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- W4200022889 abstract "Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences. Synopsis Recent surge in the application of machine learning (ML) methods in fundamental sciences has led to a perspective that these methods may become important tools in chemical science. This perspective provides an overview of the modern ML methods and their successful applications in chemistry during the last few years." @default.
- W4200022889 created "2021-12-31" @default.
- W4200022889 creator A5034329125 @default.
- W4200022889 creator A5072957737 @default.
- W4200022889 date "2021-12-21" @default.
- W4200022889 modified "2023-10-17" @default.
- W4200022889 title "Artificial intelligence: machine learning for chemical sciences" @default.
- W4200022889 cites W1531674615 @default.
- W4200022889 cites W1575307313 @default.
- W4200022889 cites W1901616594 @default.
- W4200022889 cites W1971044734 @default.
- W4200022889 cites W1971698657 @default.
- W4200022889 cites W1978183953 @default.
- W4200022889 cites W1982598895 @default.
- W4200022889 cites W1983759267 @default.
- W4200022889 cites W1988037271 @default.
- W4200022889 cites W1991238353 @default.
- W4200022889 cites W1994685255 @default.
- W4200022889 cites W1996327711 @default.
- W4200022889 cites W1998260904 @default.
- W4200022889 cites W2004303971 @default.
- W4200022889 cites W2007400012 @default.
- W4200022889 cites W2008505552 @default.
- W4200022889 cites W2008758209 @default.
- W4200022889 cites W2017398555 @default.
- W4200022889 cites W2019403558 @default.
- W4200022889 cites W2019966295 @default.
- W4200022889 cites W2020786104 @default.
- W4200022889 cites W2025444507 @default.
- W4200022889 cites W2029413789 @default.
- W4200022889 cites W2033169180 @default.
- W4200022889 cites W2033733365 @default.
- W4200022889 cites W2035585923 @default.
- W4200022889 cites W2035828432 @default.
- W4200022889 cites W2044834685 @default.
- W4200022889 cites W2046564413 @default.
- W4200022889 cites W2061843861 @default.
- W4200022889 cites W2062848325 @default.
- W4200022889 cites W2063783366 @default.
- W4200022889 cites W2068113002 @default.
- W4200022889 cites W2079199763 @default.
- W4200022889 cites W2080635178 @default.
- W4200022889 cites W2083415705 @default.
- W4200022889 cites W2085312881 @default.
- W4200022889 cites W2086286404 @default.
- W4200022889 cites W2086849375 @default.
- W4200022889 cites W2088014408 @default.
- W4200022889 cites W2102882555 @default.
- W4200022889 cites W2104489082 @default.
- W4200022889 cites W2114704115 @default.
- W4200022889 cites W2117130368 @default.
- W4200022889 cites W2122825543 @default.
- W4200022889 cites W2127553917 @default.
- W4200022889 cites W2145339207 @default.
- W4200022889 cites W2148512505 @default.
- W4200022889 cites W2157336498 @default.
- W4200022889 cites W2179425537 @default.
- W4200022889 cites W2269544137 @default.
- W4200022889 cites W2297621926 @default.
- W4200022889 cites W2313966941 @default.
- W4200022889 cites W2337050083 @default.
- W4200022889 cites W2419175238 @default.
- W4200022889 cites W2436108096 @default.
- W4200022889 cites W2478294658 @default.
- W4200022889 cites W2512980820 @default.
- W4200022889 cites W2516321997 @default.
- W4200022889 cites W2558748708 @default.
- W4200022889 cites W2591922568 @default.
- W4200022889 cites W2594183968 @default.
- W4200022889 cites W2610148085 @default.
- W4200022889 cites W2736137960 @default.
- W4200022889 cites W2742835787 @default.
- W4200022889 cites W2747592475 @default.
- W4200022889 cites W2753962198 @default.
- W4200022889 cites W2769775068 @default.
- W4200022889 cites W2775714759 @default.
- W4200022889 cites W2782516741 @default.
- W4200022889 cites W2782714865 @default.
- W4200022889 cites W2790608062 @default.
- W4200022889 cites W2803389502 @default.
- W4200022889 cites W2805002767 @default.
- W4200022889 cites W2806393871 @default.
- W4200022889 cites W2806547269 @default.
- W4200022889 cites W2883583109 @default.
- W4200022889 cites W2884430236 @default.
- W4200022889 cites W2884871339 @default.
- W4200022889 cites W2886490392 @default.
- W4200022889 cites W2886700507 @default.
- W4200022889 cites W2887598572 @default.
- W4200022889 cites W2903564615 @default.
- W4200022889 cites W2909219564 @default.
- W4200022889 cites W2909240409 @default.
- W4200022889 cites W2910175903 @default.
- W4200022889 cites W2944959599 @default.
- W4200022889 cites W2945551948 @default.
- W4200022889 cites W2952587893 @default.
- W4200022889 cites W2954088480 @default.
- W4200022889 cites W2962876364 @default.
- W4200022889 cites W2964233714 @default.
- W4200022889 cites W2966357564 @default.