Current and emerging deep-learning methods for the simulation of fluid dynamics DOI Creative Commons
Mario Lino, Stathi Fotiadis, Anil A. Bharath

et al.

Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, Journal Year: 2023, Volume and Issue: 479(2275)

Published: July 1, 2023

Over the last decade, deep learning (DL), a branch of machine learning, has experienced rapid progress. Powerful tools for tasks that have been traditionally complex to automate developed, such as image synthesis and natural language processing. In context simulating fluid dynamics, this led series novel DL methods replacing or augmenting conventional numerical solvers. We broadly classify these into physics- data-driven methods. Physics-driven methods, generally, tune model provide an analytical differentiable solution given dynamics problem by minimizing residuals governing partial differential equations. Data-driven fast approximate any shares some physical properties with observations used when tuning model’s parameters. Meanwhile, symbiosis solvers promising results in turbulence modelling accelerating iterative However, present challenges. Exclusively flow simulators often suffer from poor extrapolation, error accumulation time-dependent simulations, well difficulties training against turbulent flows. Substantial effort is, therefore, being invested approaches may improve current state art.

Language: Английский

On the Opportunities and Risks of Foundation Models DOI Creative Commons
Rishi Bommasani,

Drew A. Hudson,

Ehsan Adeli

et al.

arXiv (Cornell University), Journal Year: 2021, Volume and Issue: unknown

Published: Jan. 1, 2021

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and adaptable to wide range downstream tasks. We call these foundation underscore their critically central yet incomplete character. This report provides thorough account opportunities risks models, ranging from capabilities language, vision, robotics, reasoning, human interaction) technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) applications law, healthcare, education) societal impact inequity, misuse, economic environmental impact, legal ethical considerations). Though based standard deep learning transfer learning, results in new emergent capabilities,and effectiveness across so many tasks incentivizes homogenization. Homogenization powerful leverage but demands caution, as defects inherited by all adapted downstream. Despite impending widespread deployment we currently lack clear understanding how they work, when fail, what even capable due properties. To tackle questions, believe much critical research will require interdisciplinary collaboration commensurate fundamentally sociotechnical nature.

Language: Английский

Citations

1553
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery DOI
Haoxin Mai, Tu C. Le, Dehong Chen

et al.

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(16), P. 13478 - 13515

Published: July 21, 2022

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, providing solutions environmental pollution. Improved processes for catalyst design better understanding electro/photocatalytic essential improving effectiveness. Recent advances in data science artificial intelligence have great potential accelerate electrocatalysis photocatalysis research, particularly rapid exploration large materials chemistry spaces through machine learning. Here comprehensive introduction to, critical review of, learning techniques used research provided. Sources electro/photocatalyst current approaches representing these by mathematical features described, most commonly methods summarized, quality utility models evaluated. Illustrations how applied novel discovery elucidate electrocatalytic or photocatalytic reaction mechanisms The offers guide scientists on selection research. application catalysis represents paradigm shift way advanced, next-generation catalysts will be designed synthesized.

Language: Английский

Citations

270
Generative models for molecular discovery: Recent advances and challenges DOI
Camille L. Bilodeau, Wengong Jin, Tommi Jaakkola

et al.

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2022, Volume and Issue: 12(5)

Published: March 5, 2022

Abstract Development of new products often relies on the discovery novel molecules. While conventional molecular design involves using human expertise to propose, synthesize, and test molecules, this process can be cost time intensive, limiting number molecules that reasonably tested. Generative modeling provides an alternative approach by reformulating as inverse problem. Here, we review recent advances in state‐of‐the‐art generative discusses considerations for integrating these models into real campaigns. We first model choices required develop train a including common 1D, 2D, 3D representations typical neural network architectures. then describe different problem statements applications explore benchmarks used evaluate based those statements. Finally, discuss important factors play role experimental workflows. Our aim is will equip reader with information context necessary utilize within their domain. This article categorized under: Data Science > Artificial Intelligence/Machine Learning

Language: Английский

Citations

202
SELFIES and the future of molecular string representations DOI Creative Commons
Mario Krenn, Qianxiang Ai, Senja Barthel

et al.

Patterns, Journal Year: 2022, Volume and Issue: 3(10), P. 100588 - 100588

Published: Oct. 1, 2022

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks chemistry materials science. Examples include the prediction of properties, discovery new reaction pathways, or design molecules. The needs read write fluently a chemical language each these tasks. Strings common tool represent molecular graphs, most popular string representation, Smiles, has powered cheminformatics since late 1980s. However, context AI ML chemistry, Smiles several shortcomings—most pertinently, combinations symbols lead invalid results with no valid interpretation. To overcome this issue, molecules was introduced 2020 that guarantees 100% robustness: SELF-referencing embedded (Selfies). Selfies simplified enabled numerous chemistry. In perspective, we look future discuss representations, along their respective opportunities challenges. We propose 16 concrete projects robust representations. These involve extension toward domains, exciting questions at interface languages, interpretability both humans machines. hope proposals will inspire follow-up works exploiting full potential representations

Language: Английский

Citations

156
A review on machine learning algorithms for the ionic liquid chemical space DOI Creative Commons
Spyridon Koutsoukos, Frederik Philippi, Francisco Malaret

et al.

Chemical Science, Journal Year: 2021, Volume and Issue: 12(20), P. 6820 - 6843

Published: Jan. 1, 2021

In this review article, the authors discuss use of machine learning algorithms as tools for prediction physical and chemical properties ionic liquids.

Language: Английский

Citations

131
A Guide to In Silico Drug Design DOI Creative Commons
Yiqun Chang, Bryson A. Hawkins, Jonathan J. Du

et al.

Pharmaceutics, Journal Year: 2022, Volume and Issue: 15(1), P. 49 - 49

Published: Dec. 23, 2022

The drug discovery process is a rocky path that full of challenges, with the result very few candidates progress from hit compound to commercially available product, often due factors, such as poor binding affinity, off-target effects, or physicochemical properties, solubility stability. This further complicated by high research and development costs time requirements. It thus important optimise every step in order maximise chances success. As recent advancements computer power technology, computer-aided design (CADD) has become an integral part modern guide accelerate process. In this review, we present overview CADD methods applications, silico structure prediction, refinement, modelling target validation, are commonly used area.

Language: Английский

Citations

121
Accelerated rational PROTAC design via deep learning and molecular simulations DOI
Shuangjia Zheng, Youhai Tan, Zhenyu Wang

et al.

Nature Machine Intelligence, Journal Year: 2022, Volume and Issue: 4(9), P. 739 - 748

Published: Sept. 15, 2022

Language: Английский

Citations

78
Multimodal learning with graphs DOI
Yasha Ektefaie, George Dasoulas, Ayush Noori

et al.

Nature Machine Intelligence, Journal Year: 2023, Volume and Issue: 5(4), P. 340 - 350

Published: April 3, 2023

Language: Английский

Citations

60
Distinct chemical environments in biomolecular condensates DOI
Henry R. Kilgore, Peter G. Mikhael, Kalon J. Overholt

et al.

Nature Chemical Biology, Journal Year: 2023, Volume and Issue: 20(3), P. 291 - 301

Published: Sept. 28, 2023

Language: Английский

Citations

48
Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches DOI
Ajay Vikram Singh,

Mansi Varma,

Mansi Rai

et al.

Advanced Intelligent Systems, Journal Year: 2024, Volume and Issue: 6(4)

Published: Feb. 14, 2024

The escalating use of novel chemicals and nanomaterials (NMs) across diverse sectors underscores the need for advanced risk assessment methods to safeguard human health environment. Traditional labor‐intensive approaches have given way computational methods. This review integrates recent developments in chemical nano‐quantitative structure‐activity relationship (QSAR) with machine learning modeling, offering a comprehensive predictive NMs chemicals. It explores nanodescriptors, their role predicting toxicity, amalgamation algorithms nano‐QSAR improved accuracy. article also investigates modeling techniques like molecular dynamics simulations, docking, mechanics/quantum mechanics physical properties. By consolidating these approaches, advocates more accurate efficient means assessing risks associated NMs/chemicals, promoting safe utilization minimizing adverse effects on A valuable resource researchers practitioners, informed decision‐making, advancing our understanding potential risks, is facilitated. Beyond studying systems at various scales, data from sources, enhancing accuracy fostering NMs/chemicals while impact

Language: Английский

Citations

42