WPI-NanoLSI Special Computational Workshop on Digital Solutions
Date: Wednesday February 8th 2023
Time: 9:30am – 4:30pm
Location: Seminar Hall 4th Floor NanoLSI Building
Event: In person / online
Registration: https://forms.gle/tUNwZ5tVT1ccGPFRA
Contact: nanolsi-office@adm.kanazawa-u.ac.jp

Workshop ProgramFebruary 8th - Wednesday 9:30am – 12:30pm
Adam Foster: Workshop overview, digital workflows, open data, advanced analysis
David Gao: Introduction to digital data and machine learning tools
Filippo Canova: Highly optimized data management tools
February 8th - Wednesday 1:30pm – 4:30 pm
Adam Foster: Advanced analysis of AFM images
Niko Oinonen: Disease recognition from AFM adhesion measurements
Damien Hall: Computational AFM tools for biophysical measurements of dynamic surfaces

Access to the NanoLSI building at Kakuma Campus
For information how to access Kanazawa university (Kakuma campus) from Kanazawa
station please check the website https://nanolsi.kanazawa-u.ac.jp/en/access/
The Nano Life Science Institute is the building framed in red in the map below.
The workshop venue is located on the 4th floor.

Helpful Information
TAXI companies
Ishikawa Kintetsu Taxi phone 076-221-3265
Fuji Taxi phone 076-237-1020
Kamome Taxi phone 076-231-1128

NanoLSI Administration

Office hours: 8:30am-5pm
Contact: nanolsi-office@adm.kanazawa-u.ac.jp

WPI-NanoLSI Special Computational Workshop on Digital Solutions
Date: Wednesday February 8th 2023
Time: 9:30am - 4:30pm
Location: Seminar Hall 4th Floor NanoLSI Building
Event: In person / online
Registration: https://forms.gle/tUNwZ5tVT1ccGPFRA
Contact: nanolsi-office@adm.kanazawa-u.ac.jp

Topics covered•••••Introduction to digital data
Data management tools
Integration of machine learning techniques
Case studies in AFM
Open discussion of audience data issues and solutions.

Digital data management, advanced analysis, and machine learning
Modern research efforts benefit from a rapidly increasing ability to generate a wealth ofexperimental and modelling data. Recent developments in data science, machine learning,and automation provide powerful opportunities to leverage this research data. To use
scientific data as a resource beyond its initial publication, database infrastructures and digital
workflows must be established to collect, store, organise and analyse the data, with recordsof how it was produced, and how it was processed or transformed afterwards. Good
management of research data is of critical importance to knowledge-led discovery andinnovation, and is increasingly embedded in the requirements of research journals andfunding bodies. It is a prerequisite step in the application of data science and machine learningtechniques in research. In this workshop, we highlight some examples of modern data
management and discuss case studies involving the application of machine learning toscientific data. Emphasis is placed on applications to Scanning Probe Microscopy (SPM) in
general and Atomic Force Microscopy (AFM) in particular.

Instructors
Adam Foster (Aalto University)
David Gao (NanoLayers Incorporated)
Filippo Canova (NanoLayers Incorporated)
Nikko Oinonen (Aalto University)
Damien Hall (Kanazawa University)

ProgramFebruary 8th - Wednesday 9:30am – 12:30pm
Adam Foster: Workshop overview, digital workflows, open data, advanced analysis
David Gao: Introduction to digital data and machine learning tools
Filippo Canova: Highly optimized data management tools
February 8th - Wednesday 1:30pm – 4:30 pm
Adam Foster: Advanced analysis of AFM images
Niko Oinonen: Disease recognition from AFM adhesion measurements
Damien Hall: Computational AFM tools for biophysical measurements of dynamic surfaces

References
1. Data-driven materials science: status, challenges and perspectives - L. Himanen, A.
Geurts, A. S. Foster and P. Rinke, Adv. Sci. (2019) 1900808
2. DScribe: Library of Descriptors for Machine Learning in Materials Science - Lauri
Himanen, Marc O Jäger, Eiaki V Morooka, Filippo Federici Canova, Yashasvi SRanawat, David Z Gao, Patrick Rinke and Adam S Foster,Comp. Phys. Comm. 247 (2020) 106949
3. Machine Learning at the (Sub)Atomic Scale: Next Generation Scanning ProbeMicroscopy - Gordon, Oliver M., and Philip J. Moriarty, Machine Learning: Science andTechnology 1 (2020) 023001
4. AtomAI Framework for Deep Learning Analysis of Image and Spectroscopy Data inElectron and Scanning Probe Microscopy, Ziatdinov, Maxim, Ayana Ghosh, Chun Yin(Tommy) Wong, and Sergei V. Kalinin, Nature Machine Intelligence 4 (2022) 1101
5. Precise atom manipulation through deep reinforcement learning - I-Ju Chen, MarkusAapro, Abraham Kipnis, Alexander Ilin, Peter Liljeroth and Adam S. Foster, Nat.
Commun. 13 (2022) 7499
6. Generalised deep-learning workflow for the prediction of hydration layers oversurfaces - Yashasvi S. Ranawat, Ygor M. Jaques and Adam S. Foster, J. Mol.
Liquids 367 (2022) 120571
7. Electrostatic Discovery Atomic Force Microscopy - Niko Oinonen, Chen Xu, Benjamin
Alldritt, Prokop Hapala, Filippo Federici
8. Canova, Fedor Urtev, Ondřej Krejčí, Juho Kannala, Peter Liljeroth and Adam S. Foster,ACS Nano 16 (2022) 89
9. Practical considerations for feature assignment in high-speed AFM of live cell
membranes - Damien Hall and Adam Foster. Biophysics and Physicobiology. 19e190016, 1-21

Instructors
Instructor:

Adam Foster

Email:

adam.foster@aalto.fi

Position:

Professor

Institution:

Department of Applied Physics, Aalto University

Biography: Adam Foster is an overseas PI at the WPI Nano Life Science Institute (WPI-NanoLSI)
and leader of the Surface and Interfaces at the Nanoscale (SIN) group at Aalto University in
Finland. He obtained his PhD in University College London in 2000, before moving to Helsinki
University of Technology in the same year. He obtained a senior academy fellowship in 2004,
followed by a professorship in Tampere University of Technology in 2009. He joined Aalto
University in 2012 and has held a visiting position in Kanazawa since 2015. Foster is acomputational physicist applying his expertise in simulations to a wide range of areas
including molecular self-assembly, solid-liquid interfaces and quantum materials, often in
conjunction with scanning probe microscopy research. The SIN group generally work at theatomistic level, looking from both the quantum and classical viewpoints and working closelywith experimentalists. More recently, they have focused on developing and applying dataintensive and machine learning approaches in materials research.
Instructor:David Gao

Email:

david@nanolayers.com

Position:

Director

Institution:

Nanolayers Research Computing Ltd.

Biography: Director and founder of Nanolayers Research Computing LTD (UK) and Nanolayers
OU (ES), David has a B.Sc. in chemistry from UC Berkeley, a Ph.D. in physics from University
College London, and is currently an Assoc. Prof. in the Materials Theory group at theNorwegian Institute of Science and Technology. David also holds an Executive M.B.A. from theQuantic School of Business & Technology and has industrial experience working for Chevron
Corp. as a chemical engineer and chemist and is acutely aware of the research anddevelopment needs of similar commercial entities. His career there was focused on bridging
the gap between chemistry and engineering and now has turned his attention to bridging asimilar gap between molecular modelling, machine learning, and industrial research anddevelopment. David has been a long-time member of the European Materials Modelling
Council (EMMC) as a stakeholder, an active participant in CEN consortium workshop
agreements, and contributed to developing EU standards for metadata, data management,and digital research. He has authored 32 papers in international, high impact, refereed
journals and has ongoing industrial research projects funded by companies including Chevron,Applied Materials, and Nantero, and drives the research digitalization efforts of several
international research projects including the Horizon Europe Mast3rboost and the joint
iUK/NSERC ALANN projects.

Instructor:

Filippo Federici Canova

Email:

filippo.federici@aalto.fi

Position:

Scientific Director at NanoLayers

Institution:

NanoLayers Incorporated

Biography: Filippo is the co-founder and scientific director for Nanolayers Research
Computing, where he leads the business' development of scientific data management
solutions, the design of lubricants, and image recognition and machine learning methods formaterials science and image recognition. Filippo holds a M.Sc. in experimental physics and adoctorate in Computational Physics from the Tampere University of Technology in Finland.
His main research focus was simulation of nc-AFM contrast formation and energy dissipation
mechanisms. During his post doc he investigated the frictional dynamics of nanoconfined ionicliquids (AIMR, Tohoku University) and prediction of frictional properties with machinelearning methods (ASCI, Aalto University). With extensive knowledge of advanced
experimental and computational systems, Filippo has been instrumental in the developmentof some of Nanolayers most significant solutions, including the Nature Conservancy wildlife
image recognition system and Cassandra Scientific databasing system.

Instructor:

Niko Oinonen

Email:

niko.oinonen@aalto.fi

Position:

PhD Student D3

Institution:

Department of Applied Physics, Aalto University

Biography: Niko Oinonen is a doctoral student the Surfaces and interfaces at the Nanoscalegroup at Aalto University, Finland, where he also graduated with the degree of M.Sc. in 2019.
The topic of his doctoral research is applying machine learning methods to the interpretationof high-resolution atomic force microscopy images. His work focuses on the design anddevelopment of neural networks for this task as well as the development of software foratomic force microscopy simulations.
Instructor:

Damien Hall

Email:

hall.damien@staff.kanazawa-u.ac.jp

Position:

Assistant Professor

Institution:

WPI - Nano Life Science Institute, Kanazawa University, Japan.

Biography: Damien Hall received an undergraduate degree in chemistry (1994) and a PhD inphysical biochemistry in 2000 from the University of Queensland. In January of 2021 he wasappointed as a Specially Appointed Assistant Professor at the WPI Nano Life Science Institutelocated at Kanazawa University, Japan. Prior to this he held a series of personal five-year
research fellowships at the National Institutes of Health, Osaka University, The Australian
National University, University of Tsukuba and the University of Cambridge. In addition to his
direct research activities, Damien has been the Chief Editor of the International Union for Pureand Applied Biophysics (IUPAB) journal, ‘Biophysical Reviews’ since 2019.