000 | 02876nam a2200409 i 4500 | ||
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001 | 9780841299467 | ||
003 | DACS | ||
005 | 20230516163028.0 | ||
008 | 100319s2022 dcua ob 101 0 eng d | ||
020 |
_a9780841299467 _qelectronic |
||
024 | 7 |
_a10.1021/acsinfocus.7e5033 _2doi |
|
035 | _a(CaBNVSL)slc00002820 | ||
040 |
_aNjRocCCS _beng _erda _cNjRocCCS |
||
050 | 4 |
_aTA404.23 _b.B886 2022eb |
|
082 | 0 | 4 |
_a620.11 _223 |
100 | 1 |
_aButler, Keith T., _eauthor. _uRutherford Appleton Laboratory. _967855 |
|
245 | 1 | 0 |
_aMachine learning in materials science / _cKeith T. Butler, Felipe Oviedo & Pieremanuele Canepa. |
264 | 1 |
_aWashington, DC, USA : _bAmerican Chemical Society, _c2022. |
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300 |
_a1 online resource : _billustrations (some color). |
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336 |
_atext _2rdacontent |
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337 |
_acomputer _2rdamedia |
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338 |
_aonline resource _2rdacarrier |
||
490 | 1 |
_aACS in focus, _x2691-8307 |
|
504 | _aIncludes bibliographical references and index. | ||
505 | 0 | 0 |
_tApplying Machine Learning to Materials Science -- _tBuilding Trust in Machine Learning -- _tMachine Learning for Materials Simulations -- _tAnalyzing Experimental Data -- _tClosed-Loop Optimization and Active Learning for Materials -- _tDiscovering New Materials -- _tCoda. |
520 |
_a" Machine Learning for Materials Science provides the fundamentals and useful insight into where Machine Learning (ML) will have the greatest impact for the materials science researcher. This digital primer provides example methods for ML applied to experiments and simulations, including the early stages of building an ML solution for a materials science problem, concentrating on where and how to get data and some of the considerations when choosing an approach. The authors demonstrate how to build more robust models, how to make sure that your colleagues trust the results, and how to use ML to accelerate or augment simulations, by introducing methods in which ML can be applied to analyze and process experimental data. They also cover how to build integrated closed-loop experiments where ML is used to plan the course of a materials optimization experiment and how ML can be utilized in the discovery of materials on computers."-- _cProvided by publisher. |
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590 | _aAmerican Chemical Society, ACS In Focus eBooks - 2022 Front Files. | ||
650 | 0 |
_aMaterials _xData processing. _919619 |
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650 | 0 |
_aMaterials science _xMathematical models. _914849 |
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650 | 0 |
_aMachine learning _xIndustrial applications. _912876 |
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700 | 1 |
_aOviedo, Felipe, _eauthor. _uMicrosoft AI For Good and Massachusetts Institute of Technology. _967856 |
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700 | 1 |
_aCanepa, Pieremanuele, _eauthor. _uNational University of Singapore. _967857 |
|
710 | 2 |
_aAmerican Chemical Society. _967532 |
|
830 | 0 |
_aACS in focus, _x2691-8307. _967858 |
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856 | 4 | _uhttp://dx.doi.org/10.1021/acsinfocus.7e5033 | |
942 | _cEBK | ||
999 |
_c82153 _d82153 |