Large property models: a new generative machine-learning formulation for molecules DOI Creative Commons

Tianfan Jin,

Veerupaksh Singla,

Hsuan‐Hao Hsu

et al.

Faraday Discussions, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

Generative models for the inverse design of molecules with particular properties have been heavily hyped, but yet to demonstrate significant gains over machine-learning-augmented expert intuition. A major challenge such is their limited accuracy in predicting targeted data-scarce regime, which regime typical prized outliers that it hoped will discover. For example, activity data a drug target or stability material may only number tens hundreds samples, insufficient learn an accurate and reasonably general property-to-structure mapping from scratch. We've hypothesized becomes unique when sufficient are supplied during training. This hypothesis has several important corollaries if true. It would imply can be completely determined using set more accessible molecular properties. also generative model trained on multiple exhibit phase transition after achieving size-a process analogous what observed context large language models. To interrogate these behaviors, we built first transformers property-to-molecular-graph task, dub "large property models" (LPMs). key ingredient supplementing training relatively basic abundant chemical data. The motivation large-property-model paradigm, architectures, case studies presented here.

Language: Английский

Self-Driving Laboratories for Chemistry and Materials Science DOI Creative Commons
Gary Tom, Stefan P. Schmid, Sterling G. Baird

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Language: Английский

Citations

40
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

28
Computational and Machine Learning Methods for CO2 Capture Using Metal–Organic Frameworks DOI
Hossein Mashhadimoslem, Mohammad Ali Abdol, Peyman Karimi

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: 18(35), P. 23842 - 23875

Published: Aug. 22, 2024

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress provided benefits in the chemistry material science. This work examines interactions between materials computational science at scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO

Language: Английский

Citations

16
The Future of Material Scientists in an Age of Artificial Intelligence DOI Creative Commons
Ayman Maqsood,

Chen Chen,

T. Jesper Jacobsson

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 11(19)

Published: March 13, 2024

Abstract Material science has historically evolved in tandem with advancements technologies for characterization, synthesis, and computation. Another type of technology to add this mix is machine learning (ML) artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety fields. From material perspective, it indisputable offer potent toolkit the potential substantially accelerate research efforts areas such as development discovery new functional materials. Less clear how best harness development, what skill sets will be required, may affect established practices. In paper, those question explored respect more ML/AI‐approaches. To structure discussion, conceptual framework an AI‐ladder introduced. This ranges from basic data‐fitting techniques advanced functionalities semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, orchestration specialized AI modules stepping‐stones toward general intelligence. ladder metaphor provides hierarchical contemplating opportunities, challenges, evolving required stay competitive age

Language: Английский

Citations

14
Automation and machine learning augmented by large language models in a catalysis study DOI Creative Commons
Yuming Su, Xue Wang,

Yuanxiang Ye

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(31), P. 12200 - 12233

Published: Jan. 1, 2024

AI and automation are revolutionizing catalyst discovery, shifting from manual methods to high-throughput digital approaches, enhanced by large language models.

Language: Английский

Citations

14
ORGANA: A robotic assistant for automated chemistry experimentation and characterization DOI
Kourosh Darvish, Marta Skreta, Yuchi Zhao

et al.

Matter, Journal Year: 2024, Volume and Issue: unknown, P. 101897 - 101897

Published: Nov. 1, 2024

Language: Английский

Citations

14
Density Functional Theory-Guided Synthesis of Cu-N-TiO2 for Overall Water Splitting by Breaking the Scaling Relationship DOI
Wenhao Jing, Guiwei He, Shengjie Bai

et al.

ACS Materials Letters, Journal Year: 2024, Volume and Issue: 6(4), P. 1347 - 1355

Published: March 8, 2024

For solar-driven overall pure water splitting, a superior photocatalyst with reasonable atomic and electronic structure is needed to be suitable for both half-reactions, HER OER. TiO2 has showcased remarkable catalytic efficiency in the field of but it still encounters obstacles accomplishing proficient splitting. Within this work, following sequential screening based on element type, stability, structure, adsorption energy, we designed TiO2-based catalyst workflow This DFT-based significantly reduced time trial-and-error costs associated traditional experimental design. It precisely guided synthesis highly dispersed Cu-loaded/N-doped TiO2, which facilitated sacrificial-agent-free resulting solar fuel 0.2% an H2 yield 1027.7 μmol/h/g. Advanced DFT calculations revealed that d–p orbital coupling between Cu N broke scaling relationship O-based intermediates. work holds promise extension other reactions, offering valuable insights into design endeavors.

Language: Английский

Citations

9
A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand DOI
Tao Song, Man Luo, Xiaolong Zhang

et al.

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown

Published: March 8, 2025

The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream chemical research on demand. Here, we report a robotic AI chemist powered by hierarchical multiagent system, ChemAgents, based on-board Llama-3.1-70B LLM, capable executing complex, multistep experiments with minimal human intervention. It operates through Task Manager agent that interacts researchers coordinates four role-specific agents─Literature Reader, Experiment Designer, Computation Performer, Robot Operator─each leveraging one foundational resources: comprehensive Literature Database, extensive Protocol Library, versatile Model state-of-the-art Automated Lab. We demonstrate its versatility efficacy six experimental tasks varying complexity, ranging from straightforward synthesis characterization more complex exploration screening parameters, culminating discovery optimization functional materials. Additionally, introduce seventh task, where ChemAgents is deployed new chemistry lab environment autonomously perform photocatalytic organic reactions, highlighting ChemAgents's scalability adaptability. Our multiagent-driven showcases potential on-demand accelerate democratize access advanced across academic disciplines industries.

Language: Английский

Citations

1
Real-world robot applications of foundation models: a review DOI Creative Commons
Kento Kawaharazuka, Tatsuya Matsushima,

Andrew Gambardella

et al.

Advanced Robotics, Journal Year: 2024, Volume and Issue: 38(18), P. 1232 - 1254

Published: Sept. 16, 2024

Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language (VLMs), trained on extensive data, facilitate flexible application across different tasks modalities. Their impact spans various fields, including healthcare, education, robotics. This paper provides an overview of the practical models real-world robotics, with a primary emphasis replacement specific components within existing robot systems. The summary encompasses perspective input-output relationships as well their role perception, motion planning, control field concludes discussion future challenges implications for applications.

Language: Английский

Citations

7
Transforming science labs into automated factories of discovery DOI
Angelos Angelopoulos, James F. Cahoon, Ron Alterovitz

et al.

Science Robotics, Journal Year: 2024, Volume and Issue: 9(95)

Published: Oct. 23, 2024

Laboratories in chemistry, biochemistry, and materials science are at the leading edge of technology, discovering molecules to unlock capabilities energy, catalysis, biotechnology, sustainability, electronics, more. Yet, most modern laboratories resemble factories from generations past, with a large reliance on humans manually performing synthesis characterization tasks. Robotics automation can enable scientific experiments be conducted faster, more safely, accurately, greater reproducibility, allowing scientists tackle societal problems domains such as health energy shorter timescale. We define five levels laboratory automation, assistance full automation. also introduce robotics research challenges that arise when increasing generality tasks within laboratory. Robots poised transform labs into automated discovery accelerate progress.

Language: Английский

Citations

5