Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis

Abstract

The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.

Colloidal Nanoparticles of High Entropy Materials: Capabilities, Challenges, and Opportunities in Synthesis and Characterization

“…Consider the solution-phase synthesis of colloidal noble metal nanoparticles. We now know that every aspect of a synthetic protocol matters in achieving a desired outcome: the metal reagents (including their oxidation states, counterions, and/or ligands), reducing agents, solvents, additives (ligands, polymers, and/or salts), how the reagents are added (one pot from the beginning, slowly injected, rapidly injected, sequentially), reaction temperatures, reaction times, heating rates, cooling rates, and/or other variables. , These considerations are for one metal. These same variables matter for a successful synthesis of bimetallic alloy nanoparticles as well, although additional considerations are necessary to ensure that the metals mix homogeneously rather than nucleate and grow independently as separate nonalloyed metal nanoparticles.…”

“…It is therefore important to acknowledge this complexity in reactions and reactivity, especially the reality of multiple and competing reactions and reaction pathways that can change as the reaction progresses. Because of this complexity, synthetic development is well-suited for Bayesian optimization, Design of Experiments, machine learning, and other automated strategies capable of handling multiple variables, as these methods can help to accelerate the identification of reaction parameters that lead to products with targeted features having the highest possible yields and purities. , …”

“…Because of this complexity, synthetic development is well-suited for Bayesian optimization, Design of Experiments, machine learning, and other automated strategies capable of handling multiple variables, as these methods can help to accelerate the identification of reaction parameters that lead to products with targeted features having the highest possible yields and purities. 63,64 ■ IS THE FORMATION OF COLLOIDAL HIGH…”

Revisiting El-Sayed Synthesis: Bayesian Optimization for Revealing New Insights during the Growth of Gold Nanorods

“…While these studies have provided valuable insights, − they often fail to capture the intricate synergies between different reaction conditions, thereby limiting the depth of understanding they impart . Although studies utilizing design of experiments (DoE) are essential, BO offers several advantages, including efficient optimizations and the ability to handle categorical variables, leveraging complex predictive models . This makes BO particularly attractive for material synthesis.…”

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

“…The advancement of nanomaterials can be expedited by leveraging data mining, an essential tool in scientific research, given the vast and complex data generated. This process is crucial for the progression of nanomaterials [ 281 , 282 , 283 , 284 , 285 , 286 ].…”