Materials Acceleration Platforms: On the way to autonomous experimentation

Flores‐Leonar, Martha M.; Mejía-Mendoza, L.M.; Aguilar‐Granda, Andrés; Sánchez-Lengeling, Benjamín; Tribukait, Hermann; Amador‐Bedolla, Carlos; Aspuru‐Guzik, Alán

doi:10.1016/j.cogsc.2020.100370

Cited by 99 publications

(101 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-throughput screening, database generation, and subsequent ML model development are crucial components for realizing the full potential of reticular chemistry 56 and accelerating materials discovery in general. [57][58][59][60] In the present study, we leverage a recently developed high-throughput periodic DFT workflow tailored for MOF structures 61 to construct a large-scale database of MOF quantum mechanical properties. This publicly available dataset-the Quantum MOF (QMOF) database 62 -contains computed properties for 15,713 experimentally characterized MOFs after structure relaxation via DFT, including but not limited to optimized geometries, energies, band gaps, charge densities, density of states, partial charges, spin densities, and bond orders.…”

Section: Progress and Potentialmentioning

confidence: 99%

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen

Iyer

Ray

et al. 2021

Matter

Self Cite

219

259

View full text Add to dashboard Cite

Using a new database of electronic structure properties derived from highthroughput density functional theory calculations for thousands of metal-organic frameworks (MOFs), we benchmark a variety of machine learning models to accurately and rapidly predict MOF band gaps. Unsupervised dimensionality reduction techniques are also used to map the MOF feature space and identify otherwise subtle structure-property relationships. We anticipate that machine learning models derived from this new database will accelerate the discovery of promising MOFs with targeted quantum-chemical properties.

show abstract

Section: Progress and Potentialmentioning

confidence: 99%

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen

Iyer

Ray

et al. 2021

Matter

Self Cite

219

259

View full text Add to dashboard Cite

show abstract

“…Accelerating materials design ultimately requires close integration of computer simulation, ML and experimentation in self-driving platforms, which our group termed Materials Acceleration Platforms (MAPs). 43 …”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, ultimately, improvements in the experimental throughput are essential, calling for self-driving laboratories and closed-loop experimentation. 42 , 43 …”

Section: Methodsmentioning

confidence: 99%

Data-Driven Strategies for Accelerated Materials Design

et al. 2021

Self Cite

View full text Add to dashboard Cite

Conspectus The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

show abstract

“…78 These algorithmic developments need to be matched by technological advances, and full experimental workflow implementation will enable closed-loop optimization but is challenging to achieve. [79][80][81] Accordingly, autonomous closed-loop discovery is the ultimate dream for catalysis and science in general. 81 It is abundantly clear that the success of data-driven catalyst optimization relies on generating significant amounts of high-quality experimental data providing both structural and quantitative information about the reaction substrates and products.…”

Section: Robust Synthesis and Data-driven Experimentationmentioning

confidence: 99%

“…[79][80][81] Accordingly, autonomous closed-loop discovery is the ultimate dream for catalysis and science in general. 81 It is abundantly clear that the success of data-driven catalyst optimization relies on generating significant amounts of high-quality experimental data providing both structural and quantitative information about the reaction substrates and products. 82 Most notable in that regard are recent developments in mass spectrometry (MS) methods enabling analysis times below 1s per sample allowing to screen a large number of samples essentially in parallel through imaging techniques, providing both structural and semi-quantitative information.…”

Section: Robust Synthesis and Data-driven Experimentationmentioning

confidence: 99%

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

Gomes

Pollice

Aspuru‐Guzik

2020

Preprint

View full text Add to dashboard Cite

<div><div><div><p>The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.</p></div></div></div>

show abstract

Materials Acceleration Platforms: On the way to autonomous experimentation

Cited by 99 publications

References 49 publications

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Data-Driven Strategies for Accelerated Materials Design

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

Contact Info

Product

Resources

About