Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES) DOI Creative Commons
Fuzhan Rahmanian, Stefan Fuchs, Maximilian Fichtner

et al.

Published: Jan. 8, 2024

Discoveries of novel electrolyte-electrode combinations require comprehensive structure-property-interface correlations. Herein, we present an autonomous millimeter scale high-throughput battery research system (MISCHBARES) operated by hierarchical laboratory automation and orchestration (HELAO) which integrates modular instrumentation AI control. This paper will cathode electrolyte interphase (CEI) formation in lithium-ion batteries at various potentials correlating electrochemistry spectroscopy. We believe quality control complex data analysis to be the missing puzzle piece towards more workflow automation. Auto-MISCHBARES automatic for both hardware software ensure high reliability through on-the-fly fidelity assessment each individual experiment. Data is achieved our Modular Autonomous Analysis Platform (MADAP) presented platform, capable performing a fully automated voltammetry measurements real-time. Integration MISCHBARES MADAP HELAO enables versatile active learning workflows discovery new materials. demonstrate this integrated reliable charging/discharging protocols.

Language: Английский

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine DOI Creative Commons
Dolores R. Serrano,

Francis C. Luciano,

Brayan J. Anaya

et al.

Pharmaceutics, Journal Year: 2024, Volume and Issue: 16(10), P. 1328 - 1328

Published: Oct. 14, 2024

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep and other advanced computational methods. These innovations unlocked unprecedented opportunities the acceleration drug discovery delivery, optimization treatment regimens, improvement patient outcomes. AI is swiftly transforming industry, revolutionizing everything from development to personalized medicine, target identification validation, selection excipients, prediction synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While integration promises enhance efficiency, reduce costs, improve both medicines health, it also raises important questions regulatory point view. In this review article, we will present comprehensive overview AI's applications in covering areas such as discovery, safety, more. By analyzing current research trends case studies, aim shed light on transformative impact industry its broader implications healthcare.

Language: Английский

Citations

42
Theoretical Design Strategies for Area-Selective Atomic Layer Deposition DOI Creative Commons
Miso Kim, Jiwon Kim,

Sujin Kwon

et al.

Chemistry of Materials, Journal Year: 2024, Volume and Issue: 36(11), P. 5313 - 5324

Published: May 22, 2024

Area-selective atomic layer deposition (AS-ALD) is a bottom-up fabrication technique that may revolutionize the semiconductor manufacturing process. Because efficiency and applicability of AS-ALD strongly depend on properties molecular precursors for deposition, structural design optimization are needed. With aid various modern computational chemistry tools, tailor-made ALD high selectivity become possible. In this Perspective, requirements challenges precursors, as well theoretical strategies them, discussed. Current approaches analysis processes materials reviewed. A possible simulation strategy aspects suggested.

Language: Английский

Citations

13
Large Language Models for Inorganic Synthesis Predictions DOI
Seong-Min Kim, Yousung Jung, Joshua Schrier

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(29), P. 19654 - 19659

Published: July 11, 2024

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting synthesizability inorganic compounds selection precursors needed to perform synthesis. The predictions LLMs are comparable to─and sometimes better than─recent bespoke machine learning these tasks but require only minimal user expertise, cost, time develop. Therefore, this strategy can serve both as an effective strong baseline future studies various chemical applications a practical tool experimental chemists.

Language: Английский

Citations

8
Comment on “Comparing the Performance of College Chemistry Students with ChatGPT for Calculations Involving Acids and Bases” DOI Open Access
Joshua Schrier

Journal of Chemical Education, Journal Year: 2024, Volume and Issue: 101(5), P. 1782 - 1784

Published: April 11, 2024

In a recent paper in this Journal ( J. Chem. Educ. 2023, 100, 3934−3944), Clark et al. evaluated the performance of GPT-3.5 large language model (LLM) on ten undergraduate pH calculation problems. They reported that gave especially poor results for salt and titration problems, returning correct only 10% 0% time, respectively, that, despite application heuristics, LLM made mathematical errors used flawed strategies. However, these problems are partially mitigated using more advanced GPT-4 entirely corrected simple prompting calculator tool use patterns demonstrated herein.

Language: Английский

Citations

8
Materials Acceleration Platforms (MAPs) Accelerating Materials Research and Development to Meet Urgent Societal Challenges DOI Creative Commons
Simon Stier,

Christoph Kreisbeck,

H. Ihssen

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(45)

Published: Sept. 6, 2024

Abstract Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These drive policy to achieve sustainable, resilient, clean solutions with Advanced (AdMats) for industrial supply chains economic prosperity. The research landscape comprising industry, academe, government identified a critical path accelerate the Green Transition far beyond slow conventional through Digital Technologies that harness Artificial Intelligence, Smart Automation High Performance Computing Acceleration Platforms, MAPs. In this perspective, following short paper, broad overview about addressed, existing projects building blocks of MAPs will be provided while concluding review remaining gaps measures overcome them.

Language: Английский

Citations

8
Illustrating an Effective Workflow for Accelerated Materials Discovery DOI
Mrinalini Mulukutla,

A. Nicole Person,

Sven Voigt

et al.

Integrating materials and manufacturing innovation, Journal Year: 2024, Volume and Issue: 13(2), P. 453 - 473

Published: June 1, 2024

Language: Английский

Citations

7
Extrapolation validation (EV): a universal validation method for mitigating machine learning extrapolation risk DOI Creative Commons
Mengxian Yu, Yin‐Ning Zhou, Qiang Wang

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(5), P. 1058 - 1067

Published: Jan. 1, 2024

A generic machine learning model validation method named extrapolation (EV) has been proposed, which evaluates the trustworthiness of predictions to mitigate risk before transitions applications.

Language: Английский

Citations

6
Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence DOI
Min A Kim, Qianxiang Ai, Alexander J. Norquist

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(22), P. 14514 - 14522

Published: May 22, 2024

Ligands play a critical role in the optical properties and chemical stability of colloidal nanocrystals (NCs), but identifying ligands that can enhance NC is daunting, given high dimensionality space. Here, we use machine learning (ML) robotic screening to accelerate discovery photoluminescence quantum yield (PLQY) CsPbBr3 perovskite NCs. We developed ML model designed predict relative PL enhancement NCs when coordinated with ligand selected from pool 29,904 candidate molecules. Ligand candidates were using an active (AL) approach accounted for uncertainty quantified by twin regressors. After eight experimental iterations batch AL (corresponding 21 initial 72 model-recommended ligands), decreased, demonstrating increased confidence predictions. Feature importance counterfactual analyses predictions illustrate potential field strength designing PL-enhancing ligands. Our versatile framework be readily adapted screen effect on wide range nanomaterials.

Language: Английский

Citations

6
Top 20 influential AI-based technologies in chemistry DOI Creative Commons
Valentine P. Ananikov

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(2), P. 100075 - 100075

Published: July 27, 2024

The beginning and ripening of digital chemistry is analyzed focusing on the role artificial intelligence (AI) in an expected leap chemical sciences to bring this area next evolutionary level. analytic description selects highlights top 20 AI-based technologies 7 broader themes that are reshaping field. It underscores integration tools such as machine learning, big data, twins, Internet Things (IoT), robotic platforms, smart control processes, virtual reality blockchain, among many others, enhancing research methods, educational approaches, industrial practices chemistry. significance study lies its focused overview how these innovations foster a more efficient, sustainable, innovative future sciences. This article not only illustrates transformative impact but also draws new pathways chemistry, offering broad appeal researchers, educators, industry professionals embrace advancements for addressing contemporary challenges

Language: Английский

Citations

6
Identify structures underlying out-of-equilibrium reaction networks with random graph analysis DOI Creative Commons

Éverton F. da Cunha,

Yanna J. Kraakman, Dmitrii V. Kriukov

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Network measures have proven very successful in identifying structural patterns complex systems (e.g., a living cell, neural network, the Internet). How such can be applied to understand rational and experimental design of chemical reaction networks (CRNs) is unknown. Here, we develop procedure model CRNs as mathematical graph on which network random analysis applied. We used an enzymatic CRN (for mass-action was previously developed) show that provides insights into its structure properties. Temporal analyses, particular, revealed when feedback interactions emerge indicating comprise various reactions are being added removed over time. envision procedure, including temporal method, could broadly chemistry characterize properties many other CRNs, promising data-driven future molecular ever greater complexity.

Language: Английский

Citations

0