Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

Abstract

<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>

Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES

“…On established benchmarks, our algorithm achieves non-trivial results despite not using any sophisticated optimization engines and is comparable in its capabilities to the state of the art in generative modeling. The ease of obtaining molecules for local optimization and interpolation via chemical paths allows for our methods to be used in high-throughput virtual screening for materials science, 25 catalysis, 26 and drug design. 27 Ultimately, we anticipate that our results will stimulate more powerful models, more meaningful benchmarks, and more widespread use of generative models in general.…”

Transforming Predictions into Testable Hypotheses: The Case of Polar Organic Reactivity

“…The diversity of benchmarkable reactions can be increased remarkably in this way, which increases the significance of conclusions drawn from theoretical studies and is particularly important in the context of data-driven chemical design [40,41,42]. Currently, we are benchmarking first-principles models of reactivity against results of this study.…”