In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(40), P. 21699 - 21716

Published: Sept. 27, 2023

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable fundamentally interesting, because they often involve new physical phenomena compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) automated experimentation have widely proposed to accelerate target identification synthesis planning. In this Perspective, we argue the data-driven methods commonly used today well-suited for optimization not realization of exceptional molecules. Finding such outliers should be possible using ML, only by shifting away from traditional ML approaches tweak composition, crystal structure, reaction pathway. We highlight case studies high-Tc oxide superconductors superhard demonstrate challenges ML-guided discovery discuss limitations automation task. then provide six recommendations development capable discovery: (i) Avoid tyranny middle focus on extrema; (ii) When data limited, qualitative predictions direction than interpolative accuracy; (iii) Sample what can made how make it defer optimization; (iv) Create room (and look) unexpected while pursuing your goal; (v) Try fill-in-the-blanks input output space; (vi) Do confuse human understanding model interpretability. conclude a description these integrated into workflows, which enable materials.

Language: Английский

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Self-Driving Laboratories for Chemistry and Materials Science

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Language: Английский

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Delocalized, asynchronous, closed-loop discovery of organic laser emitters

Science, Journal Year: 2024, Volume and Issue: 384(6697)

Published: May 16, 2024

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy enabled delocalized asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration molecular gain for organic solid-state lasers as frontier application in optoelectronics. Distributed robotic synthesis in-line property characterization, orchestrated by artificial intelligence experiment planner, resulted 21 new state-of-the-art materials. Gram-scale ultimately allowed verification best-in-class stimulated emission thin-film device. Demonstrating integration five laboratories across globe, workflow provides blueprint delocalizing-and democratizing-scientific discovery.

Language: Английский

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A dynamic knowledge graph approach to distributed self-driving laboratories

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 23, 2024

Abstract The ability to integrate resources and share knowledge across organisations empowers scientists expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require solutions. In this work, we develop an architecture for distributed self-driving laboratories within World Avatar project, which seeks create all-encompassing digital twin based on a dynamic graph. We employ ontologies capture data material flows design-make-test-analyse cycles, utilising autonomous agents as executable components carry out experimentation workflow. Data provenance recorded ensure its findability, accessibility, interoperability, reusability. demonstrate practical application of our framework by linking two robots Cambridge Singapore collaborative closed-loop optimisation pharmaceutically-relevant aldol condensation reaction real-time. graph autonomously evolves toward scientist’s research goals, with effectively generating Pareto front cost-yield three days.

Language: Английский

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An Ecosystem for Digital Reticular Chemistry

ACS Central Science, Journal Year: 2023, Volume and Issue: 9(4), P. 563 - 581

Published: March 10, 2023

The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise expediting and guiding design. Despite numerous successful applications materials, progress field stagnated, possibly because digital chemistry is more an art than a science its limited accessibility inexperienced researchers. To address this issue, we present mofdscribe, software ecosystem tailored novice seasoned chemists that streamlines ideation, modeling, publication process. Though optimized for chemistry, our tools are versatile can be used nonreticular research. We believe mofdscribe will enable reliable, efficient, comparable

Language: Английский

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Chemical Species Ontology for Data Integration and Knowledge Discovery

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(21), P. 6569 - 6586

Published: Oct. 26, 2023

Web ontologies are important tools in modern scientific research because they provide a standardized way to represent and manage web-scale amounts of complex data. In chemistry, semantic database for chemical species is indispensable its ability interrelate infer relationships, enabling more precise analysis prediction behavior. This paper presents OntoSpecies, web ontology designed their properties. The serves as core component World Avatar knowledge graph chemistry domain includes wide range identifiers, physical properties, classifications applications, spectral information associated with each species. provenance attribution metadata, ensuring the reliability traceability Most about sourced from PubChem ChEBI data on respective compound pages using software agent, making OntoSpecies comprehensive able solve novel types problems field. Access this reliable source provided through SPARQL end point. example use cases demonstrate contribution solving tasks that require integrated semantically searchable approach presented represents significant advancement field management, offering powerful tool representing, navigating, analyzing support research.

Language: Английский

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Semantic agent framework for automated flood assessment using dynamic knowledge graphs

Data-Centric Engineering, Journal Year: 2024, Volume and Issue: 5

Published: Jan. 1, 2024

Abstract This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts potential flooding events. It builds on idea connected digital twins based World Avatar dynamic create semantically rich asset data, knowledge, and computational capabilities accessible humans, applications, artificial intelligence. We develop three new ontologies describe link environmental measurements their respective reporting stations, flood events, impact population built infrastructure as well environment city itself. These coupled are deployed dynamically instantiate near real-time data from multiple fragmented sources into Avatar. Sequences autonomous via derived information consequences newly instantiated such raised warnings, cascade updates through ensure up-to-date insights number people building stock value at risk. Although we showcase strength this technology in context flooding, our findings suggest system-of-systems approach is promising solution build holistic for various other contexts use cases support truly interoperable smart cities.

Language: Английский

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Automated Rational Design of Metal–Organic Polyhedra

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(26), P. 11713 - 11728

Published: June 22, 2022

Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation MOP formulations based existing knowledge. For purpose have (i) curated relevant CBU data; (ii) developed an assembly model concept that embeds rules in construction; (iii) OntoMOPs ontology defines MOPs their key properties; (iv) input agents populate The World Avatar (TWA) graph; (v) that, using information from TWA, derive a list new constructible MOPs. Our result provides rapid automated instantiation TWA unveils immediate space known MOPs, thus shedding light targets for future investigations.

Language: Английский

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Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery

Advanced Intelligent Systems, Journal Year: 2022, Volume and Issue: 5(4)

Published: Dec. 23, 2022

The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking the conventional chemistry material science workflows. Self‐driving labs, emerged through integration disruptive physical digital technologies, including robotics, additive manufacturing, reaction miniaturization, artificial intelligence, have potential accelerate pace materials molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self‐driving labs enable access a larger part chemical universe reduce time‐to‐solution an iterative hypothesis formulation, intelligent experiment selection, automated testing. By providing data‐centric abstraction accelerated cycle, in this perspective article, required hardware software technological infrastructure unlock true is discussed. In particular, process intensification as accelerator mechanism for modules digitalization strategies further cycle sciences are

Language: Английский

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Orchestrating nimble experiments across interconnected labs

Digital Discovery, Journal Year: 2023, Volume and Issue: 2(6), P. 1806 - 1812

Published: Jan. 1, 2023

Human researchers multi-task, collaborate, and share resources. HELAO-async is a multi-workflow automation software that helps realize these attributes in materials acceleration platforms.

Language: Английский

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