The Potential of Neural Network Potentials DOI Creative Commons
Timothy T. Duignan

ACS Physical Chemistry Au, Journal Year: 2024, Volume and Issue: 4(3), P. 232 - 241

Published: March 21, 2024

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by combination of recent advances in quantum and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are breakthrough new tool that already enabling us to simulate systems at molecular scale with unprecedented accuracy speed, relying on nothing but fundamental laws. The continued development this approach realize Paul Dirac's 80-year-old vision using mechanics unify physics providing invaluable tools for understanding materials science, biology, earth sciences, beyond. era highly accurate efficient first-principles simulations provide wealth training data can be used build automated computational methodologies, such as diffusion models, design optimization scale. Large language models (LLMs) also evolve into increasingly indispensable literature review, coding, idea generation, scientific writing.

Language: Английский

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design DOI Creative Commons
Lalitkumar K. Vora, Amol D. Gholap, Keshava Jetha

et al.

Pharmaceutics, Journal Year: 2023, Volume and Issue: 15(7), P. 1916 - 1916

Published: July 10, 2023

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology machine learning present transformative opportunity the drug discovery, formulation, testing of pharmaceutical dosage forms. By utilizing algorithms analyze extensive biological data, including genomics proteomics, researchers can identify disease-associated targets predict their interactions with potential candidates. This enables more efficient targeted approach thereby increasing likelihood successful approvals. Furthermore, contribute reducing development costs by optimizing research processes. Machine assist experimental design pharmacokinetics toxicity capability prioritization optimization lead compounds, need for costly animal testing. Personalized medicine approaches be facilitated through real-world patient leading effective treatment outcomes improved adherence. comprehensive review explores wide-ranging applications delivery form designs, process optimization, testing, pharmacokinetics/pharmacodynamics (PK/PD) studies. an overview various AI-based utilized technology, highlighting benefits drawbacks. Nevertheless, continued investment exploration industry offer exciting prospects enhancing processes care.

Language: Английский

Citations

406
Representations of Materials for Machine Learning DOI Creative Commons

James Damewood,

Jessica Karaguesian,

Jaclyn R. Lunger

et al.

Annual Review of Materials Research, Journal Year: 2023, Volume and Issue: 53(1), P. 399 - 426

Published: April 18, 2023

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by the relations between composition, structure, properties exploiting such for design. However, build these connections, must be translated into numerical form, called representation, that can processed an ML model. Data sets in vary format (ranging from images spectra), size, fidelity. Predictive models scope interest. Here, we review context-dependent strategies constructing representations enable use as inputs or outputs models. Furthermore, discuss how modern techniques learn transfer chemical physical information tasks. Finally, outline high-impact questions not been fully resolved thus require further investigation.

Language: Английский

Citations

51
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks DOI Creative Commons
Sergio Pablo‐García, Santiago Morandi, Rodrigo A. Vargas–Hernández

et al.

Nature Computational Science, Journal Year: 2023, Volume and Issue: 3(5), P. 433 - 442

Published: May 1, 2023

Modeling in heterogeneous catalysis requires the extensive evaluation of energy molecules adsorbed on surfaces. This is done via density functional theory but for large organic it enormous computational time, compromising viability approach. Here we present GAME-Net, a graph neural network to quickly evaluate adsorption energy. GAME-Net trained well-balanced chemically diverse dataset with C

Language: Английский

Citations

47
AI in analytical chemistry: Advancements, challenges, and future directions DOI
Rafael Cardoso Rial

Talanta, Journal Year: 2024, Volume and Issue: 274, P. 125949 - 125949

Published: March 19, 2024

Language: Английский

Citations

29
Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry DOI Creative Commons
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan

et al.

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(1), P. 100049 - 100049

Published: Jan. 19, 2024

Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI's pivotal roles field organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies planning, accelerates catalyst discovery, and fuels material innovation so on. It seamlessly integrates data-driven algorithms with intuition to redefine As chemistry advances, it promises accelerated research, sustainability, innovative solutions chemistry's pressing challenges. The fusion poised shape field's future profoundly, offering new horizons precision efficiency. encapsulates transformation marking moment where data converge revolutionize world molecules.

Language: Английский

Citations

25
Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations DOI Creative Commons
Guanjie Wang, Changrui Wang,

Xuanguang Zhang

et al.

iScience, Journal Year: 2024, Volume and Issue: 27(5), P. 109673 - 109673

Published: April 4, 2024

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and relatively low accuracy classical large-scale molecular dynamics, facilitating more efficient precise simulations materials research design. In this review, current state four essential stages MLIP is discussed, including data generation methods, material structure descriptors, six unique machine algorithms, available software. Furthermore, applications various fields are investigated, notably phase-change memory materials, searching, properties predicting, pre-trained universal models. Eventually, future perspectives, consisting standard datasets, transferability, generalization, trade-off between complexity MLIPs, reported.

Language: Английский

Citations

25
Advancing material property prediction: using physics-informed machine learning models for viscosity DOI Creative Commons
Alex K. Chew,

Matthew Sender,

Zachary Kaplan

et al.

Journal of Cheminformatics, Journal Year: 2024, Volume and Issue: 16(1)

Published: March 14, 2024

Abstract In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially the material science domain where data limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance accuracy interpretability of models. Our current study focuses on predicting viscosity liquid systems using MD descriptors. this work, curated a comprehensive dataset over 4000 small organic molecules’ viscosities scientific literature, publications, online databases. This enabled us develop quantitative structure–property relationships (QSPR) consisting descriptor-based graph neural network predict temperature-dependent for wide range viscosities. The QSPR reveal that including improves prediction experimental viscosities, particularly at set scale fewer than thousand points. Furthermore, feature importance tools intermolecular interactions captured by are most important predictions. Finally, can capture inverse relationship between temperature six battery-relevant solvents, some which were not included original set. research highlights effectiveness incorporating into leads improved difficult when alone or limited available. Graphical

Language: Английский

Citations

23
Graph Neural Network-Based EEG Classification: A Survey DOI Creative Commons
Dominik Klepl, Min Wu, Fei He

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 32, P. 493 - 503

Published: Jan. 1, 2024

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases disorders. A wide range of methods have been proposed design GNN-based classifiers. Therefore, there is a need systematic review categorisation these approaches. We exhaustively search the published literature on this topic derive several categories comparison. These highlight similarities differences among methods. The results suggest prevalence spectral graph convolutional layers over spatial. Additionally, we identify standard forms node features, with most popular being raw signal differential entropy. Our summarise emerging trends in approaches classification. Finally, discuss promising research directions, exploring potential transfer learning appropriate modelling cross-frequency interactions.

Language: Английский

Citations

19
From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts DOI

Xinyu Fan,

Letian Chen,

Dulin Huang

et al.

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: 34(34)

Published: April 25, 2024

Abstract The rapid advancement of high‐performance computing and artificial intelligence technology has opened up novel avenues for the development various metal electrocatalysts. In particular, dilute high‐entropy alloys have garnered significant attention owing to their unique electronic spatial structures, as well exceptional electrocatalytic performance. Commencing with exploration single‐atom alloy catalysts, latest advancements in machine learning (ML) techniques are presented efficient screening a broad spectrum spaces. Subsequently, review delves into prevailing trend research, focusing specifically on rare‐metal electrocatalysts, offers an overview progress outcomes achieved through application ML these domains. Finally, highlighted promising category electrocatalysts underscore importance potential applications addressing complex challenging research issues underscored.

Language: Английский

Citations

18
GNNs for mechanical properties prediction of strut-based lattice structures DOI

Bingyue Jiang,

Yangwei Wang,

Haiyan Niu

et al.

International Journal of Mechanical Sciences, Journal Year: 2024, Volume and Issue: 269, P. 109082 - 109082

Published: Feb. 2, 2024

Language: Английский

Citations

17