Digital discovery最新文献

{"title":"Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules†","authors":"Patrizia Mazzeo, Edoardo Cignoni, Amanda Arcidiacono, Lorenzo Cupellini and Benedetta Mennucci","doi":"10.1039/D4DD00295D","DOIUrl":"https://doi.org/10.1039/D4DD00295D","url":null,"abstract":"<p >The application of quantum mechanics (QM)/molecular mechanics (MM) models for studying dynamics in complex systems is nowadays well established. However, their significant limitation is the high computational cost, which restricts their use for larger systems and long-timescale processes. We propose a machine-learning (ML) based approach to study the dynamics of solvated molecules on the ground- and excited-state potential energy surfaces. Our ML model is trained on QM/MM calculations and is designed to predict energies and forces within an electrostatic embedding framework. We built a socket-based interface of our machinery with AMBER to run ML/MM molecular dynamics simulations. As an application, we investigated the excited-state intramolecular proton transfer of 3-hydroxyflavone in two different solvents: methanol and methylcyclohexane. Our ML/MM simulations accurately distinguished between the two solvents, effectively reproducing the solvent effects on proton transfer dynamics.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 12","pages":" 2560-2571"},"PeriodicalIF":6.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00295d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HH130: a standardized database of machine learning interatomic potentials, datasets, and its applications in the thermal transport of half-Heusler thermoelectrics†","authors":"Yuyan Yang, Yifei Lin, Shengnan Dai, Yifan Zhu, Jinyang Xi, Lili Xi, Xiaokun Gu, David J. Singh, Wenqing Zhang and Jiong Yang","doi":"10.1039/D4DD00240G","DOIUrl":"https://doi.org/10.1039/D4DD00240G","url":null,"abstract":"<p >High-throughput screening of thermoelectric materials from databases requires efficient and accurate computational methods. Machine-learning interatomic potentials (MLIPs) provide a promising avenue, facilitating the development of database-driven thermal transport applications through high-throughput simulations. However, the present challenge is the lack of standardized databases and openly available models for precise large-scale simulations. Here, we introduce HH130, a standardized database for 130 half-Heusler (HH) compounds in MatHub-3d (http://www.mathub3d.net), containing both MLIP models and datasets for the thermal transport of HH thermoelectrics. HH130 contains 31 891 total configurations (∼245 configurations per HH) and 390 MLIP models (three models per HH), generated using the dual adaptive sampling method to cover a wide range of thermodynamic conditions, and can be openly accessed on MatHub-3d. Comprehensive validation against first-principles calculations demonstrates that the MLIP models accurately predict energies, forces, and interatomic force constants (IFCs). The MLIP models in HH130 enabled us to efficiently perform four-phonon interactions for 80 HHs with phonon frequencies closely matching <em>ab initio</em> results. It is found that HHs with an 8 valence electron count (VEC) per unit cell generally exhibit lower lattice thermal conductivities (<em>κ</em><small>_L</small>s) compared to those with an 18 VEC, due to a combination of low 2nd-order IFCs and large scattering phase spaces in the former group. Additionally, we identified several HHs that demonstrate significant reductions in <em>κ</em><small>_L</small> due to four-phonon interactions. HH130 provides a robust platform for high-throughput computation of <em>κ</em><small>_L</small> and aids in the discovery of next-generation thermoelectrics through machine learning.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 11","pages":" 2201-2210"},"PeriodicalIF":6.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00240g?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing exploration and exploitation in de novo drug design","authors":"Maxime Langevin, Marc Bianciotto and Rodolphe Vuilleumier","doi":"10.1039/D4DD00105B","DOIUrl":"https://doi.org/10.1039/D4DD00105B","url":null,"abstract":"<p >Goal-directed molecular generation is the computational design of novel molecular structures optimised with respect to a given scoring function. While it holds great promise for the acceleration of drug design, there remain limitations that hamper its adoption in an industrial context. In particular, the lack of diversity of molecules generated currently limits their relevance for drug design. Yet, most algorithms proposed focus solely on optimizing the scoring function, and do not address the question of diversity of the solutions they propose. Here, we propose a conceptual framework for analyzing the need for diverse solutions in goal-directed generation. Using a mean-variance framework, we present a simple model to bridge the optimization objective of goal-directed generation with the need for diverse solutions. We also show how to integrate it within different goal-directed learning algorithms.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 12","pages":" 2572-2588"},"PeriodicalIF":6.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00105b?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Micro-kinetic modeling of temporal analysis of products data using kinetics-informed neural networks†","authors":"Dingqi Nai, Gabriel S. Gusmão, Zachary A. Kilwein, Fani Boukouvala and Andrew J. Medford","doi":"10.1039/D4DD00163J","DOIUrl":"https://doi.org/10.1039/D4DD00163J","url":null,"abstract":"<p >The temporal analysis of products (TAP) technique produces extensive transient kinetic data sets, but it is challenging to translate the large quantity of raw data into physically interpretable kinetic models, largely due to the computational scaling of existing numerical methods for fitting TAP data. In this work, we utilize kinetics-informed neural networks (KINNs), which are artificial feedforward neural networks designed to solve ordinary differential equations constrained by micro-kinetic models, to model the TAP data. We demonstrate that, under the assumption that all concentrations are known in the thin catalyst zone, KINNs can simultaneously fit the transient data, retrieve the kinetic model parameters, and interpolate unseen pulse behavior for multi-pulse experiments. We further demonstrate that, by modifying the loss function, KINNs maintain these capabilities even when precise thin-zone information is unavailable, as would be the case with real experimental TAP data. We also compare the approach to existing optimization techniques, which reveals improved noise tolerance and performance in extracting kinetic parameters. The KINNs approach offers an efficient alternative for TAP analysis and can assist in interpreting transient kinetics in complex systems over long timescales.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 11","pages":" 2327-2340"},"PeriodicalIF":6.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00163j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MolBar: a molecular identifier for inorganic and organic molecules with full support of stereoisomerism†","authors":"Nils van Staalduinen and Christoph Bannwarth","doi":"10.1039/D4DD00208C","DOIUrl":"https://doi.org/10.1039/D4DD00208C","url":null,"abstract":"<p >Before a new molecular structure is registered to a chemical structure database, a duplicate check is essential to ensure the integrity of the database. The Simplified Molecular Input Line Entry Specification (SMILES) and the IUPAC International Chemical Identifier (InChI) stand out as widely used molecular identifiers for these checks. Notable limitations arise when dealing with molecules from inorganic chemistry or structures characterized by non-central stereochemistry. When the stereoinformation needs to be assigned to a group of atoms, widely used identifiers cannot describe axial and planar chirality due to the atom-centered description of a molecule. To address this limitation, we introduce a novel chemical identifier called the Molecular Barcode (MolBar). Motivated by the field of theoretical chemistry, a fragment-based approach is used in addition to the conventional atomistic description. In this approach, the 3D structure of fragments is normalized using a specialized force field and characterized by physically inspired matrices derived solely from atomic positions. The resulting permutation-invariant representation is constructed from the eigenvalue spectra, providing comprehensive information on both bonding and stereochemistry. The robustness of MolBar is demonstrated through duplication and permutation invariance tests on the Molecule3D dataset of 3.9 million molecules. A Python implementation is available as open source and can be installed <em>via pip install molbar</em>.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 11","pages":" 2298-2319"},"PeriodicalIF":6.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00208c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polyuniverse: generation of a large-scale polymer library using rule-based polymerization reactions for polymer informatics†","authors":"Tianle Yue, Jianxin He and Ying Li","doi":"10.1039/D4DD00196F","DOIUrl":"https://doi.org/10.1039/D4DD00196F","url":null,"abstract":"<p >Recent advancements in machine learning have revolutionized polymer research, leading to the swift integration of diverse computational techniques for <em>de novo</em> molecular design. A crucial aspect of these processes is to expand the number of candidate polymer structures, as the currently known real polymer structures are very limited. In contrast, small molecule databases are vast, offering extensive opportunities for the design of new molecules, such as drug discovery. In this study, we collected extensive small molecule compounds from GDB-17, GDB-13, and PubChem and selected polymerization reaction pathways for eight types of polymers, including polyimide, polyolefin, polyester, polyamide, polyurethane, epoxy, polybenzimidazole (PBI), and vitrimer. These small molecule datasets and polymerization reactions enabled us to generate hundreds of quadrillions of hypothetical polymer structures. For each of the eight polymers, along with one promising copolymer, poly(imide-imine), we randomly generated over one million hypothetical structures, except for PBI, for which we created 10 000 structures. Chemical space visualization using t-distributed stochastic neighbor embedding and synthetic accessibility scores were employed to assess the feasibility of synthesizing these new polymers. Customized feedforward neural network models predicted thermal, mechanical, and gas permeation properties for both real and hypothetical polymers. The results show that many hypothetical polymers, especially polyimides, exhibit significant potential, often surpassing real polymers in performance, particularly for high-temperature applications and gas separation. Our findings highlight the immense potential of large-scale hypothetical polymer libraries for materials discovery and design. These libraries not only aid in identifying promising polymer materials through high-throughput screening but also provide valuable datasets for training advanced machine learning models, such as large language models. This research also demonstrates the power of data-driven approaches in polymer science, paving the way for the development of next-generation polymeric materials with superior properties for diverse industrial applications.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 12","pages":" 2465-2478"},"PeriodicalIF":6.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00196f?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer vision enabled high-quality electrochemical experimentation","authors":"Keiichi Okubo, Jaydeep Thik, Tomoya Yamaguchi and Chen Ling","doi":"10.1039/D4DD00213J","DOIUrl":"https://doi.org/10.1039/D4DD00213J","url":null,"abstract":"<p >The rotating disk electrode (RDE) technique is an essential tool for studying the activity, stability, and other fundamental properties of electrocatalysts. High-quality RDE experimentation requires evenly coating the catalyst layer on the electrode surface, which relies heavily on experience and currently lacks necessary quality control. The lack of an adequate evaluation method to ensure the quality of RDE experimentation, aside from conventional judgment based on expertise, reduces efficiency, complicates data interpretation, and hinders future automation of RDE experimentation. Here we propose a simple, easy-to-execute and non-destructive method that combines microscopy imaging and artificial intelligence-based decision-making to assess the quality of as-prepared electrodes. We develop a convolutional neural network-based method that uses microscopic images of as-prepared electrodes to directly evaluate the sample quality. In a study of electrodes used for the oxygen reduction reaction, the model achieved an accuracy of over 80% in predicting sample qualities. Our method enables the removal of low-quality samples prior to the actual RDE test, thereby ensuring high-quality electrochemical experimentation and paving the way towards high-quality automated electrochemical experimentation. This approach is applicable to various electrochemical systems and highlights the potential of artificial intelligence in automated experimentation.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 11","pages":" 2183-2191"},"PeriodicalIF":6.2,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00213j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging GPT-4 to transform chemistry from paper to practice†","authors":"Wenyu Zhang, Mason A. Guy, Jerrica Yang, Lucy Hao, Junliang Liu, Joel M. Hawkins, Jason Mustakis, Sebastien Monfette and Jason E. Hein","doi":"10.1039/D4DD00248B","DOIUrl":"https://doi.org/10.1039/D4DD00248B","url":null,"abstract":"<p >Large Language Models (LLMs) have revolutionized numerous industries as well as accelerated scientific research. However, their application in planning and conducting experimental science, has been limited. In this study, we introduce an adaptable prompt-set with GPT-4, converting literature experimental procedures into actionable experimental steps for a Mettler Toledo EasyMax automated laboratory reactor. Through prompt engineering, we developed a 2-step sequential prompt: the first prompt converts literature synthesis procedures into step-by-step instructions for reaction planning; the second prompt generates an XML script to communicate these instructions to the EasyMax reactor, automating experimental design and execution. We successfully automated the reproduction of three distinct literature-based synthetic procedures and validated the reactions by monitoring and characterizing the products. This approach bridges the gap between text-to-procedure transcription and automated execution, and streamlines literature procedure reproduction.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 11","pages":" 2367-2376"},"PeriodicalIF":6.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00248b?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories","authors":"Yuxing Fei, Bernardus Rendy, Rishi Kumar, Olympia Dartsi, Hrushikesh P. Sahasrabuddhe, Matthew J. McDermott, Zheren Wang, Nathan J. Szymanski, Lauren N. Walters, David Milsted, Yan Zeng, Anubhav Jain and Gerbrand Ceder","doi":"10.1039/D4DD00129J","DOIUrl":"https://doi.org/10.1039/D4DD00129J","url":null,"abstract":"<p >The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, the A-Lab, with around 3500 samples synthesized over 1.5 years.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 11","pages":" 2275-2288"},"PeriodicalIF":6.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00129j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven exploration of silver nanoplate formation in multidimensional chemical design spaces†","authors":"Huat Thart Chiang, Kiran Vaddi and Lilo Pozzo","doi":"10.1039/D4DD00211C","DOIUrl":"https://doi.org/10.1039/D4DD00211C","url":null,"abstract":"<p >We present an autonomous data-driven framework that iteratively explores the experimental design space of silver nanoparticle synthesis to obtain control over the formation of a desired morphology and size. The objective of the method is to identify design rules such as the effects of the design variables on the structure of the nanoparticle. The framework balances multimodal characterization methods (<em>i.e.</em> UV-vis spectroscopy, SAXS, TEM), taking into account the cost of performing a measurement and the quality of information gained. By integrating with an AI agent, we identify important design variables in the synthesis of small colloidally stable plate-like silver particles and outline how each variable affects plate thickness, radius, polydispersity, and relative concentration. Our findings are consistent with the literature, demonstrating that the framework could be further applied to new systems that have not been well characterized and understood. The framework is generalizable and allows tangible knowledge extraction from the high-throughput experimental runs while still considering inherent stochasticity.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 11","pages":" 2252-2264"},"PeriodicalIF":6.2,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d4dd00211c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}