14 papers · ranked by Valyu relevance
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Considering transformation products (TPs) in environmental studies remains a huge challenge for scientists, from identification in samples via mass spectrometry through to inclusion in chemical regulation. This article introduces FAIR-TPs, a website to browse openly-available TP data collated from literature sources.…
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Solubility is the maximum amount of solutes that can dissolve in a certain amount of solvent at a certain temperature, and it is significant in battery electrolyte research since it confines the design space. Thus, solubility measurement is a critical constraint on running self-driving labs for battery electrolyte…
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The analysis of molecular dynamics (MD) simulations is a critical but fragmented process, often requiring researchers to chain together multiple software tools and write bespoke scripts for routine structural and dynamic analyses. This workflow complexity creates a significant barrier to efficiency, standardization…
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With the rapid growth of chemical data and information, there is an increasing need for chemistry undergraduates to master Python tools for analyzing large chemical datasets and extracting key or feature information. Currently, more than 100,000 types of metal-organic frameworks (MOFs), as the material recently awarded…
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Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
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We present an open source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab-initio simulation package (VASP), called utils4VASP. It contains 20 independent Python scripts and Fortran programs, all with a unified and intuitive handling concept based on command…
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Process chemistry creates scalable routes for new lead molecules and is a crucial but laborious stage in pharmaceutical and agrochemical development cycles. We have built an automated process chemistry platform that tackles late-stage process development. The modular workflow integrates both industry-standard tools and…
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Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
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Computational modeling of enzymes provides molecular-level insight into catalysis, but the preparation of quantum mechanical (QM) calculations starting from experimental structures is a significant bottleneck for high-throughput studies. Automated tools developed to accelerate this process may fail to generalize across…
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DNA-encoded libraries (DELs) have emerged as a powerful platform for screening ultra-large chemical spaces by leveraging DNA barcodes to tag and track individual small molecules. Recent work has shown that machine learning can enhance DEL based hit discovery by denoising sequencing artifacts and improving binder…
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Mass spectrometry (MS) generates large datasets that are stored in increasingly optimized and complex file types, demanding technical expertise to extract information rapidly and easily. We wondered whether a simple structured query language (SQL) database could hold raw MS data and allow for easily readable queries…
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Data-driven approaches offer great potential for accelerating ab initio electronic structure calculations of molecules and materials but their transferability is often limited due to the vast amount of data needed for training, including when addressing the need to fine-tune universal models for each specific system to…
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This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…