The compositional and evolutionary logic of metabolism

Metabolism displays striking and robust regularities in the forms of modularity and hierarchy, whose composition may be compactly described. This renders metabolic architecture comprehensible as a system, and suggests the order in which layers of that system emerged. Metabolism also serves as the foundation in other hierarchies, at least up to cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, suggests metabolism as a source of causation or constraint on many forms of organization in the biosphere. We identify as modules widely reused subsets of chemicals, reactions, or functions, each with a conserved internal structure. At the small molecule substrate level, module boundaries are generally associated with the most complex reaction mechanisms and the most conserved enzymes. Cofactors form a structurally and functionally distinctive control layer over the small-molecule substrate. Complex cofactors are often used at module boundaries of the substrate level, while simpler ones participate in widely used reactions. Cofactor functions thus act as "keys" that incorporate classes of organic reactions within biochemistry. The same modules that organize the compositional diversity of metabolism are argued to have governed long-term evolution. Early evolution of core metabolism, especially carbon-fixation, appears to have required few innovations among a small number of conserved modules, to produce adaptations to simple biogeochemical changes of environment. We demonstrate these features of metabolism at several levels of hierarchy, beginning with the small-molecule substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.Comment: 56 pages, 28 figure

More is Different: Modern Computational Modeling for Heterogeneous Catalysis

La combinació d'observacions experimentals i estudis de la Density Functional Theory (DFT) és un dels pilars de la investigació química moderna. Atès que permeten recopilar informació física addicional d'un sistema químic, difícilment accessible a través de l'entorn experimental, aquests estudis es fan servir àmpliament per modelar i predir el comportament d'una gran varietat de compostos químics en entorns únics. A la catàlisi heterogènia, els models DFT s'utilitzen habitualment per avaluar la interacció entre els compostos moleculars i els catalitzadors, vinculant aquestes interpretacions amb els resultats experimentals. Tanmateix, l'alta complexitat trobada tant als escenaris catalítics com a la reactivitat, implica la necessitat de metodologies sofisticades que requereixen automatització, emmagatzematge i anàlisi per estudiar correctament aquests sistemes. Aquest treball presenta el desenvolupament i la combinació de múltiples metodologies per avaluar correctament la complexitat d'aquests sistemes químics. A més, aquest treball mostra com s'han utilitzat les tècniques proporcionades per estudiar noves configuracions catalítiques d'interès acadèmic i industrial.La combinación de observaciones experimentales y estudios de la Density Functional Theory (DFT) es uno de los pilares de la investigación química moderna. Dado que permiten recopilar información física adicional de un sistema químico, difícilmente accesible a través del entorno experimental, estos estudios se emplean ampliamente para modelar y predecir el comportamiento de una gran variedad de compuestos químicos en entornos únicos. En la catálisis heterogénea, los modelos DFT se emplean habitualmente para evaluar la interacción entre los compuestos moleculares y los catalizadores, vinculando estas interpretaciones con los resultados experimentales. Sin embargo, la alta complejidad encontrada tanto en los escenarios catalíticos como en la reactividad, implica la necesidad de metodologías sofisticadas que requieren de automatización, almacenamiento y análisis para estudiar correctamente estos sistemas. Este trabajo presenta el desarrollo y la combinación de múltiples metodologías con el objetivo de evaluar correctamente la complejidad de estos sistemas químicos. Además, este trabajo muestra cómo las técnicas proporcionadas se han utilizado para estudiar nuevas configuraciones catalíticas de interés académico e industrial.The combination of Experimental observations and Density Functional Theory studies is one of the pillars of modern chemical research. As they enable the collection of additional physical information of a chemical system, hardly accessible via the experimental setting, Density Functional Theory studies are widely employed to model and predict the behavior of a diverse variety of chemical compounds under unique environments. Particularly, in heterogeneous catalysis, Density Functional Theory models are commonly employed to evaluate the interaction between molecular compounds and catalysts, lately linking these interpretations with experimental results. However, high complexity found in both, catalytic settings and reactivity, implies the need of sophisticated methodologies involving automation, storage and analysis to correctly study these systems. Here, I present the development and combination of multiple methodologies, aiming at correctly asses complexity. Also, this work shows how the provided techniques have been actively used to study novel catalytic settings of academic and industrial interest

Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design

Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design and manufacturing, including their capacity to work effectively with both human language, symbols, code, and numerical data. Here we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. When used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how finetuning endows LLMs with reasonable understanding of domain knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty to recall correct information. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies that discern how the model understands what concepts are important and how they are related. Illustrated for a use case of relating distinct areas of knowledge - here, music and proteins - such strategies can also provide an interpretable graph structure with rich information at the node, edge and subgraph level. We discuss nonlinear sampling strategies and agent-based modeling applied to complex question answering, code generation and execution in the context of automated force field development from actively learned Density Functional Theory (DFT) modeling, and data analysis

Automated Discovery of porous molecular materials facilitated by characterization of molecular porosity

Porous materials are critical to many industrial sectors, including petrochemicals, energy and water. Traditional porous polymers and zeolites are currently most widely employed within membranes, as adsorbents for separations and storage, and as heterogeneous catalysts. The emerging advanced porous materials, e.g. extended framework materials and molecular porous materials, can boost performance and energy-efficiency of the current technologies because of the unprecedented level of control of their structure and function. The enormous possibilities for tuning these materials by changing their building blocks mean that, in principle, optimally performing materials for a variety of applications can be systematically designed. However, the process of finding a set of optimal structures for a given application could take decades using the traditional materials development approaches. These is a substantial payoff for developing tools and approaches that can accelerate this process. Among advanced porous materials, porous molecular materials are one of the most recent members though they have already attracted significant interest......Programa de Doctorado en Ciencia e Ingeniería de Materiales por la Universidad Carlos III de MadridPresidente: Germán Ignacio Sastre Navarro.- Secretario: Javier Carrasco Rodríguez.- Vocal: Andreas Mavrantonaki

The random geometry of equilibrium phases

This is a (long) survey about applications of percolation theory in equilibrium statistical mechanics. The chapters are as follows: 1. Introduction 2. Equilibrium phases 3. Some models 4. Coupling and stochastic domination 5. Percolation 6. Random-cluster representations 7. Uniqueness and exponential mixing from non-percolation 8. Phase transition and percolation 9. Random interactions 10. Continuum modelsComment: 118 pages. Addresses: [email protected] http://www.mathematik.uni-muenchen.de/~georgii.html [email protected] http://www.math.chalmers.se/~olleh [email protected]