1) The document discusses challenges in using machine learning and data analytics for materials science research. Specifically, most materials are irrelevant for a given purpose, so models need to identify statistically exceptional subgroups rather than averaging all data.
2) Two potential methods for identifying promising subgroups are discussed: focusing on materials with small oxygen-carbon-oxygen angles or large carbon-oxygen bond lengths for catalysis applications.
3) The concept of a model's domain of applicability is introduced, wherein models perform best when applied only to similar data they were trained on, rather than all data globally. Identifying these reliable domains is important.
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
The interplay between data-driven and theory-driven methods for chemical scie...Ichigaku Takigawa
The 1st International Symposium on Human InformatiX
X-Dimensional Human Informatics and Biology
ATR, Kyoto, February 27-28, 2020
https://human-informatix.atr.jp
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
The interplay between data-driven and theory-driven methods for chemical scie...Ichigaku Takigawa
The 1st International Symposium on Human InformatiX
X-Dimensional Human Informatics and Biology
ATR, Kyoto, February 27-28, 2020
https://human-informatix.atr.jp
Invited talk at workshop "Exascale Computing in Astrophysics" held in Ascona, Switzerland, 8-13 September 2013.
http://www.itp.uzh.ch/exastro2013/Home.html
New learning technologies seem likely to transform much of science, as they are already doing for many areas of industry and society. We can expect these technologies to be used, for example, to obtain new insights from massive scientific data and to automate research processes. However, success in such endeavors will require new learning systems: scientific computing platforms, methods, and software that enable the large-scale application of learning technologies. These systems will need to enable learning from extremely large quantities of data; the management of large and complex data, models, and workflows; and the delivery of learning capabilities to many thousands of scientists. In this talk, I review these challenges and opportunities and describe systems that my colleagues and I are developing to enable the application of learning throughout the research process, from data acquisition to analysis.
The computational infrastructure is becoming a vast interconnected fabric of formal methods, including per a major shift from 2d grids to 3d graphs in machine learning architectures
The implication is systems-level digital science at unprecedented scale for discovery in a diverse range of scientific disciplines
Machine Learning in Materials Science and Chemistry, USPTO, Nathan C. FreyNathan Frey, PhD
Machine learning and artificial intelligence have transformed our online experience, and for an increasing number of individuals, these fields are fundamentally changing the way we work. In this talk, I will discuss how machine learning is used in the physical sciences, particularly materials science and chemistry, and what transformative impacts we have seen or might expect to see in the future. This discussion will focus on the unique challenges (and opportunities) faced by materials and chemistry researchers applying machine learning in their work. I will present a brief introduction to machine learning for physical scientists and give examples related to synthesis, property prediction and engineering, and artificial intelligence that “reads” research articles. These examples will introduce some of the most prevalent and useful open-source software tools that drive modern machine learning applications. Two significant themes will be emphasized throughout: the careful evaluation of machine learning results and the central importance of data quality and quantity. Finally, I will provide some mundane, “human learned” speculation about the future of machine learning in physical science and recommended resources for further study.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
1. 8/7/2019
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Max Planck SocietyMax Planck Society
When The New Science Is in The Outliers
When The New Science Is In The Outliers
Matthias Scheffler
Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany, and Physics Department and IRIS
Adlershof, Humboldt-Universität zu Berlin, 12489 Berlin, Germany
Several issues hamper progress in data-driven materials science. In particular, these are a missing FAIR [1]
data infrastructure and appropriate data-analytics methodology [2].
Significant efforts are still necessary to fully realize the A and I of FAIR. Here the development of metadata,
their intricate relationships, and data ontology need critical attention. Obviously, a FAIR data infrastructure
– for being accepted by the community – should work without bureaucratic hurdles or the needs for special
training. In this talk, I will discuss the challenges and progress, focusing on computational materials science.
Concerning the data-analytics, we note that the number of possible materials is practically infinite, but only
10 or 100 of them may be relevant for a certain science or engineering purpose. In simple words, in
materials science and engineering, we are often looking for “needles in a hay stack”. Fitting or machine-
learning all data (i.e. the hay) with a single, global model may average away the specialties of the
interesting minority (i.e. the needles). I will discuss methods that identify statistically-exceptional
subgroups in a large amount of data, and I will discuss how one can estimate the domains of applicability of
machine-learning models. [3]
1. FAIR stands for Findable, Accessible, Interoperable and Re-usable. The FAIR Data Principles;
https://www.force11.org/group/fairgroup/fairprinciples
2. C. Draxl and M. Scheffler, Big-Data-Driven Materials Science and its FAIR Data Infrastructure. Plenary Chapter in Handbook of Materials
Modeling (eds. S. Yip and W. Andreoni), Springer (2019). https://arxiv.org/ftp/arxiv/papers/1904/1904.05859.pdf
3. Ch. Sutton, M. Boley, L. M. Ghiringhelli, M. Rupp, J. Vreeken, M. Scheffler, Domains of Applicability of Machine-Learning Models for Novel
Materials Discovery, to be published.
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Max Planck SocietyMax Planck SocietyMax Planck Society
High-Throughput Screening
in Computational (and Experimental) Materials Science
Sharing
Advances Science
Needs for a FAIR,
Efficient Research-
Data Infrastructure
Animation by G.-M. RignaneseO(101) – O(102) compounds selected
Recycle the “waste”!
Enable re-purposing.
Consider as many compounds a possible, typically O(103) – O(105)
Max Planck SocietyMax Planck SocietyMax Planck Society
Findable Accessible Interoperable Reusable
M. D. Wilkinson et al., Scientific Data 3, 160018 (2016)
Since
2015
3. 8/7/2019
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Max Planck SocietyMax Planck SocietyMax Planck Society
Since
2014
Findable Accessible Interoperable Reusable
M. D. Wilkinson et al., Scientific Data 3, 160018 (2016)
Encyclopedia
Archive (normalized data)
Visualization
Repository
(raw data)
Big-Data
Analytics
Requests the full input and output files The NOMAD Center of Excellence
Since
2015
The NOMAD Repository
>50 Mio. Total-Energy Calculations
90% of the VASP
files are from
AFLOW
S. Curtarolo
OQMD
C. Wolverton
Materials Project
G. Ceder K. Persson
Max Planck Society
4. 8/7/2019
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Max Planck SocietyMax Planck SocietyMax Planck Society
What Is Needed for A
FAIR Data Infrastructure?
Scientific results are only meaningful and worth keeping if they are
fully characterized and all individual steps are fully documented.
Computed data are only meaningful when method, approximations,
code, code version, and all computational parameters are known.
For experimental data, we need a full characterization of the
sample, the description of the apparatus, the measurement
conditions, and the measured quantity.
This requires metadata, ontologies, and workflows.
We also need good search engines, an
“encyclopedia” GUI, and appropriate hardware.
Max Planck SocietyMax Planck Society
Any technique that
enables computers to
mimic human intelli-
gence, using logical if-
then rules, compressed
sensing, machine
learning (including
deep learning)
Artificial Intelligence (AI)
Machine Learning
The subset of machine lear-
ning composed of algorithms
that permit software to train
itself to perform tasks, like
speech and image recognition,
by exposing multilayered
neural networks to vast
amounts of data
A subset of AI
that includes
statistical techni-
ques that enable
machines to im-
prove at tasks
with more data.
It includes deep
learning
Deep Learning
Learning from “Big” Data:
Very Many Methods and Concepts,
Very Interdisciplinary
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Building Maps of Materials
(Role Models: Periodic Table, Ashby Plots)
Building Maps of Materials
(Role Models: Periodic Table, Ashby Plots)
-
Crystal-structure prediction
• Octet binaries (ZB vs. RS)
• AlxGayInzO3 (x+y+z=2)
• Perovskites (Goldschmidt
tolerance factor)
Property
classification:
• Topological
insulators
Activation
of CO2 at
metal
oxides and
carbides
Property
classification:
• Metal vs.
insulator
work in progress
Max Planck Society
Max Planck Society
One single model to describe the whole population
(known and unknown data)
• minimize the overall prediction error (e.g. RMSE)
using regularization
• therefore, disregard (on purpose) all local details
Global Learning
-- Machine Learning --
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Subgroups are statistically
exceptional.
Global vs. Local Learning
x=a
x=b
P
0.0 0.2 0.4 0.6 0.8 1.0
1.0
0.8
0.6
0.4
0.2
0.0
d
𝜎 𝑗 ≡ 𝑑 𝑗 ≥ 0.8 ∧ (𝑥 𝑗 = 𝑎)
Max Planck Society
A global model fitted to the entire
dataset may be difficult to interpret and
may well hide or incorrectly describe the
actuating physical mechanisms.
Given:
Sample S population
Target property Pj
Features (descriptors) dj
Formic acid
Formaldehyde
Methanol
Methane
Turning Greenhouse Gases into
Useful Chemicals and Fuels
Max Planck Society
CO
CO2
C
Aliaksei
Mazheika
Sergey
Levchenko
Francesc
Illas H.-J. Freund et al., Angew. Chem. Int. 50, 10064 (2011).We need an efficient catalyst!
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Identifying New Potential Catalysts
Considering Oxides
Oxides:
A2+B4+O3, AO, BO2,
A3+B3+O3, A2O3 (B2O3),
A1+B5+O3, A2O, BO
A2+: Mg, Ca, Sr, Ba
A3+(B3+): Al, Ga, In, Sc, Y, La
B4+: Ti, Zr, Si, Ge, Sn
A+: Li, Na, K, Rb, Cs;
B5+: Nb, V, Sb
Max Planck Society
Machine learning of all produced data
does not provide a good description.
Consider surfaces of many different
materials and all possibly relevant surface
sites: Which materials (and surface sites)
are catalytically active?
Two Possibly Interesting Subgroups
for Idenifying High-Performance Materials
Subgroup identification:
Define a ‘target property’
Minimize the width of the target-property distribution.
Maximize the distance between the median of the target-
property distribution and that of the whole data set.
Maximize the size of the subgroup.
For how many xxx compunds do we know high catalytic
activity? Whar is meant by high catalytic activity?
Max Planck Society
1) ‘Small O-C-O angle’ subgroup
2) ‘Large C-O bond length’ subgroup
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Statistically Exceptional Subgroups of Oxides
– Considering 51 Potential Descriptors –
VBM < − 5.14 eV
(wrt vacuum)
Min. of Hirschfeld
charges of the A and
B atoms qmin <
0.48 e−
Distance between
the O surface atom
and its second-
nearest neighbor
cation d2 > 2.26 Å
‘Small OCO angle’ subgroup
‘Large C-O bond length’ subgroup
Other materials
gas-phase CO2
δ− molecule (2 > δ > 0.9)
Max Planck Society
C-Obondlength,Å
(qmin < 0.48 e) AND (W ≥ 5.14) AND (d2 > 2.16 Å).
δ = 0
1.17 Å, 180°
Max. of O 2p DOS M
> −6.0 eV
Distance between O
surface atom and its
nearest neighbor
cation d1 > 1.8 Å
Distance between the
O surface atom and
its second-nearest
neighbor cation d2 >
2.12 Å
1.5
1.4
1.3
1.2
The descriptors should
characterize the clean surface
‘Small OCO angle’subgroup
‘Large C-O bond-length’subgroup
All materials and sites
Two Possibly Relevant Subgroups for
Semiconducting Oxide Materials
Most known materials
with good catalytic
performance belong
to the ‘large C-O bond
length’ subgroup.
From the “bad-
performance
materials”, none
belongs to the green
subgroup.
Max Planck Society
NumberofSystemsperEnergy
NOVEL MATERIALS DISCOVERY
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Domain of (reliable) Applicability (DoA)
of Machine-Learning Models
Max Planck Society
𝑒𝑖 = |𝑓 𝑥𝑖 − 𝑦 𝑥𝑖 |
• How reliable are machine-learning
models when fitted to all data?
• Are all data fitted equally well by the
one selected representation?
Individual absolute error
Find the subgroup with small individual errors.
Example: Data from NOMAD-Kaggle-2018
competition(*) on transparent, conducting oxides: AlxGayInz)2O3 (for 6 space
groups and up to 80 atoms/unit cell). Consider conjunctions on lattice-vector
lengths and angles, volume per atom, # atoms/unit cell, composition (%),
average nn distances (Al-Al, Al-Ga, Al-In, ... ), etc.
representation x
(*) C. Sutton, L.M. Ghiringhelli, et al., npj Comput. Materials, in print
simplified sketch
linear fit in
the DoA
Domain of
Applicability
linear fit
to all data
knowndatay(xi)andfitf(x)
Max Planck Society
ML model all data DoA selectors defining the DoA
(meV/cation) (meV/cation)
n-gram 15.2 11.41 𝑏 ≥ 5.59 Å 𝛾 < 90.35° 𝑅 Al−O ≤ 2.06Å 𝑅 Ga−O ≤ 2.07Å
SOAP 14.5 11.25 𝑎
𝑐 ≤ 3.89 𝛾 < 90.35° 𝛽 ≥ 88.68°
MBTR 13.9 8.03 𝑁 ≥ 50 𝛾 < 90.35° 𝑅Al-O ≤ 2.06 Å
Mean Absolute Error of the cohesive energy: 1
𝑁 𝑖=1
𝑁
|𝑓 𝑥𝑖 − 𝑦 𝑥𝑖 |
Example: (AlxGayInz)2O3
with Gaussian-kernel KRR and different representations(*)
(*) C. Sutton, M. Boley, L.M. Ghiringhelli, M. Rupp, J. Vreeken, M. Scheffler, to be published
Domain of (reliable) Applicability (DoA)
of Machine-Learning Models
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Max Planck Society
The Materials-Science Challenge Is Different
to That of Standard Machine Learning
RMSE =
Regularized RMSE optimization emphasizes the description of the majority.
It provides a “high chance of being right in the description of the hay”.
= predicted value
= true value
We are looking for statistically exceptional
data groups. This may be needles, or nuts,
or bolts, or coins, or … Often, we don’t know exactly what we are
searching for, except that the data should be statistically exceptional.
Identify these subgroups, and don’t “regularize away” the outliers!