1. The document discusses using artificial intelligence and the internet of things for healthcare applications.
2. It provides contact information for Ichigaku Takigawa of the Wireless Promotion Center at RIKEN to discuss potential collaborations.
3. The document includes links to additional information about Takigawa's work on the website https://itakigawa.github.io/news.html and slide presentation https://www.slideshare.net/itakigawa/presentations.
This document summarizes the history and activities of SIG-FPAI, a special interest group on artificial intelligence and natural language processing in Japan. It discusses past annual meetings and key topics discussed. It also provides an overview of the development of AI and the internet in Japan from the 1980s to present day. Key events and technologies discussed include the emergence of ISPs in the early 1990s, the rise of search engines and e-commerce in the late 1990s/early 2000s, and the growth of social media and mobile internet in the mid-2000s.
The document discusses machine learning algorithms for boosting including LightGBM, See5/C5.0, Cubist, CART, MARS, TreeNet, Random Forests, CatBoost, TFBoost, and TenscentBoost. It provides a brief overview of each algorithm and its developer.
The document discusses machine learning algorithms for classification and regression. It compares different tree-based algorithms such as LightGBM, See5/C5.0, CART, Random Forests, CatBoost, TFBoost, and TenscentBoost. These algorithms are useful for tasks like prediction, risk assessment, and decision making.
The document discusses a deep learning model for analyzing biomedical documents. It was developed by researchers at the RIKEN Center for Biosystems Dynamics Research in Japan. The model uses an attention-based hierarchical graph convolutional network to analyze documents at the word, sentence, and paragraph level to extract important information.
This bioinformatics lesson is brought to you by the letter 'W'Keith Bradnam
The document describes a typical bioinformatics workflow for analyzing Illumina sequencing data. It involves several common processing steps: removing adapter contamination, trimming reads for quality, mapping reads to a genome or transcriptome, filtering for uniquely mapped reads, and filtering for high quality alignments. Each step progressively reduces the total number of reads until arriving at a data set suitable for final analysis. The document emphasizes understanding why each step is important and how it affects the data. It also provides a tip to use the "ls -ltr" command after each step to check that output files were properly created and contain data.
블록체인 열린 가능성을 확인하다Jisu Park
1. Blockchain technology has developed rapidly since 2008 with the emergence of Bitcoin. It is now being applied in various fields beyond cryptocurrency such as education and social media.
2. There are ongoing discussions around developing new consensus mechanisms to improve scalability and efficiency compared to the original Proof-of-Work mechanism used in Bitcoin. Proof-of-Stake and other alternatives are being explored.
3. As blockchain technology continues to evolve, it has the potential to transform industries and create new business models through decentralized applications, initial coin offerings, and new platforms that facilitate interaction between users, content, and advertisers in a distributed manner.
This document summarizes the history and activities of SIG-FPAI, a special interest group on artificial intelligence and natural language processing in Japan. It discusses past annual meetings and key topics discussed. It also provides an overview of the development of AI and the internet in Japan from the 1980s to present day. Key events and technologies discussed include the emergence of ISPs in the early 1990s, the rise of search engines and e-commerce in the late 1990s/early 2000s, and the growth of social media and mobile internet in the mid-2000s.
The document discusses machine learning algorithms for boosting including LightGBM, See5/C5.0, Cubist, CART, MARS, TreeNet, Random Forests, CatBoost, TFBoost, and TenscentBoost. It provides a brief overview of each algorithm and its developer.
The document discusses machine learning algorithms for classification and regression. It compares different tree-based algorithms such as LightGBM, See5/C5.0, CART, Random Forests, CatBoost, TFBoost, and TenscentBoost. These algorithms are useful for tasks like prediction, risk assessment, and decision making.
The document discusses a deep learning model for analyzing biomedical documents. It was developed by researchers at the RIKEN Center for Biosystems Dynamics Research in Japan. The model uses an attention-based hierarchical graph convolutional network to analyze documents at the word, sentence, and paragraph level to extract important information.
This bioinformatics lesson is brought to you by the letter 'W'Keith Bradnam
The document describes a typical bioinformatics workflow for analyzing Illumina sequencing data. It involves several common processing steps: removing adapter contamination, trimming reads for quality, mapping reads to a genome or transcriptome, filtering for uniquely mapped reads, and filtering for high quality alignments. Each step progressively reduces the total number of reads until arriving at a data set suitable for final analysis. The document emphasizes understanding why each step is important and how it affects the data. It also provides a tip to use the "ls -ltr" command after each step to check that output files were properly created and contain data.
블록체인 열린 가능성을 확인하다Jisu Park
1. Blockchain technology has developed rapidly since 2008 with the emergence of Bitcoin. It is now being applied in various fields beyond cryptocurrency such as education and social media.
2. There are ongoing discussions around developing new consensus mechanisms to improve scalability and efficiency compared to the original Proof-of-Work mechanism used in Bitcoin. Proof-of-Stake and other alternatives are being explored.
3. As blockchain technology continues to evolve, it has the potential to transform industries and create new business models through decentralized applications, initial coin offerings, and new platforms that facilitate interaction between users, content, and advertisers in a distributed manner.
This document contains a discussion of AI technologies from 2023, including ChatGPT, DALL-E, Copilot, and developments from Microsoft, Google, Apple, and other companies. It also discusses gradient descent optimization techniques like momentum, AdaGrad, RMSProp and Adam for training neural networks. Methods for calculating gradients using the chain rule are explained for multi-layer models.
The document discusses the rise of major technology companies and social networks. It notes that Google was founded in 1996 and Facebook in 2004, and that smartphones running iOS and Android became popular around 2008-2009. It also mentions that Japan's GDP ranking may improve to #1 by 2023, surpassing other countries, and that the integration of technologies like artificial intelligence and the internet of things will continue to change our lives. Charts show the growth of data and devices over time.
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneo...Ichigaku Takigawa
Video https://youtu.be/P4QogT8bdqY
ACS Spring 2023 Symposium on AI-Accelerated Scientific Workflow
https://acs.digitellinc.com/acs/sessions/526630/view
ACS SPRING 2023 ———— Crossroads of Chemistry
Indianapolis, IN & Hybrid, March 26-30
https://www.acs.org/meetings/acs-meetings/spring-2023.html
Slide PDF
https://itakigawa.page.link/acs2023spring
Our Paper
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach (2022, ChemRxiv)
https://doi.org/10.26434/chemrxiv-2022-695rj
Ichi Takigawa
https://itakigawa.github.io/
The document appears to be a research paper discussing the relationship between weight (g) and distance (cm) when objects are thrown. It contains several scatter plots with data points showing the weight and distance of different objects. The paper suggests running regression analysis on the data to determine the linear relationship between weight and distance. Additional analysis may include investigating the effects of other variables like air resistance.
The document contains contact information for Ichigaku Takigawa including their email address ichigaku.takigawa@riken.jp, personal website URL https://itakigawa.github.io/, and mentions they are working with IBISML and ATR on materials informatics and bioinformatics. It also includes a link to their page https://itakigawa.page.link/IBISML for a PDF document.
The document discusses the Rubik's Cube and cubing as a competitive sport. It provides an overview of popular cubing methods like CFOP and compares the number of combinations for different puzzle sizes. It also mentions top cubing brands and provides statistics on the growth of the cubing market in recent years.
1. The document discusses issues related to technology companies and artificial intelligence, focusing on topics like social media, big data, and privacy concerns.
2. It notes that companies like Google, Facebook, Amazon, Apple, and Microsoft now dominate the global technology industry and have significant influence over people's lives and access to information.
3. Concerns are raised about the use of personal data by these companies and how technologies like social media, internet of things, and artificial intelligence could impact privacy, democracy, and society.
The document appears to be a biography or CV for Ichigaku Takigawa. It details his employment history from 1995-2004 at an unnamed organization, 2004 at another unnamed organization, 2005-2011 at the RIKEN Center for Developmental Biology, and 2012-2018 at the Japan Science and Technology Agency. In 2019 he became the director of the RIKEN Center for Integrative Medical Sciences. His research focuses on induced pluripotent stem cells.
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
- The document contains the results of an experiment with multiple data points plotted as dots across various x and y-axis values.
- There are a large number of data points densely plotted in the graph, with some outliers at the edges.
- The data points are recorded measurements from an experiment, but no other context is provided about the experiment, variables, or what is being measured.
Machine Learning for Molecular Graph Representations and GeometriesIchigaku Takigawa
Dec 1, 2021, Pacifico Yokohama, Japan.
Symposium 1AS-17 "Data science and machine learning: Tackling the Noise and Heterogeneity of the Real World"
The 44th Annual Meetingn of the Molecular Biology Society of Japan
https://www2.aeplan.co.jp/mbsj2021/english/designation/index.html
The document provides a self-introduction by Takigawa Ichigaku, who specializes in machine learning and data-driven natural science research, particularly those involving discrete structures. It outlines his work experience and current affiliations with RIKEN and Hokkaido University. It then previews the topics to be covered in the talk, including machine learning applications in molecular representation and chemical reaction design, as well as challenges in interpreting machine learning models.
This document contains a discussion of AI technologies from 2023, including ChatGPT, DALL-E, Copilot, and developments from Microsoft, Google, Apple, and other companies. It also discusses gradient descent optimization techniques like momentum, AdaGrad, RMSProp and Adam for training neural networks. Methods for calculating gradients using the chain rule are explained for multi-layer models.
The document discusses the rise of major technology companies and social networks. It notes that Google was founded in 1996 and Facebook in 2004, and that smartphones running iOS and Android became popular around 2008-2009. It also mentions that Japan's GDP ranking may improve to #1 by 2023, surpassing other countries, and that the integration of technologies like artificial intelligence and the internet of things will continue to change our lives. Charts show the growth of data and devices over time.
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneo...Ichigaku Takigawa
Video https://youtu.be/P4QogT8bdqY
ACS Spring 2023 Symposium on AI-Accelerated Scientific Workflow
https://acs.digitellinc.com/acs/sessions/526630/view
ACS SPRING 2023 ———— Crossroads of Chemistry
Indianapolis, IN & Hybrid, March 26-30
https://www.acs.org/meetings/acs-meetings/spring-2023.html
Slide PDF
https://itakigawa.page.link/acs2023spring
Our Paper
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach (2022, ChemRxiv)
https://doi.org/10.26434/chemrxiv-2022-695rj
Ichi Takigawa
https://itakigawa.github.io/
The document appears to be a research paper discussing the relationship between weight (g) and distance (cm) when objects are thrown. It contains several scatter plots with data points showing the weight and distance of different objects. The paper suggests running regression analysis on the data to determine the linear relationship between weight and distance. Additional analysis may include investigating the effects of other variables like air resistance.
The document contains contact information for Ichigaku Takigawa including their email address ichigaku.takigawa@riken.jp, personal website URL https://itakigawa.github.io/, and mentions they are working with IBISML and ATR on materials informatics and bioinformatics. It also includes a link to their page https://itakigawa.page.link/IBISML for a PDF document.
The document discusses the Rubik's Cube and cubing as a competitive sport. It provides an overview of popular cubing methods like CFOP and compares the number of combinations for different puzzle sizes. It also mentions top cubing brands and provides statistics on the growth of the cubing market in recent years.
1. The document discusses issues related to technology companies and artificial intelligence, focusing on topics like social media, big data, and privacy concerns.
2. It notes that companies like Google, Facebook, Amazon, Apple, and Microsoft now dominate the global technology industry and have significant influence over people's lives and access to information.
3. Concerns are raised about the use of personal data by these companies and how technologies like social media, internet of things, and artificial intelligence could impact privacy, democracy, and society.
The document appears to be a biography or CV for Ichigaku Takigawa. It details his employment history from 1995-2004 at an unnamed organization, 2004 at another unnamed organization, 2005-2011 at the RIKEN Center for Developmental Biology, and 2012-2018 at the Japan Science and Technology Agency. In 2019 he became the director of the RIKEN Center for Integrative Medical Sciences. His research focuses on induced pluripotent stem cells.
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
- The document contains the results of an experiment with multiple data points plotted as dots across various x and y-axis values.
- There are a large number of data points densely plotted in the graph, with some outliers at the edges.
- The data points are recorded measurements from an experiment, but no other context is provided about the experiment, variables, or what is being measured.
Machine Learning for Molecular Graph Representations and GeometriesIchigaku Takigawa
Dec 1, 2021, Pacifico Yokohama, Japan.
Symposium 1AS-17 "Data science and machine learning: Tackling the Noise and Heterogeneity of the Real World"
The 44th Annual Meetingn of the Molecular Biology Society of Japan
https://www2.aeplan.co.jp/mbsj2021/english/designation/index.html
The document provides a self-introduction by Takigawa Ichigaku, who specializes in machine learning and data-driven natural science research, particularly those involving discrete structures. It outlines his work experience and current affiliations with RIKEN and Hokkaido University. It then previews the topics to be covered in the talk, including machine learning applications in molecular representation and chemical reaction design, as well as challenges in interpreting machine learning models.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...Travis Hills MN
By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
7. Empirical optimization or "Edisonian empiricism"
!7
( )
feedback
Thomas Edison
• Genius is 1% inspiration and 99% perspiration.
• There is no substitute for hard work.
• I have not failed. I've just found 10,000 ways that won't work.
:
速
" (empirical/inductive)" " (rational/deductive)"
17. Use and Abuse of Regression (1966)
!17
😉
Data-driven( ) 😅
18. Use and Abuse of Regression (1966)
!18
"one of the great statistical minds of the 20th century"
George E. P. Box (1919-2013)
https://en.wikipedia.org/wiki/All_models_are_wrong
"Essentially, all models are wrong,
but some are useful"
1.
2.
: ( )
Use 😆
Abuse 😫
... 😅
27. Data-driven vs Theory-driven
!27
David Hand
Data-driven Theory-driven
All models are wrong, but some are useful
(George Box)
Theory-driven models can be wrong
But data-driven models cannot be wrong
or right
Data-driven are not trying to describe an underlying reality.
so they could be poor or useless, but not wrong
But are merely intended to be useful
http://videolectures.net/kdd2018_hand_data_science/
28. With enough data, the numbers
speak for themselves.
Chris Anderson (2008)
cf.
58. Key
!58
( ) ( + )
( )
In-House + Public +
+ Quality Control / Annotations
Multilevel
59. !59
REVIEW
Inverse molecular design using
machine learning: Generative models
for matter engineering
Benjamin Sanchez-Lengeling1
and Alán Aspuru-Guzik2,3,4
*
The discovery of new materials can bring enormous societal and technological progress. In this
context, exploring completely the large space of potential materials is computationally
intractable. Here, we review methods for achieving inverse design, which aims to discover
tailored materials from the starting point of a particular desired functionality. Recent advances
from the rapidly growing field of artificial intelligence, mostly from the subfield of machine
learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular
design are being proposed and employed at a rapid pace. Among these, deep generative models
have been applied to numerous classes of materials: rational design of prospective drugs,
synthetic routes to organic compounds, and optimization of photovoltaics and redox flow
batteries, as well as a variety of other solid-state materials.
M
any of the challenges of the 21st century
(1), from personalized health care to
energy production and storage, share a
common theme: materials are part of
the solution (2). In some cases, the solu-
tions to these challenges are fundamentally
limited by the physics and chemistry of a ma-
terial, such as the relationship of a materials
bandgap to the thermodynamic limits for the
generation of solar energy (3).
Several important materials discoveries arose
by chance or through a process of trial and error.
For example, vulcanized rubber was prepared in
the 19th century from random mixtures of com-
pounds, based on the observation that heating
with additives such as sulfur improved the
rubber’s durability. At the molecular level, in-
dividual polymer chains cross-linked, forming
bridges that enhanced the macroscopic mechan-
ical properties (4). Other notable examples in
this vein include Teflon, anesthesia, Vaseline,
Perkin’s mauve, and penicillin. Furthermore,
these materials come from common chemical
compounds found in nature. Potential drugs
either were prepared by synthesis in a chem-
ical laboratory or were isolated from plants,
soil bacteria, or fungus. For example, up until
2014, 49% of small-molecule cancer drugs were
natural products or their derivatives (5).
In the future, disruptive advances in the dis-
covery of matter could instead come from unex-
plored regions of the set of all possible molecular
and solid-state compounds, known as chemical
space (6, 7). One of the largest collections of
molecules, the chemical space project (8), has
mapped 166.4 billion molecules that contain at
most 17 heavy atoms. For pharmacologically rele-
vant small molecules, the number of structures is
estimated to be on the order of 1060
(9). Adding
consideration of the hierarchy of scale from sub-
nanometer to microscopic and mesoscopic fur-
ther complicates exploration of chemical space
in its entirety (10). Therefore, any global strategy
for covering this space might seem impossible.
Simulation offers one way of probing this
space without experimentation. The physics
and chemistry of these molecules are governed
by quantum mechanics, which can be solved via
the Schrödinger equation to arrive at their ex-
act properties. In practice, approximations are
used to lower computational time at the cost of
accuracy.
Although theory enjoys enormous progress,
now routinely modeling molecules, clusters, and
perfect as well as defect-laden periodic solids, the
size of chemical space is still overwhelming, and
smart navigation is required. For this purpose,
machine learning (ML), deep learning (DL), and
artificial intelligence (AI) have a potential role
to play because their computational strategies
automatically improve through experience (11).
In the context of materials, ML techniques are
often used for property prediction, seeking to
learn a function that maps a molecular material
to the property of choice. Deep generative models
are a special class of DL methods that seek to
model the underlying probability distribution of
both structure and property and relate them in a
nonlinear way. By exploiting patterns in massive
datasets, these models can distill average and
salient features that characterize molecules (12, 13).
Inverse design is a component of a more
complex materials discovery process. The time
scale for deployment of new technologies, from
discovery in a laboratory to a commercial pro-
duct, historically, is 15 to 20 years (14). The pro-
cess (Fig. 1) conventionally involves the following
steps: (i) generate a new or improved material
concept and simulate its potential suitability; (ii)
synthesize the material; (iii) incorporate the ma-
terial into a device or system; and (iv) characterize
and measure the desired properties. This cycle
generates feedback to repeat, improve, and re-
fine future cycles of discovery. Each step can take
up to several years.
In the era of matter engineering, scientists
seek to accelerate these cycles, reducing the
FRONTIERS IN COMPUTATION
1
Department of Chemistry and Chemical Biology, Harvard
University 12 Oxford Street, Cambridge, MA 02138, USA.
2
Department of Chemistry and Department of Computer
Science, University of Toronto, Toronto Ontario, M5S 3H6,
Canada. 3
Vector Institute for Artificial Intelligence, Toronto,
Ontario M5S 1M1, Canada. 4
Canadian Institute for Advanced
Fig. 1. Schematic comparison of material discovery paradigms. The current paradigm is
APTEDBYK.HOLOSKI
onJuly26,2018http://science.sciencemag.org/Downloadedfrom
REVIEW https://doi.org/10.1038/s41586-018-0337-2
Machine learning for molecular and
materials science
Keith T. Butler1
, Daniel W. Davies2
, Hugh Cartwright3
, Olexandr Isayev4
* & Aron Walsh5,6
*
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning
techniques that are suitable for addressing research questions in this domain, as well as future directions for the field.
We envisage a future in which the design, synthesis, characterization and application of molecules and materials is
accelerated by artificial intelligence.
T
he Schrödinger equation provides a powerful structure–
property relationship for molecules and materials. For a given
spatial arrangement of chemical elements, the distribution of
electrons and a wide range of physical responses can be described. The
development of quantum mechanics provided a rigorous theoretical
foundationforthechemicalbond.In1929,PaulDiracfamouslyproclaimed
that the underlying physical laws for the whole of chemistry are “completely
known”1
. John Pople, realizing the importance of rapidly developing
computer technologies, created a program—Gaussian 70—that could
perform ab initio calculations: predicting the behaviour, for molecules
of modest size, purely from the fundamental laws of physics2
. In the 1960s,
the Quantum Chemistry Program Exchange brought quantum chemistry
to the masses in the form of useful practical tools3
. Suddenly, experi-
mentalists with little or no theoretical training could perform quantum
calculations too. Using modern algorithms and supercomputers,
systems containing thousands of interacting ions and electrons can now
be described using approximations to the physical laws that govern the
world on the atomic scale4–6
.
The field of computational chemistry has become increasingly pre-
dictive in the twenty-first century, with activity in applications as wide
ranging as catalyst development for greenhouse gas conversion, materials
discovery for energy harvesting and storage, and computer-assisted drug
design7
. The modern chemical-simulation toolkit allows the properties
of a compound to be anticipated (with reasonable accuracy) before it has
been made in the laboratory. High-throughput computational screening
has become routine, giving scientists the ability to calculate the properties
of thousands of compounds as part of a single study. In particular, den-
sity functional theory (DFT)8,9
, now a mature technique for calculating
the structure and behaviour of solids10
, has enabled the development of
extensive databases that cover the calculated properties of known and
hypothetical systems, including organic and inorganic crystals, single
molecules and metal alloys11–13
.
The emergence of contemporary artificial-intelligence methods has
the potential to substantially alter and enhance the role of computers in
science and engineering. The combination of big data and artificial intel-
ligence has been referred to as both the “fourth paradigm of science”14
and the “fourth industrial revolution”15
, and the number of applications
in the chemical domain is growing at an astounding rate. A subfield of
artificial intelligence that has evolved rapidly in recent years is machine
learning. At the heart of machine-learning applications lie statistical algo-
rithms whose performance, much like that of a researcher, improves with
training. There is a growing infrastructure of machine-learning tools for
generating, testing and refining scientific models. Such techniques are
suitable for addressing complex problems that involve massive combi-
natorial spaces or nonlinear processes, which conventional procedures
either cannot solve or can tackle only at great computational cost.
As the machinery for artificial intelligence and machine learning
matures, important advances are being made not only by those in main-
stream artificial-intelligence research, but also by experts in other fields
(domain experts) who adopt these approaches for their own purposes. As
we detail in Box 1, the resources and tools that facilitate the application
of machine-learning techniques mean that the barrier to entry is lower
than ever.
In the rest of this Review, we discuss progress in the application of
machine learning to address challenges in molecular and materials
research. We review the basics of machine-learning approaches, iden-
tify areas in which existing methods have the potential to accelerate
research and consider the developments that are required to enable more
wide-ranging impacts.
Nuts and bolts of machine learning
With machine learning, given enough data and a rule-discovery algo-
rithm, a computer has the ability to determine all known physical laws
(and potentially those that are currently unknown) without human
input. In traditional computational approaches, the computer is little
more than a calculator, employing a hard-coded algorithm provided
by a human expert. By contrast, machine-learning approaches learn
the rules that underlie a dataset by assessing a portion of that data
and building a model to make predictions. We consider the basic steps
involved in the construction of a model, as illustrated in Fig. 1; this
constitutes a blueprint of the generic workflow that is required for the
successful application of machine learning in a materials-discovery
process.
Data collection
Machine learning comprises models that learn from existing (train-
ing) data. Data may require initial preprocessing, during which miss-
ing or spurious elements are identified and handled. For example, the
Inorganic Crystal Structure Database (ICSD) currently contains more
than 190,000 entries, which have been checked for technical mistakes
but are still subject to human and measurement errors. Identifying
and removing such errors is essential to avoid machine-learning
algorithms being misled. There is a growing public concern about
the lack of reproducibility and error propagation of experimental data
DNA to be sequences into distinct pieces,
parcel out the detailed work of sequencing,
and then reassemble these independent ef-
forts at the end. It is not quite so simple in the
world of genome semantics.
Despite the differences between genome se-
quencing and genetic network discovery, there
are clear parallels that are illustrated in Table 1.
In genome sequencing, a physical map is useful
to provide scaffolding for assembling the fin-
ished sequence. In the case of a genetic regula-
tory network, a graphical model can play the
same role. A graphical model can represent a
high-level view of interconnectivity and help
isolate modules that can be studied indepen-
dently. Like contigs in a genomic sequencing
project, low-level functional models can ex-
plore the detailed behavior of a module of genes
in a manner that is consistent with the higher
level graphical model of the system. With stan-
dardized nomenclature and compatible model-
ing techniques, independent functional models
can be assembled into a complete model of the
cell under study.
To enable this process, there will need to
be standardized forms for model representa-
tion. At present, there are many different
modeling technologies in use, and although
models can be easily placed into a database,
they are not useful out of the context of their
specific modeling package. The need for a
standardized way of communicating compu-
tational descriptions of biological systems ex-
tends to the literature. Entire conferences
have been established to explore ways of
mining the biology literature to extract se-
mantic information in computational form.
Going forward, as a community we need
to come to consensus on how to represent
what we know about biology in computa-
tional form as well as in words. The key to
postgenomic biology will be the computa-
tional assembly of our collective knowl-
edge into a cohesive picture of cellular and
organism function. With such a comprehen-
sive model, we will be able to explore new
types of conservation between organisms
and make great strides toward new thera-
peutics that function on well-characterized
pathways.
References
1. S. K. Kim et al., Science 293 , 2087 (2001).
2. A. Hartemink et al., paper presented at the Pacific
Symposium on Biocomputing 2000, Oahu, Hawaii, 4
to 9 January 2000.
3. D. Pe’er et al., paper presented at the 9th Conference
on Intelligent Systems in Molecular Biology (ISMB),
Copenhagen, Denmark, 21 to 25 July 2001.
4. H. McAdams, A. Arkin, Proc. Natl. Acad. Sci. U.S.A.
94 , 814 ( 1997 ).
5. A. J. Hartemink, thesis, Massachusetts Institute of
Technology, Cambridge (2001).
V I E W P O I N T
Machine Learning for Science: State of the
Art and Future Prospects
Eric Mjolsness* and Dennis DeCoste
Recent advances in machine learning methods, along with successful
applications across a wide variety of fields such as planetary science and
bioinformatics, promise powerful new tools for practicing scientists. This
viewpoint highlights some useful characteristics of modern machine learn-
ing methods and their relevance to scientific applications. We conclude
with some speculations on near-term progress and promising directions.
Machine learning (ML) (1) is the study of
computer algorithms capable of learning to im-
prove their performance of a task on the basis of
their own previous experience. The field is
closely related to pattern recognition and statis-
tical inference. As an engineering field, ML has
become steadily more mathematical and more
successful in applications over the past 20
years. Learning approaches such as data clus-
tering, neural network classifiers, and nonlinear
regression have found surprisingly wide appli-
cation in the practice of engineering, business,
and science. A generalized version of the stan-
dard Hidden Markov Models of ML practice
have been used for ab initio prediction of gene
structures in genomic DNA (2). The predictions
correlate surprisingly well with subsequent
gene expression analysis (3). Postgenomic bi-
ology prominently features large-scale gene ex-
pression data analyzed by clustering methods
(4), a standard topic in unsupervised learning.
Many other examples can be given of learning
and pattern recognition applications in science.
Where will this trend lead? We believe it will
lead to appropriate, partial automation of every
element of scientific method, from hypothesis
generation to model construction to decisive
experimentation. Thus, ML has the potential to
amplify every aspect of a working scientist’s
progress to understanding. It will also, for better
or worse, endow intelligent computer systems
with some of the general analytic power of
scientific thinking.
Machine Learning at Every Stage of
the Scientific Process
Each scientific field has its own version of the
scientific process. But the cycle of observing,
creating hypotheses, testing by decisive exper-
iment or observation, and iteratively building
up comprehensive testable models or theories is
shared across disciplines. For each stage of this
abstracted scientific process, there are relevant
developments in ML, statistical inference, and
pattern recognition that will lead to semiauto-
matic support tools of unknown but potentially
broad applicability.
Increasingly, the early elements of scientific
method—observation and hypothesis genera-
tion—face high data volumes, high data acqui-
sition rates, or requirements for objective anal-
ysis that cannot be handled by human percep-
tion alone. This has been the situation in exper-
imental particle physics for decades. There
automatic pattern recognition for significant
events is well developed, including Hough
transforms, which are foundational in pattern
recognition. A recent example is event analysis
for Cherenkov detectors (8) used in neutrino
oscillation experiments. Microscope imagery in
cell biology, pathology, petrology, and other
fields has led to image-processing specialties.
So has remote sensing from Earth-observing
satellites, such as the newly operational Terra
spacecraft with its ASTER (a multispectral
thermal radiometer), MISR (multiangle imag-
ing spectral radiometer), MODIS (imaging
Machine Learning Systems Group, Jet Propulsion Lab-
oratory/California Institute of Technology, Pasadena,
CA, 91109, USA.
*To whom correspondence should be addressed. E-
mail: mjolsness@jpl.nasa.gov
Table 1. Parallels between genome sequencing
and genetic network discovery.
Genome
sequencing
Genome semantics
Physical maps Graphical model
Contigs Low-level functional
models
Contig
reassembly
Module assembly
Finished genome
sequence
Comprehensive model
www.sciencemag.org SCIENCE VOL 293 14 SEPTEMBER 2001 2051
C O M P U T E R S A N D S C I E N C E
onAugust29,2018http://science.sciencemag.org/Downloadedfrom
Nature, 559
pp. 547–555 (2018)
Science, 293
pp. 2051-2055 (2001)
Science, 361
pp. 360-365 (2018)
Science is changing, the tools of science are changing. And that
requires different approaches. Erich Bloch, 1925-2016
( )
"low input, high throughput, no output science." (Sydney Brenner)
60. ( )
!60
( )
速 ( ...)
In-House + Public +
+ Quality Control / Annotations)
+
61. ( )
!61
(surrogate)
x!y<latexit sha1_base64="kaK7x7wFN3GZpVpMGGOJaQQWYoA=">AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5</latexit><latexit sha1_base64="kaK7x7wFN3GZpVpMGGOJaQQWYoA=">AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5</latexit><latexit sha1_base64="kaK7x7wFN3GZpVpMGGOJaQQWYoA=">AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5</latexit><latexit sha1_base64="kaK7x7wFN3GZpVpMGGOJaQQWYoA=">AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5</latexit>
1. Initial Sampling
2. Loop:
1. Construct a Surrogate Model.
2. Search In ll Criterion.
3. Add new samples.
e.g.
Latin hypercube sampling (LHS)
e.g.
Expected improvement (EI)
☺Recent advances in surrogate-based optimization (Forrester & Keane, 2009)
https://doi.org/10.1016/j.paerosci.2008.11.001
62. In ll
!62
x<latexit sha1_base64="BLB8K/n7QYAsE73zsDEUiBvCSV8=">AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc=</latexit><latexit sha1_base64="BLB8K/n7QYAsE73zsDEUiBvCSV8=">AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc=</latexit><latexit sha1_base64="BLB8K/n7QYAsE73zsDEUiBvCSV8=">AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc=</latexit><latexit sha1_base64="BLB8K/n7QYAsE73zsDEUiBvCSV8=">AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc=</latexit>
ML <latexit sha1_base64="0VEGB1BS2t8KmbZWf3FuR1QlwM8=">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</latexit>
) ,
e.g.
"expected improvement"
Hot Topics
(CFR )
"exploitation" "exploration" ML
( )
+
68. !68
(1892)
"Statistics is the grammar of science." (Karl Pearson)
速 100%YES/NO 速
YES and NO
10
速 ( )
69. Impossible to model everything...?
!69
( )
/ " " ?
"one of the great statistical minds of the 20th century"
George E. P. Box (1919-2013)
https://en.wikipedia.org/wiki/All_models_are_wrong
"Essentially, all models are wrong,
but some are useful"
72. !72
Box 1966 Use and Abuse of regression
To find out what happens to a system when you
interfere with it you have to interfere with it
(not just passively observe it).
(?)