Berhold Hasenknopf, and her PhD from the Weizmann Institute of Science in 2011 with Ronny Neumann. Her postdoctoral studies at the University of Utah with Matthew Sigman focused on developing physical organic descriptors and data analysis approaches for chemical reactions. At the end of 2015 she returned to Israel to join the Department of Chemistry at Ben-Gurion University of the Negev, where her research group develops experimental, statistical, and computational strategies for identifying molecular design principles in catalysis with a particular focus on stabilizing and intercepting reactive intermediates by second sphere interactions.

Read some of Anat’s recent papers below.

Designing the Secondary Coordination Sphere in Small-Molecule Catalysis
Inbal L. Zak, Santosh C. Gadekar, Anat Milo
Synlett, 2021, 32, 329–336

Unravelling mechanistic features of organocatalysis with in situ modifications at the secondary sphere
Vasudevan Dhayalan, Santosh C. Gadekar, Zayed Alassad and Anat Milo
Nat. Chem., 2019, 11, 543–551

The Art of Organic Synthesis in the Age of Automation
Anat Milo
Isr. J. Chem., 2018, 58, 131–135

Please join us in welcoming Dr Milo to Digital Discovery.

Welcome to Digital Discovery!

We are delighted to welcome Professor Lilo D. Pozzo, University of Washington, USA, as a new member of the Editorial Board of Digital Discovery.

“We started experimenting with open-source instrumentation and high-throughput algorithms to eliminate bottlenecks in our experimental work. This accelerated and has now taken a life of its own to integrate AI and data-driven automation to ‘close the loop’ and drive autonomous experimentation.”

Lilo D. Pozzo is the Boeing-Roundhill Professor of Chemical Engineering and interim chair of the Department of Materials Science and Engineering at the University of Washington in Seattle. Her research focuses on controlling and manipulating the structure of soft matter for applications in healthcare, alternative energy, chemical manufacturing and separations. Her group also focuses on developing and utilizing experimental high-throughput tools and techniques to accelerate deployment timelines for new materials, and she is an expert in the use of neutron and x-ray scattering techniques for the analysis of colloids and polymers. Professor Pozzo obtained her BS in Chemical Engineering from the University of Puerto Rico at Mayagüez in 2001 and her PhD in Chemical Engineering from Carnegie Mellon University in Pittsburgh PA in 2006. She also worked at the NIST Center for Neutron Research as a postdoctoral fellow, and has served at the University of Washington since 2007. She has also been recognized with the Early Career Award from the US Department of Energy and with the C3E Award for Women in Clean Energy.

Read some of Lilo’s recent papers below.

Contrast-Variation Time-Resolved Small-Angle Neutron Scattering Analysis of Oil-Exchange Kinetics Between Oil-in-Water Emulsions Stabilized by Anionic Surfactants
Yi-Ting Lee and Lilo D. Pozzo
Langmuir, 2019, 35, 15192–15203

On-Demand Sonochemical Synthesis of Ultrasmall and Magic-Size CdSe Quantum Dots in Single-Phase and Emulsion Systems
Ryan Kastilani, Brittany P. Bishop, Vincent C. Holmberg, and Lilo D. Pozzo
Langmuir, 2019, 35, 16583–16592

Assessment of molecular dynamics simulations for amorphous poly(3-hexylthiophene) using neutron and X-ray scattering experiments
Caitlyn M. Wolf, Kiran H. Kanekal, Yeneneh Y. Yimer, Madhusudan Tyagi, Souleymane Omar-Diallo,   Viktoria Pakhnyuk, Christine K. Luscombe, Jim Pfaendtner and Lilo D. Pozzo
Soft Matter, 2019, 15, 5067–5083

Please join us in welcoming Professor Pozzo to Digital Discovery.

Recently, Editor-in-Chief of Digital Discovery, Alan Aspuru-Guzik, shared his thoughts on the journal and why it is an important new destination for digital chemistry research.  Explore our recent interview with him below.

"If you have something that you feel is scopeless, don’t be hopeless – give us a call… maybe we’ll put it in #DigitalDiscovery – the future is what we want to capture in our journal!" @A_Aspuru_Guzik

For more from Alán and more about #DigitalDiscovery
👉 https://t.co/Nqr0jmU9Cb pic.twitter.com/Z1FJQzaDS4

— Royal Society of Chemistry (@RoySocChem) May 28, 2021

“If you want to collaborate across timezones and geographical divisions and cultures, data is king, data speaks, data is what you need to think about. It will democratise science. It will make people from all over the world be able to pick it up sooner. " @A_Aspuru_Guzik pic.twitter.com/Is2DKd2Qlb

— Royal Society of Chemistry (@RoySocChem) May 28, 2021

"We will be encouraging our authors to share ALL their experimentation data – good or bad, failed or wrong – the AI and all these tools drive on negative data. Datasets balanced between positive and negative data are kind of like what feeds the monster of AI" @A_Aspuru_Guzik pic.twitter.com/LW7oKBVbIn

— Royal Society of Chemistry (@RoySocChem) May 28, 2021

"The big question behind #DigitalDiscovery is we want to accelerate scientific discovery – to think about how to discover things faster either for discovery or engineering purposes. We want an inclusive journal where these advances are featured and described." @A_Aspuru_Guzik pic.twitter.com/3BnSiwETO0

— Royal Society of Chemistry (@RoySocChem) May 28, 2021

Professor Alan Aspuru-Guzik joins as Editor-in-Chief

Digital Discovery will cover the application of machine learning to solve scientific problems, so will be home to groundbreaking computational research from the areas of chemistry, biology, physics, and materials & biomedical sciences.

The journal will be open access with all article processing charges (APCs) waived until mid-2024, to ensure as many people as possible have the opportunity to publish and read the top papers in this field.

We are honoured to have Professor Aspuru-Guzik leading our Editorial team.

“I am excited to be editor-in-chief of Digital Discovery. In its pages, we aim to capture the top research at the intersection of chemistry, materials science and biotechnology with topics related to machine learning, high-throughput computational and experimental screening in order to accelerate the process of scientific discovery.

“The ‘digital transformation’ of the chemical industry is a huge driver for the twenty-first century and we want Digital Discovery to be the premier venue for papers related to this topic.”

Alán Aspuru-Guzik is a professor of Chemistry and Computer Science at the University of Toronto and is also the Canada 150 Research Chair in Theoretical Chemistry and a Canada CIFAR AI Chair at the Vector Institute. He is a CIFAR Lebovic Fellow in the Biologically Inspired Solar Energy program. Alán also holds an Google Industrial Research Chair in Quantum Computing. Alán is the director of the Acceleration Consortium, a University of Toronto-based strategic initiative that aims to gather researchers from industry, government and academia around pre-competitive research topics related to the lab of the future.

Alán began his independent career at Harvard University in 2006 and was a Full Professor at Harvard University from 2013-2018. He received his B.Sc. from the National Autonomous University of Mexico (UNAM) in 1999 and obtained a PhD from the University of California, Berkeley in 2004, where he was also a postdoctoral fellow from 2005-2006.

Alán conducts research in the interfaces of quantum information, chemistry, machine learning and chemistry. He was a pioneer in the development of algorithms and experimental implementations of quantum computers and quantum simulators dedicated to chemical systems. He has studied the role of quantum coherence in the transfer of excitonic energy in photosynthetic complexes and has accelerated the discovery by calculating organic semiconductors, organic photovoltaic energy, organic batteries and organic light-emitting diodes. He has worked on molecular representations and generative models for the automatic learning of molecular properties. Currently, Alán is interested in automation and “autonomous” chemical laboratories for accelerating scientific discovery.

Among other recognitions, he received the Google Focused Award for Quantum Computing, the Sloan Research Fellowship, The Camille and Henry Dreyfus Teacher-Scholar award, and was selected as one of the best innovators under the age of 35 by the MIT Technology Review. He is a member of the American Physical Society and an elected member of the American Association for the Advancement of Science (AAAS) and received the Early Career Award in Theoretical Chemistry from the American Chemical Society.

Alán is editor-in-chief of the journal Digital Discovery as well as co-founder of Zapata Computing and Kebotix.

Professor Aspuru-Guzik is a pioneering leader in the field of machine learning and quantum information.  Read some of his recent publications below.

Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
AkshatKumar Nigam, Robert Pollice, Mario Krenn, Gabriel dos Passos Gomes and Alán Aspuru-Guzik
Chem. Sci., 2021, 12, 7079-7090

A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers
Jakob S. Kottmann, Abhinav Anand and Alán Aspuru-Guzik
Chem. Sci., 2021, 12, 3497-3508
(From the 2021 Chemical Science HOT article collection)

Please join us in welcoming Professor Aspuru-Guzik to Digital Discovery.

Best wishes,

the Editorial team @ Digital Discovery

DigitalDiscovery-rsc@rsc.org