Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

565
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
565

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bridging Innovation and Efficiency: The Promises and Challenges of Self-Driving Labs as Sustainable Drivers for Chemistry.

Chimia·2025
Same author

Best practices for multi-fidelity Bayesian optimization in materials and molecular research.

Nature computational science·2025
Same author

Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes.

Chemical science·2024
Same author

Machine learning the frontier orbital energies of SubPc based triads.

Journal of molecular modeling·2022
Same author

ChemOS: Orchestrating autonomous experimentation.

Science robotics·2020
Same author

Designing and understanding light-harvesting devices with machine learning.

Nature communications·2020

Related Experiment Video

Updated: Jul 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science.

Daniel Pacheco Gutierrez1, Linnea M Folkmann1, Hermann Tribukait1

  • 1Atinary Technologies, Sàrl, Lausanne.

Chimia
|December 4, 2023
PubMed
Summary

Accelerating research and development (R&D) requires smart, data-driven strategies. Utilizing artificial intelligence (AI) and machine learning (ML) can significantly speed up the discovery of new materials and optimize experiment planning.

Keywords:
Artificial intelligenceAutonomous experimentationClosed-loop optimizationExperiment planningMachine learningMaterials acceleration platformsProcess optimizationSelf-driving labs

More Related Videos

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.4K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.5K

Related Experiment Videos

Last Updated: Jul 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.4K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.5K

Area of Science:

  • Materials Science
  • Research and Development Strategy
  • Innovation Management

Background:

  • The traditional Edisonian trial-and-error approach in R&D is slow, often taking up to two decades for new materials to reach the market.
  • This lengthy process hinders progress on critical global challenges, including sustainability goals.
  • There is a pressing need for strategies to upgrade R&D processes and accelerate innovation.

Purpose of the Study:

  • To present a framework for accelerating R&D processes.
  • To outline key technologies for optimizing experiment planning.
  • To enhance the efficiency of discovering new materials and solutions.

Main Methods:

  • Implementing data-driven experiment planning guided by AI/ML.
  • Leveraging digitized data management for enhanced data utility.
  • Utilizing statistical analysis and visualization tools alongside AI/ML.

Main Results:

  • AI/ML-guided experiment planning allows for more efficient navigation of complex experimental spaces.
  • Researchers can identify optimal experimental conditions faster than with traditional methods.
  • Digitized data management maximizes the short-term and long-term value of research data.

Conclusions:

  • A framework combining AI/ML, smart experiment planning, and digitized data management can significantly accelerate R&D.
  • These strategies enable faster discovery and optimization of materials and processes.
  • Upgrading R&D through these technologies is crucial for addressing global challenges efficiently.