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DOIs for this ISSN

Showing the top 5 of 1152 DOIs, ordered by correction priority.

#	Title	Missing	Priority	Citations
1	A forward view for <i>Digital Discovery<… (10.1039/d2dd90001g)	ORCID	21.13	6
2	The decade of artificial intelligence in chemistry and mater… (10.1039/d3dd90001k)	ORCID	21.13	6
3	Correction: Tackling data scarcity with transfer learning: a… (10.1039/d4dd90015d)	References ORCID	15.05	1
4	Commit: Mini article for dynamic reporting of incremental im… (10.1039/d4dd90053g)	References	11.93	2
5	Convergence acceleration in machine learning potentials for… (10.1039/d1dd00005e)		0.00	32
6	Reaction classification and yield prediction using the diffe… (10.1039/d1dd00006c)		0.00	141
7	Rapid prediction of protein natural frequencies using graph… (10.1039/d1dd00007a)		0.00	26
8	Accelerated automated screening of viscous graphene suspensi… (10.1039/d1dd00008j)		0.00	9
9	Natural language processing models that automate programming… (10.1039/d1dd00009h)		0.00	50
10	Sparse modeling for small data: case studies in controlled s… (10.1039/d1dd00010a)		0.00	25
11	MPSM-DTI: prediction of drug–target interaction… (10.1039/d1dd00011j)		0.00	18
12	Performance of chemical structure string representations for… (10.1039/d1dd00013f)		0.00	17
13	Predicting 3D shapes, masks, and properties of materials ins… (10.1039/d1dd00014d)		0.00	15
14	<i>ChemSpaX</i> : exploration of chemica… (10.1039/d1dd00017a)		0.00	13
15	Consideration of predicted small-molecule metabolites in com… (10.1039/d1dd00018g)		0.00	14
16	Deep generative models for peptide design (10.1039/d1dd00024a)		0.00	114
17	Machine learning platform for determining experimental lipid… (10.1039/d1dd00025j)		0.00	12
18	Bayesian progress curve analysis of MicroScale thermophoresi… (10.1039/d1dd00026h)		0.00	4
19	Machine learning enhanced spectroscopic analysis: towards au… (10.1039/d1dd00027f)		0.00	36
20	DiSCoVeR: a materials discovery screening tool for high perf… (10.1039/d1dd00028d)		0.00	19
21	Towards automation of <i>operando</i>… (10.1039/d1dd00029b)		0.00	14
22	The resolution- <i>vs.</i>… (10.1039/d1dd00031d)		0.00	15
23	RegioML: predicting the regioselectivity of electrophilic ar… (10.1039/d1dd00032b)		0.00	20
24	ULSA: unified language of synthesis actions for the represen… (10.1039/d1dd00034a)		0.00	26
25	Plot2Spectra: an automatic spectra extraction tool (10.1039/d1dd00036e)		0.00	8
26	Graph neural networks for the prediction of infinite dilutio… (10.1039/d1dd00037c)		0.00	70
27	Reorganization energies of flexible organic molecules as a c… (10.1039/d1dd00038a)		0.00	27
28	Explainable graph neural networks for organic cages (10.1039/d1dd00039j)		0.00	13
29	Self-learning entropic population annealing for interpretabl… (10.1039/d1dd00043h)		0.00	7
30	Predicting compositional changes of organic–inorganic hybrid… (10.1039/d1dd00044f)		0.00	4
31	Data management matters (10.1039/d1dd00046b)		0.00	12
32	Hybrid computational–experimental data-driven design of self… (10.1039/d1dd00047k)		0.00	11
33	SPA <sup>H</sup> M:… (10.1039/d1dd00050k)		0.00	16
34	A transfer learning protocol for chemical catalysis using a… (10.1039/d1dd00052g)		0.00	35
35	Nuisance small molecules under a machine-learning lens (10.1039/d2dd00001f)		0.00	3
36	Parallel tempered genetic algorithm guided by deep neural ne… (10.1039/d2dd00003b)		0.00	70
37	Limitations of machine learning models when predicting compo… (10.1039/d2dd00004k)		0.00	34
38	Artificial neural networks and data fusion enable concentrat… (10.1039/d2dd00006g)		0.00	9
39	NewtonNet: a Newtonian message passing network for deep lear… (10.1039/d2dd00008c)		0.00	122
40	Machine learning enabling high-throughput and remote operati… (10.1039/d2dd00014h)		0.00	15
41	PaRoutes: towards a framework for benchmarking retrosynthesi… (10.1039/d2dd00015f)		0.00	47
42	Long-range dispersion-inclusive machine learning potentials… (10.1039/d2dd00016d)		0.00	30
43	Multivariate analysis of peptide-driven nucleation and growt… (10.1039/d2dd00017b)		0.00	9
44	High-throughput computational screening of nanoporous materi… (10.1039/d2dd00018k)		0.00	56
45	Extraction of chemical structures from literature and patent… (10.1039/d2dd00019a)		0.00	30
46	3D chemical structures allow robust deep learning models for… (10.1039/d2dd00021k)		0.00	10
47	A unified ML framework for solubility prediction across orga… (10.1039/d2dd00024e)		0.00	39
48	Autonomous retrosynthesis of gold nanoparticles… (10.1039/d2dd00025c)		0.00	32
49	Machine-learning improves understanding of glass formation i… (10.1039/d2dd00026a)		0.00	10
50	Learning the laws of lithium-ion transport in electrolytes u… (10.1039/d2dd00027j)		0.00	22