000			02876nam a2200409 i 4500
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.
300			_a1 online resource : _billustrations (some color).
336			_atext _2rdacontent
337			_acomputer _2rdamedia
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.
590			_aAmerican Chemical Society, ACS In Focus eBooks - 2022 Front Files.
650		0	_aMaterials _xData processing. _919619
650		0	_aMaterials science _xMathematical models. _914849
650		0	_aMachine learning _xIndustrial applications. _912876
700	1		_aOviedo, Felipe, _eauthor. _uMicrosoft AI For Good and Massachusetts Institute of Technology. _967856
700	1		_aCanepa, Pieremanuele, _eauthor. _uNational University of Singapore. _967857
710	2		_aAmerican Chemical Society. _967532
830		0	_aACS in focus, _x2691-8307. _967858
856	4		_uhttp://dx.doi.org/10.1021/acsinfocus.7e5033
942			_cEBK
999			_c82153 _d82153