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Heterogeneous Catalysis

Heterogeneous Catalysis

Heterogeneous catalysis involves a catalyst in a different phase from the reactants. It is a process where the catalyst and the reactants are in distinct phases, typically solid and gas or liquid.Most heterogeneous catalysts are metals, metal oxides, or acids. The list includes transition metals like iron (Fe), cobalt (Co), nickel (Ni), palladium (Pd), platinum (Pt), chromium (Cr), manganese (Mn), tungsten (W), silver (Ag), and copper (Cu). These metals possess partially vacant d orbitals that...

Introduction to Mechanisms of Enzyme Catalysis

Introduction to Mechanisms of Enzyme Catalysis

For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes a mild...

Introduction to Mechanisms of Enzyme Catalysis

Introduction to Mechanisms of Enzyme Catalysis

For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes a mild...

Catalysis

Catalysis

The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.

Catalysis

Catalysis

Catalysis influences the rate of chemical reactions by providing an alternative reaction pathway with lower activation energy. A catalyst speeds up a reaction, but it is not consumed during the process. The fundamental principle of catalysis is the ability of a catalyst to alter the reaction mechanism, often introducing a more efficient pathway than the uncatalyzed process.In a catalyzed reaction, the catalyst participates directly in the reaction mechanism. It interacts with reactants to form...

Catalytically Perfect Enzymes

Catalytically Perfect Enzymes

The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.

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Related Experiment Video

Updated: Jun 2, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Bayesian Optimization of Catalysis with In-Context Learning.

Mayk Caldas Ramos¹, Shane S Michtavy², Andrew D White^2,1

¹Edison Scientific Inc., San Francisco, California 94107, United States.

ACS Central Science

|June 1, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) now perform Bayesian optimization (BO) for materials discovery using in-context learning (ICL). This AI-driven approach accelerates the identification of novel materials without retraining models.

Related Experiment Videos

Last Updated: Jun 2, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Area of Science:

Artificial Intelligence
Materials Science
Chemistry

Background:

Large language models (LLMs) excel at classification via in-context learning (ICL), adapting to new tasks without weight updates.
Current materials discovery often involves extensive characterization of suboptimal materials, slowing innovation.

Purpose of the Study:

To extend in-context learning (ICL) capabilities of frozen LLMs to regression tasks with uncertainty estimation.
To enable Bayesian optimization (BO) for materials discovery using natural language prompts, bypassing traditional training and feature engineering.

Main Methods:

Representing materials as synthesis and testing procedures within natural language prompts for LLMs.
Applying Bayesian optimization with in-context learning (BO-ICL) for a design-first materials discovery approach.
Utilizing frozen LLMs (e.g., GPT-4o, Gemini) for regression and uncertainty estimation.

Main Results:

BO-ICL demonstrated performance matching or exceeding Gaussian processes on aqueous solubility and oxidative coupling of methane (OCM) benchmarks.
In reverse water-gas shift (RWGS) experiments, BO-ICL rapidly identified high-performing multimetallic catalysts from large candidate pools.
Achieved near-equilibrium CO yield within 6 and 10 iterations for datasets of 3,700 and 360,000 materials, respectively.

Conclusions:

The novel BO-ICL method redefines materials representation and significantly accelerates discovery processes.
This approach offers broad applicability in catalysis, materials science, and artificial intelligence, paving the way for faster innovation.